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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::depth_to_space::Prepare | tflite::ops::builtin::depth_to_space::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteDepthToSpaceParams*>(node->builtin_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
auto data_type = output->type;
TF_LITE_ENSURE(context,
data_type == kTfLiteFloat32 || data_type == kTfLiteUInt8 ||
data_type == kTfLiteInt8 || data_type == kTfLiteInt32 ||
data_type == kTfLiteInt64);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
const int block_size = params->block_size;
const int input_height = input->dims->data[1];
const int input_width = input->dims->data[2];
const int input_channels = input->dims->data[3];
int output_height = input_height * block_size;
int output_width = input_width * block_size;
int output_channels = input_channels / block_size / block_size;
TF_LITE_ENSURE_EQ(context, input_height, output_height / block_size);
TF_LITE_ENSURE_EQ(context, input_width, output_width / block_size);
TF_LITE_ENSURE_EQ(context, input_channels,
output_channels * block_size * block_size);
TfLiteIntArray* output_size = TfLiteIntArrayCreate(4);
output_size->data[0] = input->dims->data[0];
output_size->data[1] = output_height;
output_size->data[2] = output_width;
output_size->data[3] = output_channels;
return context->ResizeTensor(context, output, output_size);
} | 304 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::depth_to_space::Prepare | tflite::ops::builtin::depth_to_space::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteDepthToSpaceParams*>(node->builtin_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
auto data_type = output->type;
TF_LITE_ENSURE(context,
data_type == kTfLiteFloat32 || data_type == kTfLiteUInt8 ||
data_type == kTfLiteInt8 || data_type == kTfLiteInt32 ||
data_type == kTfLiteInt64);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
const int block_size = params->block_size;
const int input_height = input->dims->data[1];
const int input_width = input->dims->data[2];
const int input_channels = input->dims->data[3];
int output_height = input_height * block_size;
int output_width = input_width * block_size;
int output_channels = input_channels / block_size / block_size;
TF_LITE_ENSURE_EQ(context, input_height, output_height / block_size);
TF_LITE_ENSURE_EQ(context, input_width, output_width / block_size);
TF_LITE_ENSURE_EQ(context, input_channels,
output_channels * block_size * block_size);
TfLiteIntArray* output_size = TfLiteIntArrayCreate(4);
output_size->data[0] = input->dims->data[0];
output_size->data[1] = output_height;
output_size->data[2] = output_width;
output_size->data[3] = output_channels;
return context->ResizeTensor(context, output, output_size);
} | 304 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::depthwise_conv::Eval | tflite::ops::builtin::depthwise_conv::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
switch (input->type) { // Already know in/out types are same.
case kTfLiteFloat32:
return EvalImpl<kernel_type, kTfLiteFloat32>(context, node);
case kTfLiteUInt8:
return EvalImpl<kernel_type, kTfLiteUInt8>(context, node);
case kTfLiteInt8:
return EvalImpl<kernel_type, kTfLiteInt8>(context, node);
case kTfLiteInt16:
return EvalImpl<kernel_type, kTfLiteInt16>(context, node);
default:
context->ReportError(context, "Type %d not currently supported.",
input->type);
return kTfLiteError;
}
} | 116 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::depthwise_conv::Eval | tflite::ops::builtin::depthwise_conv::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
switch (input->type) { // Already know in/out types are same.
case kTfLiteFloat32:
return EvalImpl<kernel_type, kTfLiteFloat32>(context, node);
case kTfLiteUInt8:
return EvalImpl<kernel_type, kTfLiteUInt8>(context, node);
case kTfLiteInt8:
return EvalImpl<kernel_type, kTfLiteInt8>(context, node);
case kTfLiteInt16:
return EvalImpl<kernel_type, kTfLiteInt16>(context, node);
default:
context->ReportError(context, "Type %d not currently supported.",
input->type);
return kTfLiteError;
}
} | 116 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::depthwise_conv::EvalHybridPerChannel | tflite::ops::builtin::depthwise_conv::EvalHybridPerChannel( TfLiteContext * context , TfLiteNode * node , TfLiteDepthwiseConvParams * params , OpData * data , const TfLiteTensor * input , const TfLiteTensor * filter , const TfLiteTensor * bias , TfLiteTensor * output) | ['context', 'node', 'params', 'data', 'input', 'filter', 'bias', 'output'] | TfLiteStatus EvalHybridPerChannel(TfLiteContext* context, TfLiteNode* node,
TfLiteDepthwiseConvParams* params,
OpData* data, const TfLiteTensor* input,
const TfLiteTensor* filter,
const TfLiteTensor* bias,
TfLiteTensor* output) {
float output_activation_min, output_activation_max;
CalculateActivationRange(params->activation, &output_activation_min,
&output_activation_max);
const int input_size = NumElements(input) / SizeOfDimension(input, 0);
const int batch_size = SizeOfDimension(input, 0);
const TfLiteTensor* input_quantized =
GetTemporary(context, node, data->input_quantized_index);
int8_t* quantized_input_ptr_batch = input_quantized->data.int8;
float* scaling_factors_ptr = GetTensorData<float>(
GetTemporary(context, node, data->scaling_factors_index));
int32_t* input_offset_ptr = GetTensorData<int32_t>(
GetTemporary(context, node, data->input_offset_index));
for (int b = 0; b < batch_size; ++b) {
const int offset = b * input_size;
tensor_utils::AsymmetricQuantizeFloats(
GetTensorData<float>(input) + offset, input_size,
quantized_input_ptr_batch + offset, &scaling_factors_ptr[b],
&input_offset_ptr[b]);
}
DepthwiseParams op_params;
op_params.padding_type = PaddingType::kSame;
op_params.padding_values.width = data->padding.width;
op_params.padding_values.height = data->padding.height;
op_params.stride_width = params->stride_width;
op_params.stride_height = params->stride_height;
op_params.dilation_width_factor = params->dilation_width_factor;
op_params.dilation_height_factor = params->dilation_height_factor;
op_params.depth_multiplier = params->depth_multiplier;
op_params.weights_offset = 0;
op_params.float_activation_min = output_activation_min;
op_params.float_activation_max = output_activation_max;
const auto* affine_quantization =
reinterpret_cast<TfLiteAffineQuantization*>(filter->quantization.params);
if (kernel_type == kReference) {
reference_integer_ops::DepthwiseConvHybridPerChannel(
op_params, scaling_factors_ptr, GetTensorShape(input),
quantized_input_ptr_batch, GetTensorShape(filter),
GetTensorData<int8>(filter), GetTensorShape(bias),
GetTensorData<float>(bias), GetTensorShape(output),
GetTensorData<float>(output), affine_quantization->scale->data,
input_offset_ptr);
} else {
optimized_integer_ops::DepthwiseConvHybridPerChannel(
op_params, scaling_factors_ptr, GetTensorShape(input),
quantized_input_ptr_batch, GetTensorShape(filter),
GetTensorData<int8>(filter), GetTensorShape(bias),
GetTensorData<float>(bias), GetTensorShape(output),
GetTensorData<float>(output), affine_quantization->scale->data,
input_offset_ptr, CpuBackendContext::GetFromContext(context));
}
return kTfLiteOk;
} | 467 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::depthwise_conv::EvalHybridPerChannel | tflite::ops::builtin::depthwise_conv::EvalHybridPerChannel( TfLiteContext * context , TfLiteNode * node , TfLiteDepthwiseConvParams * params , OpData * data , const TfLiteTensor * input , const TfLiteTensor * filter , const TfLiteTensor * bias , TfLiteTensor * output) | ['context', 'node', 'params', 'data', 'input', 'filter', 'bias', 'output'] | TfLiteStatus EvalHybridPerChannel(TfLiteContext* context, TfLiteNode* node,
TfLiteDepthwiseConvParams* params,
OpData* data, const TfLiteTensor* input,
const TfLiteTensor* filter,
const TfLiteTensor* bias,
TfLiteTensor* output) {
float output_activation_min, output_activation_max;
CalculateActivationRange(params->activation, &output_activation_min,
&output_activation_max);
const int input_size = NumElements(input) / SizeOfDimension(input, 0);
const int batch_size = SizeOfDimension(input, 0);
const TfLiteTensor* input_quantized =
GetTemporary(context, node, data->input_quantized_index);
int8_t* quantized_input_ptr_batch = input_quantized->data.int8;
float* scaling_factors_ptr = GetTensorData<float>(
GetTemporary(context, node, data->scaling_factors_index));
int32_t* input_offset_ptr = GetTensorData<int32_t>(
GetTemporary(context, node, data->input_offset_index));
for (int b = 0; b < batch_size; ++b) {
const int offset = b * input_size;
tensor_utils::AsymmetricQuantizeFloats(
GetTensorData<float>(input) + offset, input_size,
quantized_input_ptr_batch + offset, &scaling_factors_ptr[b],
&input_offset_ptr[b]);
}
DepthwiseParams op_params;
op_params.padding_type = PaddingType::kSame;
op_params.padding_values.width = data->padding.width;
op_params.padding_values.height = data->padding.height;
op_params.stride_width = params->stride_width;
op_params.stride_height = params->stride_height;
op_params.dilation_width_factor = params->dilation_width_factor;
op_params.dilation_height_factor = params->dilation_height_factor;
op_params.depth_multiplier = params->depth_multiplier;
op_params.weights_offset = 0;
op_params.float_activation_min = output_activation_min;
op_params.float_activation_max = output_activation_max;
const auto* affine_quantization =
reinterpret_cast<TfLiteAffineQuantization*>(filter->quantization.params);
if (kernel_type == kReference) {
reference_integer_ops::DepthwiseConvHybridPerChannel(
op_params, scaling_factors_ptr, GetTensorShape(input),
quantized_input_ptr_batch, GetTensorShape(filter),
GetTensorData<int8>(filter), GetTensorShape(bias),
GetTensorData<float>(bias), GetTensorShape(output),
GetTensorData<float>(output), affine_quantization->scale->data,
input_offset_ptr);
} else {
optimized_integer_ops::DepthwiseConvHybridPerChannel(
op_params, scaling_factors_ptr, GetTensorShape(input),
quantized_input_ptr_batch, GetTensorShape(filter),
GetTensorData<int8>(filter), GetTensorShape(bias),
GetTensorData<float>(bias), GetTensorShape(output),
GetTensorData<float>(output), affine_quantization->scale->data,
input_offset_ptr, CpuBackendContext::GetFromContext(context));
}
return kTfLiteOk;
} | 467 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::depthwise_conv::EvalImpl | tflite::ops::builtin::depthwise_conv::EvalImpl( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteDepthwiseConvParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
const TfLiteTensor* bias =
(NumInputs(node) == 3) ? GetInput(context, node, kBiasTensor) : nullptr;
TFLITE_DCHECK_EQ(input_type, input->type);
switch (input_type) { // Already know in/out types are same.
case kTfLiteFloat32:
if (filter->type == kTfLiteFloat32) {
return EvalFloat<kernel_type>(context, node, params, data, input,
filter, bias, output);
} else if (filter->type == kTfLiteInt8) {
return EvalHybridPerChannel<kernel_type>(context, node, params, data,
input, filter, bias, output);
} else {
TF_LITE_KERNEL_LOG(
context, "Type %s with filter type %s not currently supported.",
TfLiteTypeGetName(input->type), TfLiteTypeGetName(filter->type));
return kTfLiteError;
}
break;
case kTfLiteUInt8:
return EvalQuantized<kernel_type>(context, node, params, data, input,
filter, bias, output);
break;
case kTfLiteInt8:
return EvalQuantizedPerChannel<kernel_type>(context, node, params, data,
input, filter, bias, output);
break;
case kTfLiteInt16:
return EvalQuantizedPerChannel16x8(params, data, input, filter, bias,
output);
break;
default:
context->ReportError(context, "Type %d not currently supported.",
input->type);
return kTfLiteError;
}
} | 317 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::depthwise_conv::EvalImpl | tflite::ops::builtin::depthwise_conv::EvalImpl( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteDepthwiseConvParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
const TfLiteTensor* bias =
(NumInputs(node) == 3) ? GetInput(context, node, kBiasTensor) : nullptr;
TFLITE_DCHECK_EQ(input_type, input->type);
switch (input_type) { // Already know in/out types are same.
case kTfLiteFloat32:
if (filter->type == kTfLiteFloat32) {
return EvalFloat<kernel_type>(context, node, params, data, input,
filter, bias, output);
} else if (filter->type == kTfLiteInt8) {
return EvalHybridPerChannel<kernel_type>(context, node, params, data,
input, filter, bias, output);
} else {
TF_LITE_KERNEL_LOG(
context, "Type %s with filter type %s not currently supported.",
TfLiteTypeGetName(input->type), TfLiteTypeGetName(filter->type));
return kTfLiteError;
}
break;
case kTfLiteUInt8:
return EvalQuantized<kernel_type>(context, node, params, data, input,
filter, bias, output);
break;
case kTfLiteInt8:
return EvalQuantizedPerChannel<kernel_type>(context, node, params, data,
input, filter, bias, output);
break;
case kTfLiteInt16:
return EvalQuantizedPerChannel16x8(params, data, input, filter, bias,
output);
break;
default:
context->ReportError(context, "Type %d not currently supported.",
input->type);
return kTfLiteError;
}
} | 317 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::depthwise_conv::Prepare | tflite::ops::builtin::depthwise_conv::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteDepthwiseConvParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
// TODO(ahentz): use could use GetOptionalInputTensor() here, but we need to
// decide whether we are OK with optional tensors being completely absent, as
// opposed to having -1 as their index.
bool hasBias = NumInputs(node) == 3;
TF_LITE_ENSURE(context, hasBias || NumInputs(node) == 2);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
const TfLiteTensor* bias = nullptr;
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
TF_LITE_ENSURE_EQ(context, NumDimensions(filter), 4);
const TfLiteType data_type = input->type;
const TfLiteType filter_type = filter->type;
const bool is_hybrid =
data_type == kTfLiteFloat32 && filter_type == kTfLiteInt8;
TF_LITE_ENSURE(context,
data_type == kTfLiteFloat32 || data_type == kTfLiteUInt8 ||
data_type == kTfLiteInt8 || data_type == kTfLiteInt16);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, data_type);
if (!is_hybrid) {
TF_LITE_ENSURE(context,
filter->type == data_type || data_type == kTfLiteInt16);
}
// Filter in DepthwiseConv is expected to be [1, H, W, O].
TF_LITE_ENSURE_EQ(context, SizeOfDimension(filter, 0), 1);
if (hasBias) {
bias = GetInput(context, node, kBiasTensor);
if (data_type == kTfLiteUInt8 || data_type == kTfLiteInt8) {
TF_LITE_ENSURE_TYPES_EQ(context, bias->type, kTfLiteInt32);
TF_LITE_ENSURE_EQ(context, bias->params.zero_point, 0);
} else if (data_type == kTfLiteInt16) {
TF_LITE_ENSURE_TYPES_EQ(context, bias->type, kTfLiteInt64);
TF_LITE_ENSURE_EQ(context, bias->params.zero_point, 0);
TF_LITE_ENSURE_EQ(context, input->params.zero_point, 0);
TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, bias->type, data_type);
}
TF_LITE_ENSURE_EQ(context, NumDimensions(bias), 1);
TF_LITE_ENSURE_EQ(context, SizeOfDimension(filter, 3),
SizeOfDimension(bias, 0));
}
int channels_out = SizeOfDimension(filter, 3);
int width = SizeOfDimension(input, 2);
int height = SizeOfDimension(input, 1);
int filter_width = SizeOfDimension(filter, 2);
int filter_height = SizeOfDimension(filter, 1);
int batches = SizeOfDimension(input, 0);
// Matching GetWindowedOutputSize in TensorFlow.
auto padding = params->padding;
int out_width, out_height;
data->padding = ComputePaddingHeightWidth(
params->stride_height, params->stride_width,
params->dilation_height_factor, params->dilation_width_factor, height,
width, filter_height, filter_width, padding, &out_height, &out_width);
// Note that quantized inference requires that all tensors have their
// parameters set. This is usually done during quantized training or
// calibration.
if (data_type != kTfLiteFloat32) {
TF_LITE_ENSURE_EQ(context, filter->quantization.type,
kTfLiteAffineQuantization);
const auto* affine_quantization =
reinterpret_cast<TfLiteAffineQuantization*>(
filter->quantization.params);
TF_LITE_ENSURE(context, affine_quantization);
TF_LITE_ENSURE(context, affine_quantization->scale);
TF_LITE_ENSURE(context, (affine_quantization->scale->size == 1 ||
affine_quantization->scale->size == channels_out));
data->per_channel_output_multiplier.resize(channels_out);
data->per_channel_output_shift.resize(channels_out);
TF_LITE_ENSURE_STATUS(tflite::PopulateConvolutionQuantizationParams(
context, input, filter, bias, output, params->activation,
&data->output_multiplier, &data->output_shift,
&data->output_activation_min, &data->output_activation_max,
data->per_channel_output_multiplier.data(),
data->per_channel_output_shift.data(), channels_out));
}
if (is_hybrid) {
const auto* affine_quantization =
reinterpret_cast<TfLiteAffineQuantization*>(
filter->quantization.params);
TF_LITE_ENSURE(context, affine_quantization);
TF_LITE_ENSURE(context, affine_quantization->scale);
TF_LITE_ENSURE_EQ(
context, affine_quantization->scale->size,
filter->dims->data[affine_quantization->quantized_dimension]);
int temporaries_count = 0;
data->input_quantized_index = temporaries_count;
if (data->input_quantized_id == kTensorNotAllocated) {
TF_LITE_ENSURE_OK(
context, context->AddTensors(context, 1, &data->input_quantized_id));
}
++temporaries_count;
data->scaling_factors_index = temporaries_count;
if (data->scaling_factors_id == kTensorNotAllocated) {
TF_LITE_ENSURE_OK(
context, context->AddTensors(context, 1, &data->scaling_factors_id));
}
++temporaries_count;
data->input_offset_index = temporaries_count;
if (data->input_offset_id == kTensorNotAllocated) {
TF_LITE_ENSURE_OK(
context, context->AddTensors(context, 1, &data->input_offset_id));
}
++temporaries_count;
TfLiteIntArrayFree(node->temporaries);
node->temporaries = TfLiteIntArrayCreate(temporaries_count);
node->temporaries->data[data->input_quantized_index] =
data->input_quantized_id;
TfLiteTensor* input_quantized =
GetTemporary(context, node, data->input_quantized_index);
input_quantized->type = kTfLiteInt8;
input_quantized->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqual(input_quantized->dims, input->dims)) {
TfLiteIntArray* input_quantized_size = TfLiteIntArrayCopy(input->dims);
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, input_quantized,
input_quantized_size));
}
node->temporaries->data[data->scaling_factors_index] =
data->scaling_factors_id;
TfLiteTensor* scaling_factors =
GetTemporary(context, node, data->scaling_factors_index);
scaling_factors->type = kTfLiteFloat32;
scaling_factors->allocation_type = kTfLiteArenaRw;
const int batch_size = SizeOfDimension(input, 0);
int scaling_dims[1] = {batch_size};
if (!TfLiteIntArrayEqualsArray(scaling_factors->dims, 1, scaling_dims)) {
TfLiteIntArray* scaling_factors_size = TfLiteIntArrayCreate(1);
scaling_factors_size->data[0] = batch_size;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scaling_factors,
scaling_factors_size));
}
node->temporaries->data[data->input_offset_index] = data->input_offset_id;
TfLiteTensor* input_offsets =
GetTemporary(context, node, data->input_offset_index);
input_offsets->type = kTfLiteInt32;
input_offsets->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqualsArray(input_offsets->dims, 1, scaling_dims)) {
TfLiteIntArray* input_offsets_size = TfLiteIntArrayCreate(1);
input_offsets_size->data[0] = batch_size;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, input_offsets,
input_offsets_size));
}
}
TfLiteIntArray* outputSize = TfLiteIntArrayCreate(4);
outputSize->data[0] = batches;
outputSize->data[1] = out_height;
outputSize->data[2] = out_width;
outputSize->data[3] = channels_out;
return context->ResizeTensor(context, output, outputSize);
} | 1214 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::depthwise_conv::Prepare | tflite::ops::builtin::depthwise_conv::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteDepthwiseConvParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
// TODO(ahentz): use could use GetOptionalInputTensor() here, but we need to
// decide whether we are OK with optional tensors being completely absent, as
// opposed to having -1 as their index.
bool hasBias = NumInputs(node) == 3;
TF_LITE_ENSURE(context, hasBias || NumInputs(node) == 2);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
const TfLiteTensor* bias = nullptr;
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
TF_LITE_ENSURE_EQ(context, NumDimensions(filter), 4);
const TfLiteType data_type = input->type;
const TfLiteType filter_type = filter->type;
const bool is_hybrid =
data_type == kTfLiteFloat32 && filter_type == kTfLiteInt8;
TF_LITE_ENSURE(context,
data_type == kTfLiteFloat32 || data_type == kTfLiteUInt8 ||
data_type == kTfLiteInt8 || data_type == kTfLiteInt16);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, data_type);
if (!is_hybrid) {
TF_LITE_ENSURE(context,
filter->type == data_type || data_type == kTfLiteInt16);
}
// Filter in DepthwiseConv is expected to be [1, H, W, O].
TF_LITE_ENSURE_EQ(context, SizeOfDimension(filter, 0), 1);
if (hasBias) {
bias = GetInput(context, node, kBiasTensor);
if (data_type == kTfLiteUInt8 || data_type == kTfLiteInt8) {
TF_LITE_ENSURE_TYPES_EQ(context, bias->type, kTfLiteInt32);
TF_LITE_ENSURE_EQ(context, bias->params.zero_point, 0);
} else if (data_type == kTfLiteInt16) {
TF_LITE_ENSURE_TYPES_EQ(context, bias->type, kTfLiteInt64);
TF_LITE_ENSURE_EQ(context, bias->params.zero_point, 0);
TF_LITE_ENSURE_EQ(context, input->params.zero_point, 0);
TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, bias->type, data_type);
}
TF_LITE_ENSURE_EQ(context, NumDimensions(bias), 1);
TF_LITE_ENSURE_EQ(context, SizeOfDimension(filter, 3),
SizeOfDimension(bias, 0));
}
int channels_out = SizeOfDimension(filter, 3);
int width = SizeOfDimension(input, 2);
int height = SizeOfDimension(input, 1);
int filter_width = SizeOfDimension(filter, 2);
int filter_height = SizeOfDimension(filter, 1);
int batches = SizeOfDimension(input, 0);
// Matching GetWindowedOutputSize in TensorFlow.
auto padding = params->padding;
int out_width, out_height;
data->padding = ComputePaddingHeightWidth(
params->stride_height, params->stride_width,
params->dilation_height_factor, params->dilation_width_factor, height,
width, filter_height, filter_width, padding, &out_height, &out_width);
// Note that quantized inference requires that all tensors have their
// parameters set. This is usually done during quantized training or
// calibration.
if (data_type != kTfLiteFloat32) {
TF_LITE_ENSURE_EQ(context, filter->quantization.type,
kTfLiteAffineQuantization);
const auto* affine_quantization =
reinterpret_cast<TfLiteAffineQuantization*>(
filter->quantization.params);
TF_LITE_ENSURE(context, affine_quantization);
TF_LITE_ENSURE(context, affine_quantization->scale);
TF_LITE_ENSURE(context, (affine_quantization->scale->size == 1 ||
affine_quantization->scale->size == channels_out));
data->per_channel_output_multiplier.resize(channels_out);
data->per_channel_output_shift.resize(channels_out);
TF_LITE_ENSURE_STATUS(tflite::PopulateConvolutionQuantizationParams(
context, input, filter, bias, output, params->activation,
&data->output_multiplier, &data->output_shift,
&data->output_activation_min, &data->output_activation_max,
data->per_channel_output_multiplier.data(),
data->per_channel_output_shift.data(), channels_out));
}
if (is_hybrid) {
const auto* affine_quantization =
reinterpret_cast<TfLiteAffineQuantization*>(
filter->quantization.params);
TF_LITE_ENSURE(context, affine_quantization);
TF_LITE_ENSURE(context, affine_quantization->scale);
TF_LITE_ENSURE_EQ(
context, affine_quantization->scale->size,
filter->dims->data[affine_quantization->quantized_dimension]);
int temporaries_count = 0;
data->input_quantized_index = temporaries_count;
if (data->input_quantized_id == kTensorNotAllocated) {
TF_LITE_ENSURE_OK(
context, context->AddTensors(context, 1, &data->input_quantized_id));
}
++temporaries_count;
data->scaling_factors_index = temporaries_count;
if (data->scaling_factors_id == kTensorNotAllocated) {
TF_LITE_ENSURE_OK(
context, context->AddTensors(context, 1, &data->scaling_factors_id));
}
++temporaries_count;
data->input_offset_index = temporaries_count;
if (data->input_offset_id == kTensorNotAllocated) {
TF_LITE_ENSURE_OK(
context, context->AddTensors(context, 1, &data->input_offset_id));
}
++temporaries_count;
TfLiteIntArrayFree(node->temporaries);
node->temporaries = TfLiteIntArrayCreate(temporaries_count);
node->temporaries->data[data->input_quantized_index] =
data->input_quantized_id;
TfLiteTensor* input_quantized =
GetTemporary(context, node, data->input_quantized_index);
input_quantized->type = kTfLiteInt8;
input_quantized->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqual(input_quantized->dims, input->dims)) {
TfLiteIntArray* input_quantized_size = TfLiteIntArrayCopy(input->dims);
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, input_quantized,
input_quantized_size));
}
node->temporaries->data[data->scaling_factors_index] =
data->scaling_factors_id;
TfLiteTensor* scaling_factors =
GetTemporary(context, node, data->scaling_factors_index);
scaling_factors->type = kTfLiteFloat32;
scaling_factors->allocation_type = kTfLiteArenaRw;
const int batch_size = SizeOfDimension(input, 0);
int scaling_dims[1] = {batch_size};
if (!TfLiteIntArrayEqualsArray(scaling_factors->dims, 1, scaling_dims)) {
TfLiteIntArray* scaling_factors_size = TfLiteIntArrayCreate(1);
scaling_factors_size->data[0] = batch_size;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scaling_factors,
scaling_factors_size));
}
node->temporaries->data[data->input_offset_index] = data->input_offset_id;
TfLiteTensor* input_offsets =
GetTemporary(context, node, data->input_offset_index);
input_offsets->type = kTfLiteInt32;
input_offsets->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqualsArray(input_offsets->dims, 1, scaling_dims)) {
TfLiteIntArray* input_offsets_size = TfLiteIntArrayCreate(1);
input_offsets_size->data[0] = batch_size;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, input_offsets,
input_offsets_size));
}
}
TfLiteIntArray* outputSize = TfLiteIntArrayCreate(4);
outputSize->data[0] = batches;
outputSize->data[1] = out_height;
outputSize->data[2] = out_width;
outputSize->data[3] = channels_out;
return context->ResizeTensor(context, output, outputSize);
} | 1214 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::custom::detection_postprocess::DecodeCenterSizeBoxes | tflite::ops::custom::detection_postprocess::DecodeCenterSizeBoxes( TfLiteContext * context , TfLiteNode * node , OpData * op_data) | ['context', 'node', 'op_data'] | TfLiteStatus DecodeCenterSizeBoxes(TfLiteContext* context, TfLiteNode* node,
OpData* op_data) {
// Parse input tensor boxencodings
const TfLiteTensor* input_box_encodings =
GetInput(context, node, kInputTensorBoxEncodings);
TF_LITE_ENSURE_EQ(context, input_box_encodings->dims->data[0], kBatchSize);
const int num_boxes = input_box_encodings->dims->data[1];
TF_LITE_ENSURE(context, input_box_encodings->dims->data[2] >= kNumCoordBox);
const TfLiteTensor* input_anchors =
GetInput(context, node, kInputTensorAnchors);
// Decode the boxes to get (ymin, xmin, ymax, xmax) based on the anchors
CenterSizeEncoding box_centersize;
CenterSizeEncoding scale_values = op_data->scale_values;
CenterSizeEncoding anchor;
for (int idx = 0; idx < num_boxes; ++idx) {
switch (input_box_encodings->type) {
// Quantized
case kTfLiteUInt8:
DequantizeBoxEncodings(
input_box_encodings, idx,
static_cast<float>(input_box_encodings->params.zero_point),
static_cast<float>(input_box_encodings->params.scale),
input_box_encodings->dims->data[2], &box_centersize);
DequantizeBoxEncodings(
input_anchors, idx,
static_cast<float>(input_anchors->params.zero_point),
static_cast<float>(input_anchors->params.scale), kNumCoordBox,
&anchor);
break;
// Float
case kTfLiteFloat32: {
// Please see DequantizeBoxEncodings function for the support detail.
const int box_encoding_idx = idx * input_box_encodings->dims->data[2];
const float* boxes =
&(GetTensorData<float>(input_box_encodings)[box_encoding_idx]);
box_centersize = *reinterpret_cast<const CenterSizeEncoding*>(boxes);
anchor =
ReInterpretTensor<const CenterSizeEncoding*>(input_anchors)[idx];
break;
}
default:
// Unsupported type.
return kTfLiteError;
}
float ycenter = box_centersize.y / scale_values.y * anchor.h + anchor.y;
float xcenter = box_centersize.x / scale_values.x * anchor.w + anchor.x;
float half_h =
0.5f * static_cast<float>(std::exp(box_centersize.h / scale_values.h)) *
anchor.h;
float half_w =
0.5f * static_cast<float>(std::exp(box_centersize.w / scale_values.w)) *
anchor.w;
TfLiteTensor* decoded_boxes =
&context->tensors[op_data->decoded_boxes_index];
auto& box = ReInterpretTensor<BoxCornerEncoding*>(decoded_boxes)[idx];
box.ymin = ycenter - half_h;
box.xmin = xcenter - half_w;
box.ymax = ycenter + half_h;
box.xmax = xcenter + half_w;
}
return kTfLiteOk;
} | 447 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::custom::detection_postprocess::DecodeCenterSizeBoxes | tflite::ops::custom::detection_postprocess::DecodeCenterSizeBoxes( TfLiteContext * context , TfLiteNode * node , OpData * op_data) | ['context', 'node', 'op_data'] | TfLiteStatus DecodeCenterSizeBoxes(TfLiteContext* context, TfLiteNode* node,
OpData* op_data) {
// Parse input tensor boxencodings
const TfLiteTensor* input_box_encodings =
GetInput(context, node, kInputTensorBoxEncodings);
TF_LITE_ENSURE_EQ(context, input_box_encodings->dims->data[0], kBatchSize);
const int num_boxes = input_box_encodings->dims->data[1];
TF_LITE_ENSURE(context, input_box_encodings->dims->data[2] >= kNumCoordBox);
const TfLiteTensor* input_anchors =
GetInput(context, node, kInputTensorAnchors);
// Decode the boxes to get (ymin, xmin, ymax, xmax) based on the anchors
CenterSizeEncoding box_centersize;
CenterSizeEncoding scale_values = op_data->scale_values;
CenterSizeEncoding anchor;
for (int idx = 0; idx < num_boxes; ++idx) {
switch (input_box_encodings->type) {
// Quantized
case kTfLiteUInt8:
DequantizeBoxEncodings(
input_box_encodings, idx,
static_cast<float>(input_box_encodings->params.zero_point),
static_cast<float>(input_box_encodings->params.scale),
input_box_encodings->dims->data[2], &box_centersize);
DequantizeBoxEncodings(
input_anchors, idx,
static_cast<float>(input_anchors->params.zero_point),
static_cast<float>(input_anchors->params.scale), kNumCoordBox,
&anchor);
break;
// Float
case kTfLiteFloat32: {
// Please see DequantizeBoxEncodings function for the support detail.
const int box_encoding_idx = idx * input_box_encodings->dims->data[2];
const float* boxes =
&(GetTensorData<float>(input_box_encodings)[box_encoding_idx]);
box_centersize = *reinterpret_cast<const CenterSizeEncoding*>(boxes);
anchor =
ReInterpretTensor<const CenterSizeEncoding*>(input_anchors)[idx];
break;
}
default:
// Unsupported type.
return kTfLiteError;
}
float ycenter = box_centersize.y / scale_values.y * anchor.h + anchor.y;
float xcenter = box_centersize.x / scale_values.x * anchor.w + anchor.x;
float half_h =
0.5f * static_cast<float>(std::exp(box_centersize.h / scale_values.h)) *
anchor.h;
float half_w =
0.5f * static_cast<float>(std::exp(box_centersize.w / scale_values.w)) *
anchor.w;
TfLiteTensor* decoded_boxes =
&context->tensors[op_data->decoded_boxes_index];
auto& box = ReInterpretTensor<BoxCornerEncoding*>(decoded_boxes)[idx];
box.ymin = ycenter - half_h;
box.xmin = xcenter - half_w;
box.ymax = ycenter + half_h;
box.xmax = xcenter + half_w;
}
return kTfLiteOk;
} | 447 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::custom::detection_postprocess::NonMaxSuppressionMultiClass | tflite::ops::custom::detection_postprocess::NonMaxSuppressionMultiClass( TfLiteContext * context , TfLiteNode * node , OpData * op_data) | ['context', 'node', 'op_data'] | TfLiteStatus NonMaxSuppressionMultiClass(TfLiteContext* context,
TfLiteNode* node, OpData* op_data) {
// Get the input tensors
const TfLiteTensor* input_box_encodings =
GetInput(context, node, kInputTensorBoxEncodings);
const TfLiteTensor* input_class_predictions =
GetInput(context, node, kInputTensorClassPredictions);
const int num_boxes = input_box_encodings->dims->data[1];
const int num_classes = op_data->num_classes;
TF_LITE_ENSURE_EQ(context, input_class_predictions->dims->data[0],
kBatchSize);
TF_LITE_ENSURE_EQ(context, input_class_predictions->dims->data[1], num_boxes);
const int num_classes_with_background =
input_class_predictions->dims->data[2];
TF_LITE_ENSURE(context, (num_classes_with_background - num_classes <= 1));
TF_LITE_ENSURE(context, (num_classes_with_background >= num_classes));
const TfLiteTensor* scores;
switch (input_class_predictions->type) {
case kTfLiteUInt8: {
TfLiteTensor* temporary_scores = &context->tensors[op_data->scores_index];
DequantizeClassPredictions(input_class_predictions, num_boxes,
num_classes_with_background, temporary_scores);
scores = temporary_scores;
} break;
case kTfLiteFloat32:
scores = input_class_predictions;
break;
default:
// Unsupported type.
return kTfLiteError;
}
if (op_data->use_regular_non_max_suppression)
TF_LITE_ENSURE_STATUS(NonMaxSuppressionMultiClassRegularHelper(
context, node, op_data, GetTensorData<float>(scores)));
else
TF_LITE_ENSURE_STATUS(NonMaxSuppressionMultiClassFastHelper(
context, node, op_data, GetTensorData<float>(scores)));
return kTfLiteOk;
} | 247 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::custom::detection_postprocess::NonMaxSuppressionMultiClass | tflite::ops::custom::detection_postprocess::NonMaxSuppressionMultiClass( TfLiteContext * context , TfLiteNode * node , OpData * op_data) | ['context', 'node', 'op_data'] | TfLiteStatus NonMaxSuppressionMultiClass(TfLiteContext* context,
TfLiteNode* node, OpData* op_data) {
// Get the input tensors
const TfLiteTensor* input_box_encodings =
GetInput(context, node, kInputTensorBoxEncodings);
const TfLiteTensor* input_class_predictions =
GetInput(context, node, kInputTensorClassPredictions);
const int num_boxes = input_box_encodings->dims->data[1];
const int num_classes = op_data->num_classes;
TF_LITE_ENSURE_EQ(context, input_class_predictions->dims->data[0],
kBatchSize);
TF_LITE_ENSURE_EQ(context, input_class_predictions->dims->data[1], num_boxes);
const int num_classes_with_background =
input_class_predictions->dims->data[2];
TF_LITE_ENSURE(context, (num_classes_with_background - num_classes <= 1));
TF_LITE_ENSURE(context, (num_classes_with_background >= num_classes));
const TfLiteTensor* scores;
switch (input_class_predictions->type) {
case kTfLiteUInt8: {
TfLiteTensor* temporary_scores = &context->tensors[op_data->scores_index];
DequantizeClassPredictions(input_class_predictions, num_boxes,
num_classes_with_background, temporary_scores);
scores = temporary_scores;
} break;
case kTfLiteFloat32:
scores = input_class_predictions;
break;
default:
// Unsupported type.
return kTfLiteError;
}
if (op_data->use_regular_non_max_suppression)
TF_LITE_ENSURE_STATUS(NonMaxSuppressionMultiClassRegularHelper(
context, node, op_data, GetTensorData<float>(scores)));
else
TF_LITE_ENSURE_STATUS(NonMaxSuppressionMultiClassFastHelper(
context, node, op_data, GetTensorData<float>(scores)));
return kTfLiteOk;
} | 247 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::custom::detection_postprocess::NonMaxSuppressionMultiClassFastHelper | tflite::ops::custom::detection_postprocess::NonMaxSuppressionMultiClassFastHelper( TfLiteContext * context , TfLiteNode * node , OpData * op_data , const float * scores) | ['context', 'node', 'op_data', 'scores'] | TfLiteStatus NonMaxSuppressionMultiClassFastHelper(TfLiteContext* context,
TfLiteNode* node,
OpData* op_data,
const float* scores) {
const TfLiteTensor* input_box_encodings =
GetInput(context, node, kInputTensorBoxEncodings);
const TfLiteTensor* input_class_predictions =
GetInput(context, node, kInputTensorClassPredictions);
const TfLiteTensor* decoded_boxes =
&context->tensors[op_data->decoded_boxes_index];
TfLiteTensor* detection_boxes =
GetOutput(context, node, kOutputTensorDetectionBoxes);
TfLiteTensor* detection_classes =
GetOutput(context, node, kOutputTensorDetectionClasses);
TfLiteTensor* detection_scores =
GetOutput(context, node, kOutputTensorDetectionScores);
TfLiteTensor* num_detections =
GetOutput(context, node, kOutputTensorNumDetections);
const int num_boxes = input_box_encodings->dims->data[1];
const int num_classes = op_data->num_classes;
const int max_categories_per_anchor = op_data->max_classes_per_detection;
const int num_classes_with_background =
input_class_predictions->dims->data[2];
// The row index offset is 1 if background class is included and 0 otherwise.
int label_offset = num_classes_with_background - num_classes;
TF_LITE_ENSURE(context, (max_categories_per_anchor > 0));
const int num_categories_per_anchor =
std::min(max_categories_per_anchor, num_classes);
std::vector<float> max_scores;
max_scores.resize(num_boxes);
std::vector<int> sorted_class_indices;
sorted_class_indices.resize(num_boxes * num_classes);
for (int row = 0; row < num_boxes; row++) {
const float* box_scores =
scores + row * num_classes_with_background + label_offset;
int* class_indices = sorted_class_indices.data() + row * num_classes;
DecreasingPartialArgSort(box_scores, num_classes, num_categories_per_anchor,
class_indices);
max_scores[row] = box_scores[class_indices[0]];
}
// Perform non-maximal suppression on max scores
std::vector<int> selected;
TF_LITE_ENSURE_STATUS(NonMaxSuppressionSingleClassHelper(
context, node, op_data, max_scores, &selected, op_data->max_detections));
// Allocate output tensors
int output_box_index = 0;
for (const auto& selected_index : selected) {
const float* box_scores =
scores + selected_index * num_classes_with_background + label_offset;
const int* class_indices =
sorted_class_indices.data() + selected_index * num_classes;
for (int col = 0; col < num_categories_per_anchor; ++col) {
int box_offset = num_categories_per_anchor * output_box_index + col;
// detection_boxes
ReInterpretTensor<BoxCornerEncoding*>(detection_boxes)[box_offset] =
ReInterpretTensor<const BoxCornerEncoding*>(
decoded_boxes)[selected_index];
// detection_classes
GetTensorData<float>(detection_classes)[box_offset] = class_indices[col];
// detection_scores
GetTensorData<float>(detection_scores)[box_offset] =
box_scores[class_indices[col]];
output_box_index++;
}
}
GetTensorData<float>(num_detections)[0] = output_box_index;
return kTfLiteOk;
} | 465 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::custom::detection_postprocess::NonMaxSuppressionMultiClassFastHelper | tflite::ops::custom::detection_postprocess::NonMaxSuppressionMultiClassFastHelper( TfLiteContext * context , TfLiteNode * node , OpData * op_data , const float * scores) | ['context', 'node', 'op_data', 'scores'] | TfLiteStatus NonMaxSuppressionMultiClassFastHelper(TfLiteContext* context,
TfLiteNode* node,
OpData* op_data,
const float* scores) {
const TfLiteTensor* input_box_encodings =
GetInput(context, node, kInputTensorBoxEncodings);
const TfLiteTensor* input_class_predictions =
GetInput(context, node, kInputTensorClassPredictions);
const TfLiteTensor* decoded_boxes =
&context->tensors[op_data->decoded_boxes_index];
TfLiteTensor* detection_boxes =
GetOutput(context, node, kOutputTensorDetectionBoxes);
TfLiteTensor* detection_classes =
GetOutput(context, node, kOutputTensorDetectionClasses);
TfLiteTensor* detection_scores =
GetOutput(context, node, kOutputTensorDetectionScores);
TfLiteTensor* num_detections =
GetOutput(context, node, kOutputTensorNumDetections);
const int num_boxes = input_box_encodings->dims->data[1];
const int num_classes = op_data->num_classes;
const int max_categories_per_anchor = op_data->max_classes_per_detection;
const int num_classes_with_background =
input_class_predictions->dims->data[2];
// The row index offset is 1 if background class is included and 0 otherwise.
int label_offset = num_classes_with_background - num_classes;
TF_LITE_ENSURE(context, (max_categories_per_anchor > 0));
const int num_categories_per_anchor =
std::min(max_categories_per_anchor, num_classes);
std::vector<float> max_scores;
max_scores.resize(num_boxes);
std::vector<int> sorted_class_indices;
sorted_class_indices.resize(num_boxes * num_classes);
for (int row = 0; row < num_boxes; row++) {
const float* box_scores =
scores + row * num_classes_with_background + label_offset;
int* class_indices = sorted_class_indices.data() + row * num_classes;
DecreasingPartialArgSort(box_scores, num_classes, num_categories_per_anchor,
class_indices);
max_scores[row] = box_scores[class_indices[0]];
}
// Perform non-maximal suppression on max scores
std::vector<int> selected;
TF_LITE_ENSURE_STATUS(NonMaxSuppressionSingleClassHelper(
context, node, op_data, max_scores, &selected, op_data->max_detections));
// Allocate output tensors
int output_box_index = 0;
for (const auto& selected_index : selected) {
const float* box_scores =
scores + selected_index * num_classes_with_background + label_offset;
const int* class_indices =
sorted_class_indices.data() + selected_index * num_classes;
for (int col = 0; col < num_categories_per_anchor; ++col) {
int box_offset = num_categories_per_anchor * output_box_index + col;
// detection_boxes
ReInterpretTensor<BoxCornerEncoding*>(detection_boxes)[box_offset] =
ReInterpretTensor<const BoxCornerEncoding*>(
decoded_boxes)[selected_index];
// detection_classes
GetTensorData<float>(detection_classes)[box_offset] = class_indices[col];
// detection_scores
GetTensorData<float>(detection_scores)[box_offset] =
box_scores[class_indices[col]];
output_box_index++;
}
}
GetTensorData<float>(num_detections)[0] = output_box_index;
return kTfLiteOk;
} | 465 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::custom::detection_postprocess::NonMaxSuppressionMultiClassRegularHelper | tflite::ops::custom::detection_postprocess::NonMaxSuppressionMultiClassRegularHelper( TfLiteContext * context , TfLiteNode * node , OpData * op_data , const float * scores) | ['context', 'node', 'op_data', 'scores'] | TfLiteStatus NonMaxSuppressionMultiClassRegularHelper(TfLiteContext* context,
TfLiteNode* node,
OpData* op_data,
const float* scores) {
const TfLiteTensor* input_box_encodings =
GetInput(context, node, kInputTensorBoxEncodings);
const TfLiteTensor* input_class_predictions =
GetInput(context, node, kInputTensorClassPredictions);
const TfLiteTensor* decoded_boxes =
&context->tensors[op_data->decoded_boxes_index];
TfLiteTensor* detection_boxes =
GetOutput(context, node, kOutputTensorDetectionBoxes);
TfLiteTensor* detection_classes =
GetOutput(context, node, kOutputTensorDetectionClasses);
TfLiteTensor* detection_scores =
GetOutput(context, node, kOutputTensorDetectionScores);
TfLiteTensor* num_detections =
GetOutput(context, node, kOutputTensorNumDetections);
const int num_boxes = input_box_encodings->dims->data[1];
const int num_classes = op_data->num_classes;
const int num_detections_per_class = op_data->detections_per_class;
const int max_detections = op_data->max_detections;
const int num_classes_with_background =
input_class_predictions->dims->data[2];
// The row index offset is 1 if background class is included and 0 otherwise.
int label_offset = num_classes_with_background - num_classes;
TF_LITE_ENSURE(context, num_detections_per_class > 0);
// For each class, perform non-max suppression.
std::vector<float> class_scores(num_boxes);
std::vector<int> box_indices_after_regular_non_max_suppression(
num_boxes + max_detections);
std::vector<float> scores_after_regular_non_max_suppression(num_boxes +
max_detections);
int size_of_sorted_indices = 0;
std::vector<int> sorted_indices;
sorted_indices.resize(num_boxes + max_detections);
std::vector<float> sorted_values;
sorted_values.resize(max_detections);
for (int col = 0; col < num_classes; col++) {
for (int row = 0; row < num_boxes; row++) {
// Get scores of boxes corresponding to all anchors for single class
class_scores[row] =
*(scores + row * num_classes_with_background + col + label_offset);
}
// Perform non-maximal suppression on single class
std::vector<int> selected;
TF_LITE_ENSURE_STATUS(NonMaxSuppressionSingleClassHelper(
context, node, op_data, class_scores, &selected,
num_detections_per_class));
// Add selected indices from non-max suppression of boxes in this class
int output_index = size_of_sorted_indices;
for (const auto& selected_index : selected) {
box_indices_after_regular_non_max_suppression[output_index] =
(selected_index * num_classes_with_background + col + label_offset);
scores_after_regular_non_max_suppression[output_index] =
class_scores[selected_index];
output_index++;
}
// Sort the max scores among the selected indices
// Get the indices for top scores
int num_indices_to_sort = std::min(output_index, max_detections);
DecreasingPartialArgSort(scores_after_regular_non_max_suppression.data(),
output_index, num_indices_to_sort,
sorted_indices.data());
// Copy values to temporary vectors
for (int row = 0; row < num_indices_to_sort; row++) {
int temp = sorted_indices[row];
sorted_indices[row] = box_indices_after_regular_non_max_suppression[temp];
sorted_values[row] = scores_after_regular_non_max_suppression[temp];
}
// Copy scores and indices from temporary vectors
for (int row = 0; row < num_indices_to_sort; row++) {
box_indices_after_regular_non_max_suppression[row] = sorted_indices[row];
scores_after_regular_non_max_suppression[row] = sorted_values[row];
}
size_of_sorted_indices = num_indices_to_sort;
}
// Allocate output tensors
for (int output_box_index = 0; output_box_index < max_detections;
output_box_index++) {
if (output_box_index < size_of_sorted_indices) {
const int anchor_index = floor(
box_indices_after_regular_non_max_suppression[output_box_index] /
num_classes_with_background);
const int class_index =
box_indices_after_regular_non_max_suppression[output_box_index] -
anchor_index * num_classes_with_background - label_offset;
const float selected_score =
scores_after_regular_non_max_suppression[output_box_index];
// detection_boxes
ReInterpretTensor<BoxCornerEncoding*>(detection_boxes)[output_box_index] =
ReInterpretTensor<const BoxCornerEncoding*>(
decoded_boxes)[anchor_index];
// detection_classes
GetTensorData<float>(detection_classes)[output_box_index] = class_index;
// detection_scores
GetTensorData<float>(detection_scores)[output_box_index] = selected_score;
} else {
ReInterpretTensor<BoxCornerEncoding*>(
detection_boxes)[output_box_index] = {0.0f, 0.0f, 0.0f, 0.0f};
// detection_classes
GetTensorData<float>(detection_classes)[output_box_index] = 0.0f;
// detection_scores
GetTensorData<float>(detection_scores)[output_box_index] = 0.0f;
}
}
GetTensorData<float>(num_detections)[0] = size_of_sorted_indices;
box_indices_after_regular_non_max_suppression.clear();
scores_after_regular_non_max_suppression.clear();
return kTfLiteOk;
} | 696 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::custom::detection_postprocess::NonMaxSuppressionMultiClassRegularHelper | tflite::ops::custom::detection_postprocess::NonMaxSuppressionMultiClassRegularHelper( TfLiteContext * context , TfLiteNode * node , OpData * op_data , const float * scores) | ['context', 'node', 'op_data', 'scores'] | TfLiteStatus NonMaxSuppressionMultiClassRegularHelper(TfLiteContext* context,
TfLiteNode* node,
OpData* op_data,
const float* scores) {
const TfLiteTensor* input_box_encodings =
GetInput(context, node, kInputTensorBoxEncodings);
const TfLiteTensor* input_class_predictions =
GetInput(context, node, kInputTensorClassPredictions);
const TfLiteTensor* decoded_boxes =
&context->tensors[op_data->decoded_boxes_index];
TfLiteTensor* detection_boxes =
GetOutput(context, node, kOutputTensorDetectionBoxes);
TfLiteTensor* detection_classes =
GetOutput(context, node, kOutputTensorDetectionClasses);
TfLiteTensor* detection_scores =
GetOutput(context, node, kOutputTensorDetectionScores);
TfLiteTensor* num_detections =
GetOutput(context, node, kOutputTensorNumDetections);
const int num_boxes = input_box_encodings->dims->data[1];
const int num_classes = op_data->num_classes;
const int num_detections_per_class = op_data->detections_per_class;
const int max_detections = op_data->max_detections;
const int num_classes_with_background =
input_class_predictions->dims->data[2];
// The row index offset is 1 if background class is included and 0 otherwise.
int label_offset = num_classes_with_background - num_classes;
TF_LITE_ENSURE(context, num_detections_per_class > 0);
// For each class, perform non-max suppression.
std::vector<float> class_scores(num_boxes);
std::vector<int> box_indices_after_regular_non_max_suppression(
num_boxes + max_detections);
std::vector<float> scores_after_regular_non_max_suppression(num_boxes +
max_detections);
int size_of_sorted_indices = 0;
std::vector<int> sorted_indices;
sorted_indices.resize(num_boxes + max_detections);
std::vector<float> sorted_values;
sorted_values.resize(max_detections);
for (int col = 0; col < num_classes; col++) {
for (int row = 0; row < num_boxes; row++) {
// Get scores of boxes corresponding to all anchors for single class
class_scores[row] =
*(scores + row * num_classes_with_background + col + label_offset);
}
// Perform non-maximal suppression on single class
std::vector<int> selected;
TF_LITE_ENSURE_STATUS(NonMaxSuppressionSingleClassHelper(
context, node, op_data, class_scores, &selected,
num_detections_per_class));
// Add selected indices from non-max suppression of boxes in this class
int output_index = size_of_sorted_indices;
for (const auto& selected_index : selected) {
box_indices_after_regular_non_max_suppression[output_index] =
(selected_index * num_classes_with_background + col + label_offset);
scores_after_regular_non_max_suppression[output_index] =
class_scores[selected_index];
output_index++;
}
// Sort the max scores among the selected indices
// Get the indices for top scores
int num_indices_to_sort = std::min(output_index, max_detections);
DecreasingPartialArgSort(scores_after_regular_non_max_suppression.data(),
output_index, num_indices_to_sort,
sorted_indices.data());
// Copy values to temporary vectors
for (int row = 0; row < num_indices_to_sort; row++) {
int temp = sorted_indices[row];
sorted_indices[row] = box_indices_after_regular_non_max_suppression[temp];
sorted_values[row] = scores_after_regular_non_max_suppression[temp];
}
// Copy scores and indices from temporary vectors
for (int row = 0; row < num_indices_to_sort; row++) {
box_indices_after_regular_non_max_suppression[row] = sorted_indices[row];
scores_after_regular_non_max_suppression[row] = sorted_values[row];
}
size_of_sorted_indices = num_indices_to_sort;
}
// Allocate output tensors
for (int output_box_index = 0; output_box_index < max_detections;
output_box_index++) {
if (output_box_index < size_of_sorted_indices) {
const int anchor_index = floor(
box_indices_after_regular_non_max_suppression[output_box_index] /
num_classes_with_background);
const int class_index =
box_indices_after_regular_non_max_suppression[output_box_index] -
anchor_index * num_classes_with_background - label_offset;
const float selected_score =
scores_after_regular_non_max_suppression[output_box_index];
// detection_boxes
ReInterpretTensor<BoxCornerEncoding*>(detection_boxes)[output_box_index] =
ReInterpretTensor<const BoxCornerEncoding*>(
decoded_boxes)[anchor_index];
// detection_classes
GetTensorData<float>(detection_classes)[output_box_index] = class_index;
// detection_scores
GetTensorData<float>(detection_scores)[output_box_index] = selected_score;
} else {
ReInterpretTensor<BoxCornerEncoding*>(
detection_boxes)[output_box_index] = {0.0f, 0.0f, 0.0f, 0.0f};
// detection_classes
GetTensorData<float>(detection_classes)[output_box_index] = 0.0f;
// detection_scores
GetTensorData<float>(detection_scores)[output_box_index] = 0.0f;
}
}
GetTensorData<float>(num_detections)[0] = size_of_sorted_indices;
box_indices_after_regular_non_max_suppression.clear();
scores_after_regular_non_max_suppression.clear();
return kTfLiteOk;
} | 696 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::custom::detection_postprocess::NonMaxSuppressionSingleClassHelper | tflite::ops::custom::detection_postprocess::NonMaxSuppressionSingleClassHelper( TfLiteContext * context , TfLiteNode * node , OpData * op_data , const std :: vector<float> & scores , std :: vector<int> * selected , int max_detections) | ['context', 'node', 'op_data', 'scores', 'selected', 'max_detections'] | TfLiteStatus NonMaxSuppressionSingleClassHelper(
TfLiteContext* context, TfLiteNode* node, OpData* op_data,
const std::vector<float>& scores, std::vector<int>* selected,
int max_detections) {
const TfLiteTensor* input_box_encodings =
GetInput(context, node, kInputTensorBoxEncodings);
const TfLiteTensor* decoded_boxes =
&context->tensors[op_data->decoded_boxes_index];
const int num_boxes = input_box_encodings->dims->data[1];
const float non_max_suppression_score_threshold =
op_data->non_max_suppression_score_threshold;
const float intersection_over_union_threshold =
op_data->intersection_over_union_threshold;
// Maximum detections should be positive.
TF_LITE_ENSURE(context, (max_detections >= 0));
// intersection_over_union_threshold should be positive
// and should be less than 1.
TF_LITE_ENSURE(context, (intersection_over_union_threshold > 0.0f) &&
(intersection_over_union_threshold <= 1.0f));
// Validate boxes
TF_LITE_ENSURE(context, ValidateBoxes(decoded_boxes, num_boxes));
// threshold scores
std::vector<int> keep_indices;
// TODO (chowdhery): Remove the dynamic allocation and replace it
// with temporaries, esp for std::vector<float>
std::vector<float> keep_scores;
SelectDetectionsAboveScoreThreshold(
scores, non_max_suppression_score_threshold, &keep_scores, &keep_indices);
int num_scores_kept = keep_scores.size();
std::vector<int> sorted_indices;
sorted_indices.resize(num_scores_kept);
DecreasingPartialArgSort(keep_scores.data(), num_scores_kept, num_scores_kept,
sorted_indices.data());
const int num_boxes_kept = num_scores_kept;
const int output_size = std::min(num_boxes_kept, max_detections);
selected->clear();
TfLiteTensor* active_candidate =
&context->tensors[op_data->active_candidate_index];
TF_LITE_ENSURE(context, (active_candidate->dims->data[0]) == num_boxes);
int num_active_candidate = num_boxes_kept;
uint8_t* active_box_candidate = (active_candidate->data.uint8);
for (int row = 0; row < num_boxes_kept; row++) {
active_box_candidate[row] = 1;
}
for (int i = 0; i < num_boxes_kept; ++i) {
if (num_active_candidate == 0 || selected->size() >= output_size) break;
if (active_box_candidate[i] == 1) {
selected->push_back(keep_indices[sorted_indices[i]]);
active_box_candidate[i] = 0;
num_active_candidate--;
} else {
continue;
}
for (int j = i + 1; j < num_boxes_kept; ++j) {
if (active_box_candidate[j] == 1) {
float intersection_over_union = ComputeIntersectionOverUnion(
decoded_boxes, keep_indices[sorted_indices[i]],
keep_indices[sorted_indices[j]]);
if (intersection_over_union > intersection_over_union_threshold) {
active_box_candidate[j] = 0;
num_active_candidate--;
}
}
}
}
return kTfLiteOk;
} | 454 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::custom::detection_postprocess::NonMaxSuppressionSingleClassHelper | tflite::ops::custom::detection_postprocess::NonMaxSuppressionSingleClassHelper( TfLiteContext * context , TfLiteNode * node , OpData * op_data , const std :: vector<float> & scores , std :: vector<int> * selected , int max_detections) | ['context', 'node', 'op_data', 'scores', 'selected', 'max_detections'] | TfLiteStatus NonMaxSuppressionSingleClassHelper(
TfLiteContext* context, TfLiteNode* node, OpData* op_data,
const std::vector<float>& scores, std::vector<int>* selected,
int max_detections) {
const TfLiteTensor* input_box_encodings =
GetInput(context, node, kInputTensorBoxEncodings);
const TfLiteTensor* decoded_boxes =
&context->tensors[op_data->decoded_boxes_index];
const int num_boxes = input_box_encodings->dims->data[1];
const float non_max_suppression_score_threshold =
op_data->non_max_suppression_score_threshold;
const float intersection_over_union_threshold =
op_data->intersection_over_union_threshold;
// Maximum detections should be positive.
TF_LITE_ENSURE(context, (max_detections >= 0));
// intersection_over_union_threshold should be positive
// and should be less than 1.
TF_LITE_ENSURE(context, (intersection_over_union_threshold > 0.0f) &&
(intersection_over_union_threshold <= 1.0f));
// Validate boxes
TF_LITE_ENSURE(context, ValidateBoxes(decoded_boxes, num_boxes));
// threshold scores
std::vector<int> keep_indices;
// TODO (chowdhery): Remove the dynamic allocation and replace it
// with temporaries, esp for std::vector<float>
std::vector<float> keep_scores;
SelectDetectionsAboveScoreThreshold(
scores, non_max_suppression_score_threshold, &keep_scores, &keep_indices);
int num_scores_kept = keep_scores.size();
std::vector<int> sorted_indices;
sorted_indices.resize(num_scores_kept);
DecreasingPartialArgSort(keep_scores.data(), num_scores_kept, num_scores_kept,
sorted_indices.data());
const int num_boxes_kept = num_scores_kept;
const int output_size = std::min(num_boxes_kept, max_detections);
selected->clear();
TfLiteTensor* active_candidate =
&context->tensors[op_data->active_candidate_index];
TF_LITE_ENSURE(context, (active_candidate->dims->data[0]) == num_boxes);
int num_active_candidate = num_boxes_kept;
uint8_t* active_box_candidate = (active_candidate->data.uint8);
for (int row = 0; row < num_boxes_kept; row++) {
active_box_candidate[row] = 1;
}
for (int i = 0; i < num_boxes_kept; ++i) {
if (num_active_candidate == 0 || selected->size() >= output_size) break;
if (active_box_candidate[i] == 1) {
selected->push_back(keep_indices[sorted_indices[i]]);
active_box_candidate[i] = 0;
num_active_candidate--;
} else {
continue;
}
for (int j = i + 1; j < num_boxes_kept; ++j) {
if (active_box_candidate[j] == 1) {
float intersection_over_union = ComputeIntersectionOverUnion(
decoded_boxes, keep_indices[sorted_indices[i]],
keep_indices[sorted_indices[j]]);
if (intersection_over_union > intersection_over_union_threshold) {
active_box_candidate[j] = 0;
num_active_candidate--;
}
}
}
}
return kTfLiteOk;
} | 454 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::custom::detection_postprocess::Prepare | tflite::ops::custom::detection_postprocess::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* op_data = static_cast<OpData*>(node->user_data);
// Inputs: box_encodings, scores, anchors
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
const TfLiteTensor* input_box_encodings =
GetInput(context, node, kInputTensorBoxEncodings);
const TfLiteTensor* input_class_predictions =
GetInput(context, node, kInputTensorClassPredictions);
const TfLiteTensor* input_anchors =
GetInput(context, node, kInputTensorAnchors);
TF_LITE_ENSURE_EQ(context, NumDimensions(input_box_encodings), 3);
TF_LITE_ENSURE_EQ(context, NumDimensions(input_class_predictions), 3);
TF_LITE_ENSURE_EQ(context, NumDimensions(input_anchors), 2);
// number of detected boxes
const int num_detected_boxes =
op_data->max_detections * op_data->max_classes_per_detection;
// Outputs: detection_boxes, detection_scores, detection_classes,
// num_detections
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 4);
// Output Tensor detection_boxes: size is set to (1, num_detected_boxes, 4)
TfLiteTensor* detection_boxes =
GetOutput(context, node, kOutputTensorDetectionBoxes);
detection_boxes->type = kTfLiteFloat32;
SetTensorSizes(context, detection_boxes,
{kBatchSize, num_detected_boxes, kNumCoordBox});
// Output Tensor detection_classes: size is set to (1, num_detected_boxes)
TfLiteTensor* detection_classes =
GetOutput(context, node, kOutputTensorDetectionClasses);
detection_classes->type = kTfLiteFloat32;
SetTensorSizes(context, detection_classes, {kBatchSize, num_detected_boxes});
// Output Tensor detection_scores: size is set to (1, num_detected_boxes)
TfLiteTensor* detection_scores =
GetOutput(context, node, kOutputTensorDetectionScores);
detection_scores->type = kTfLiteFloat32;
SetTensorSizes(context, detection_scores, {kBatchSize, num_detected_boxes});
// Output Tensor num_detections: size is set to 1
TfLiteTensor* num_detections =
GetOutput(context, node, kOutputTensorNumDetections);
num_detections->type = kTfLiteFloat32;
// TODO (chowdhery): Make it a scalar when available
SetTensorSizes(context, num_detections, {1});
// Temporary tensors
TfLiteIntArrayFree(node->temporaries);
node->temporaries = TfLiteIntArrayCreate(3);
node->temporaries->data[0] = op_data->decoded_boxes_index;
node->temporaries->data[1] = op_data->scores_index;
node->temporaries->data[2] = op_data->active_candidate_index;
// decoded_boxes
TfLiteTensor* decoded_boxes = &context->tensors[op_data->decoded_boxes_index];
decoded_boxes->type = kTfLiteFloat32;
decoded_boxes->allocation_type = kTfLiteArenaRw;
SetTensorSizes(context, decoded_boxes,
{input_box_encodings->dims->data[1], kNumCoordBox});
// scores
TfLiteTensor* scores = &context->tensors[op_data->scores_index];
scores->type = kTfLiteFloat32;
scores->allocation_type = kTfLiteArenaRw;
SetTensorSizes(context, scores,
{input_class_predictions->dims->data[1],
input_class_predictions->dims->data[2]});
// active_candidate
TfLiteTensor* active_candidate =
&context->tensors[op_data->active_candidate_index];
active_candidate->type = kTfLiteUInt8;
active_candidate->allocation_type = kTfLiteArenaRw;
SetTensorSizes(context, active_candidate,
{input_box_encodings->dims->data[1]});
return kTfLiteOk;
} | 470 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::custom::detection_postprocess::Prepare | tflite::ops::custom::detection_postprocess::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* op_data = static_cast<OpData*>(node->user_data);
// Inputs: box_encodings, scores, anchors
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
const TfLiteTensor* input_box_encodings =
GetInput(context, node, kInputTensorBoxEncodings);
const TfLiteTensor* input_class_predictions =
GetInput(context, node, kInputTensorClassPredictions);
const TfLiteTensor* input_anchors =
GetInput(context, node, kInputTensorAnchors);
TF_LITE_ENSURE_EQ(context, NumDimensions(input_box_encodings), 3);
TF_LITE_ENSURE_EQ(context, NumDimensions(input_class_predictions), 3);
TF_LITE_ENSURE_EQ(context, NumDimensions(input_anchors), 2);
// number of detected boxes
const int num_detected_boxes =
op_data->max_detections * op_data->max_classes_per_detection;
// Outputs: detection_boxes, detection_scores, detection_classes,
// num_detections
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 4);
// Output Tensor detection_boxes: size is set to (1, num_detected_boxes, 4)
TfLiteTensor* detection_boxes =
GetOutput(context, node, kOutputTensorDetectionBoxes);
detection_boxes->type = kTfLiteFloat32;
SetTensorSizes(context, detection_boxes,
{kBatchSize, num_detected_boxes, kNumCoordBox});
// Output Tensor detection_classes: size is set to (1, num_detected_boxes)
TfLiteTensor* detection_classes =
GetOutput(context, node, kOutputTensorDetectionClasses);
detection_classes->type = kTfLiteFloat32;
SetTensorSizes(context, detection_classes, {kBatchSize, num_detected_boxes});
// Output Tensor detection_scores: size is set to (1, num_detected_boxes)
TfLiteTensor* detection_scores =
GetOutput(context, node, kOutputTensorDetectionScores);
detection_scores->type = kTfLiteFloat32;
SetTensorSizes(context, detection_scores, {kBatchSize, num_detected_boxes});
// Output Tensor num_detections: size is set to 1
TfLiteTensor* num_detections =
GetOutput(context, node, kOutputTensorNumDetections);
num_detections->type = kTfLiteFloat32;
// TODO (chowdhery): Make it a scalar when available
SetTensorSizes(context, num_detections, {1});
// Temporary tensors
TfLiteIntArrayFree(node->temporaries);
node->temporaries = TfLiteIntArrayCreate(3);
node->temporaries->data[0] = op_data->decoded_boxes_index;
node->temporaries->data[1] = op_data->scores_index;
node->temporaries->data[2] = op_data->active_candidate_index;
// decoded_boxes
TfLiteTensor* decoded_boxes = &context->tensors[op_data->decoded_boxes_index];
decoded_boxes->type = kTfLiteFloat32;
decoded_boxes->allocation_type = kTfLiteArenaRw;
SetTensorSizes(context, decoded_boxes,
{input_box_encodings->dims->data[1], kNumCoordBox});
// scores
TfLiteTensor* scores = &context->tensors[op_data->scores_index];
scores->type = kTfLiteFloat32;
scores->allocation_type = kTfLiteArenaRw;
SetTensorSizes(context, scores,
{input_class_predictions->dims->data[1],
input_class_predictions->dims->data[2]});
// active_candidate
TfLiteTensor* active_candidate =
&context->tensors[op_data->active_candidate_index];
active_candidate->type = kTfLiteUInt8;
active_candidate->allocation_type = kTfLiteArenaRw;
SetTensorSizes(context, active_candidate,
{input_box_encodings->dims->data[1]});
return kTfLiteOk;
} | 470 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::div::Eval | tflite::ops::builtin::div::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteDivParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (output->type == kTfLiteFloat32 || output->type == kTfLiteInt32) {
EvalDiv<kernel_type>(context, node, params, data, input1, input2, output);
} else if (output->type == kTfLiteUInt8) {
TF_LITE_ENSURE_OK(
context, EvalQuantized<kernel_type>(context, node, params, data, input1,
input2, output));
} else {
context->ReportError(
context,
"Div only supports FLOAT32, INT32 and quantized UINT8 now, got %d.",
output->type);
return kTfLiteError;
}
return kTfLiteOk;
} | 177 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::div::Eval | tflite::ops::builtin::div::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteDivParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (output->type == kTfLiteFloat32 || output->type == kTfLiteInt32) {
EvalDiv<kernel_type>(context, node, params, data, input1, input2, output);
} else if (output->type == kTfLiteUInt8) {
TF_LITE_ENSURE_OK(
context, EvalQuantized<kernel_type>(context, node, params, data, input1,
input2, output));
} else {
context->ReportError(
context,
"Div only supports FLOAT32, INT32 and quantized UINT8 now, got %d.",
output->type);
return kTfLiteError;
}
return kTfLiteOk;
} | 177 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::div::Prepare | tflite::ops::builtin::div::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteDivParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
output->type = input2->type;
data->requires_broadcast = !HaveSameShapes(input1, input2);
TfLiteIntArray* output_size = nullptr;
if (data->requires_broadcast) {
TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
context, input1, input2, &output_size));
} else {
output_size = TfLiteIntArrayCopy(input1->dims);
}
if (output->type == kTfLiteUInt8) {
TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
context, params->activation, output, &data->output_activation_min,
&data->output_activation_max));
const double real_multiplier =
input1->params.scale / (input2->params.scale * output->params.scale);
QuantizeMultiplier(real_multiplier, &data->output_multiplier,
&data->output_shift);
}
return context->ResizeTensor(context, output, output_size);
} | 268 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::div::Prepare | tflite::ops::builtin::div::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteDivParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
output->type = input2->type;
data->requires_broadcast = !HaveSameShapes(input1, input2);
TfLiteIntArray* output_size = nullptr;
if (data->requires_broadcast) {
TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
context, input1, input2, &output_size));
} else {
output_size = TfLiteIntArrayCopy(input1->dims);
}
if (output->type == kTfLiteUInt8) {
TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
context, params->activation, output, &data->output_activation_min,
&data->output_activation_max));
const double real_multiplier =
input1->params.scale / (input2->params.scale * output->params.scale);
QuantizeMultiplier(real_multiplier, &data->output_multiplier,
&data->output_shift);
}
return context->ResizeTensor(context, output, output_size);
} | 268 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::elementwise::EvalImpl | tflite::ops::builtin::elementwise::EvalImpl( TfLiteContext * context , TfLiteNode * node , std :: function<T(T)> func , TfLiteType expected_type) | ['context', 'node', 'func', 'expected_type'] | inline TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node,
std::function<T(T)> func,
TfLiteType expected_type) {
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, expected_type);
const int64_t num_elements = NumElements(input);
const T* in_data = GetTensorData<T>(input);
T* out_data = GetTensorData<T>(output);
for (int64_t i = 0; i < num_elements; ++i) {
out_data[i] = func(in_data[i]);
}
return kTfLiteOk;
} | 130 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::elementwise::EvalImpl | tflite::ops::builtin::elementwise::EvalImpl( TfLiteContext * context , TfLiteNode * node , std :: function<T(T)> func , TfLiteType expected_type) | ['context', 'node', 'func', 'expected_type'] | inline TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node,
std::function<T(T)> func,
TfLiteType expected_type) {
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, expected_type);
const int64_t num_elements = NumElements(input);
const T* in_data = GetTensorData<T>(input);
T* out_data = GetTensorData<T>(output);
for (int64_t i = 0; i < num_elements; ++i) {
out_data[i] = func(in_data[i]);
}
return kTfLiteOk;
} | 130 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::elementwise::GenericPrepare | tflite::ops::builtin::elementwise::GenericPrepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
if (!is_supported_type(input->type)) {
TF_LITE_UNSUPPORTED_TYPE(context, input->type, op_name);
}
return context->ResizeTensor(context, output,
TfLiteIntArrayCopy(input->dims));
} | 116 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::elementwise::GenericPrepare | tflite::ops::builtin::elementwise::GenericPrepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
if (!is_supported_type(input->type)) {
TF_LITE_UNSUPPORTED_TYPE(context, input->type, op_name);
}
return context->ResizeTensor(context, output,
TfLiteIntArrayCopy(input->dims));
} | 116 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::embedding_lookup::Eval | tflite::ops::builtin::embedding_lookup::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* lookup = GetInput(context, node, 0);
const TfLiteTensor* value = GetInput(context, node, 1);
TfLiteTensor* output = GetOutput(context, node, 0);
switch (value->type) {
case kTfLiteFloat32:
return EvalSimple(context, node, lookup, value, output);
case kTfLiteUInt8:
case kTfLiteInt8:
if (output->type == kTfLiteFloat32) {
return EvalHybrid(context, node, lookup, value, output);
} else {
return EvalSimple(context, node, lookup, value, output);
}
default:
context->ReportError(context, "Type not currently supported.");
return kTfLiteError;
}
} | 139 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::embedding_lookup::Eval | tflite::ops::builtin::embedding_lookup::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* lookup = GetInput(context, node, 0);
const TfLiteTensor* value = GetInput(context, node, 1);
TfLiteTensor* output = GetOutput(context, node, 0);
switch (value->type) {
case kTfLiteFloat32:
return EvalSimple(context, node, lookup, value, output);
case kTfLiteUInt8:
case kTfLiteInt8:
if (output->type == kTfLiteFloat32) {
return EvalHybrid(context, node, lookup, value, output);
} else {
return EvalSimple(context, node, lookup, value, output);
}
default:
context->ReportError(context, "Type not currently supported.");
return kTfLiteError;
}
} | 139 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::embedding_lookup::Prepare | tflite::ops::builtin::embedding_lookup::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* lookup = GetInput(context, node, 0);
TF_LITE_ENSURE_EQ(context, NumDimensions(lookup), 1);
TF_LITE_ENSURE_EQ(context, lookup->type, kTfLiteInt32);
const TfLiteTensor* value = GetInput(context, node, 1);
TF_LITE_ENSURE(context, NumDimensions(value) >= 2);
TfLiteTensor* output = GetOutput(context, node, 0);
TfLiteIntArray* outputSize = TfLiteIntArrayCreate(NumDimensions(value));
outputSize->data[0] = SizeOfDimension(lookup, 0);
outputSize->data[1] = SizeOfDimension(value, 1);
for (int i = 2; i < NumDimensions(value); i++) {
outputSize->data[i] = SizeOfDimension(value, i);
}
return context->ResizeTensor(context, output, outputSize);
} | 197 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::embedding_lookup::Prepare | tflite::ops::builtin::embedding_lookup::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* lookup = GetInput(context, node, 0);
TF_LITE_ENSURE_EQ(context, NumDimensions(lookup), 1);
TF_LITE_ENSURE_EQ(context, lookup->type, kTfLiteInt32);
const TfLiteTensor* value = GetInput(context, node, 1);
TF_LITE_ENSURE(context, NumDimensions(value) >= 2);
TfLiteTensor* output = GetOutput(context, node, 0);
TfLiteIntArray* outputSize = TfLiteIntArrayCreate(NumDimensions(value));
outputSize->data[0] = SizeOfDimension(lookup, 0);
outputSize->data[1] = SizeOfDimension(value, 1);
for (int i = 2; i < NumDimensions(value); i++) {
outputSize->data[i] = SizeOfDimension(value, i);
}
return context->ResizeTensor(context, output, outputSize);
} | 197 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::Eval | tflite::ops::builtin::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteEmbeddingLookupSparseParams*>(node->builtin_data);
TfLiteTensor* output = GetOutput(context, node, 0);
const TfLiteTensor* ids = GetInput(context, node, 0);
const TfLiteTensor* indices = GetInput(context, node, 1);
const TfLiteTensor* dense_shape = GetInput(context, node, 2);
const TfLiteTensor* weights = GetInput(context, node, 3);
const TfLiteTensor* value = GetInput(context, node, 4);
const int lookup_rank = SizeOfDimension(indices, 1);
const int embedding_rank = NumDimensions(value);
const int num_lookups = SizeOfDimension(ids, 0);
const int num_rows = SizeOfDimension(value, 0);
// The last dimension gets replaced by the embedding.
const int output_rank = (lookup_rank - 1) + (embedding_rank - 1);
// Make sure that the actual dense shape of the sparse tensor represented by
// (loopkup, indices, dense_shape) is consistent.
TF_LITE_ENSURE_EQ(context, SizeOfDimension(dense_shape, 0), lookup_rank);
// Resize output tensor.
TfLiteIntArray* output_shape = TfLiteIntArrayCreate(output_rank);
int k = 0;
int embedding_size = 1;
int lookup_size = 1;
for (int i = 0; i < lookup_rank - 1; i++, k++) {
const int dim = dense_shape->data.i32[i];
lookup_size *= dim;
output_shape->data[k] = dim;
}
for (int i = 1; i < embedding_rank; i++, k++) {
const int dim = SizeOfDimension(value, i);
embedding_size *= dim;
output_shape->data[k] = dim;
}
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_shape));
const int output_size = lookup_size * embedding_size;
TfLiteTensorRealloc(output_size * sizeof(float), output);
float* output_ptr = GetTensorData<float>(output);
const float* weights_ptr = GetTensorData<float>(weights);
const float* value_ptr = GetTensorData<float>(value);
std::fill_n(output_ptr, output_size, 0.0f);
// Keep track of the current bucket for aggregation/combination.
int current_output_offset = 0;
float current_total_weight = 0.0;
float current_squares_weight = 0.0;
int num_elements = 0;
for (int i = 0; i < num_lookups; i++) {
int idx = ids->data.i32[i];
if (idx >= num_rows || idx < 0) {
context->ReportError(context,
"Embedding Lookup Sparse: index out of bounds. "
"Got %d, and bounds are [0, %d]",
idx, num_rows - 1);
return kTfLiteError;
}
// Check where we need to aggregate.
const int example_indices_offset = i * lookup_rank;
int output_bucket = 0;
int stride = 1;
for (int k = (lookup_rank - 1) - 1; k >= 0; k--) {
output_bucket += indices->data.i32[example_indices_offset + k] * stride;
stride *= dense_shape->data.i32[k];
}
const int output_offset = output_bucket * embedding_size;
// If we are in a new aggregation bucket and the combiner is not the sum,
// go back and finalize the result of the previous bucket.
if (output_offset != current_output_offset) {
FinalizeAggregation(params->combiner, num_elements, current_total_weight,
current_squares_weight, embedding_size,
&output_ptr[current_output_offset]);
// Track next bucket.
num_elements = 0;
current_total_weight = 0.0;
current_squares_weight = 0.0;
current_output_offset = output_offset;
}
// Add element to aggregation.
++num_elements;
const int example_embedding_offset = idx * embedding_size;
const float w = weights_ptr[i];
current_squares_weight += w * w;
current_total_weight += w;
for (int k = 0; k < embedding_size; k++) {
output_ptr[current_output_offset + k] +=
value_ptr[example_embedding_offset + k] * w;
}
}
// Finalize last bucket.
FinalizeAggregation(params->combiner, num_elements, current_total_weight,
current_squares_weight, embedding_size,
&GetTensorData<float>(output)[current_output_offset]);
return kTfLiteOk;
} | 682 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::Eval | tflite::ops::builtin::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteEmbeddingLookupSparseParams*>(node->builtin_data);
TfLiteTensor* output = GetOutput(context, node, 0);
const TfLiteTensor* ids = GetInput(context, node, 0);
const TfLiteTensor* indices = GetInput(context, node, 1);
const TfLiteTensor* dense_shape = GetInput(context, node, 2);
const TfLiteTensor* weights = GetInput(context, node, 3);
const TfLiteTensor* value = GetInput(context, node, 4);
const int lookup_rank = SizeOfDimension(indices, 1);
const int embedding_rank = NumDimensions(value);
const int num_lookups = SizeOfDimension(ids, 0);
const int num_rows = SizeOfDimension(value, 0);
// The last dimension gets replaced by the embedding.
const int output_rank = (lookup_rank - 1) + (embedding_rank - 1);
// Make sure that the actual dense shape of the sparse tensor represented by
// (loopkup, indices, dense_shape) is consistent.
TF_LITE_ENSURE_EQ(context, SizeOfDimension(dense_shape, 0), lookup_rank);
// Resize output tensor.
TfLiteIntArray* output_shape = TfLiteIntArrayCreate(output_rank);
int k = 0;
int embedding_size = 1;
int lookup_size = 1;
for (int i = 0; i < lookup_rank - 1; i++, k++) {
const int dim = dense_shape->data.i32[i];
lookup_size *= dim;
output_shape->data[k] = dim;
}
for (int i = 1; i < embedding_rank; i++, k++) {
const int dim = SizeOfDimension(value, i);
embedding_size *= dim;
output_shape->data[k] = dim;
}
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_shape));
const int output_size = lookup_size * embedding_size;
TfLiteTensorRealloc(output_size * sizeof(float), output);
float* output_ptr = GetTensorData<float>(output);
const float* weights_ptr = GetTensorData<float>(weights);
const float* value_ptr = GetTensorData<float>(value);
std::fill_n(output_ptr, output_size, 0.0f);
// Keep track of the current bucket for aggregation/combination.
int current_output_offset = 0;
float current_total_weight = 0.0;
float current_squares_weight = 0.0;
int num_elements = 0;
for (int i = 0; i < num_lookups; i++) {
int idx = ids->data.i32[i];
if (idx >= num_rows || idx < 0) {
context->ReportError(context,
"Embedding Lookup Sparse: index out of bounds. "
"Got %d, and bounds are [0, %d]",
idx, num_rows - 1);
return kTfLiteError;
}
// Check where we need to aggregate.
const int example_indices_offset = i * lookup_rank;
int output_bucket = 0;
int stride = 1;
for (int k = (lookup_rank - 1) - 1; k >= 0; k--) {
output_bucket += indices->data.i32[example_indices_offset + k] * stride;
stride *= dense_shape->data.i32[k];
}
const int output_offset = output_bucket * embedding_size;
// If we are in a new aggregation bucket and the combiner is not the sum,
// go back and finalize the result of the previous bucket.
if (output_offset != current_output_offset) {
FinalizeAggregation(params->combiner, num_elements, current_total_weight,
current_squares_weight, embedding_size,
&output_ptr[current_output_offset]);
// Track next bucket.
num_elements = 0;
current_total_weight = 0.0;
current_squares_weight = 0.0;
current_output_offset = output_offset;
}
// Add element to aggregation.
++num_elements;
const int example_embedding_offset = idx * embedding_size;
const float w = weights_ptr[i];
current_squares_weight += w * w;
current_total_weight += w;
for (int k = 0; k < embedding_size; k++) {
output_ptr[current_output_offset + k] +=
value_ptr[example_embedding_offset + k] * w;
}
}
// Finalize last bucket.
FinalizeAggregation(params->combiner, num_elements, current_total_weight,
current_squares_weight, embedding_size,
&GetTensorData<float>(output)[current_output_offset]);
return kTfLiteOk;
} | 682 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::Prepare | tflite::ops::builtin::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 5);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* ids = GetInput(context, node, 0);
TF_LITE_ENSURE_EQ(context, NumDimensions(ids), 1);
TF_LITE_ENSURE_EQ(context, ids->type, kTfLiteInt32);
const TfLiteTensor* indices = GetInput(context, node, 1);
TF_LITE_ENSURE_EQ(context, NumDimensions(indices), 2);
TF_LITE_ENSURE_EQ(context, indices->type, kTfLiteInt32);
const TfLiteTensor* shape = GetInput(context, node, 2);
TF_LITE_ENSURE_EQ(context, NumDimensions(shape), 1);
TF_LITE_ENSURE_EQ(context, shape->type, kTfLiteInt32);
const TfLiteTensor* weights = GetInput(context, node, 3);
TF_LITE_ENSURE_EQ(context, NumDimensions(weights), 1);
TF_LITE_ENSURE_EQ(context, weights->type, kTfLiteFloat32);
TF_LITE_ENSURE_EQ(context, SizeOfDimension(indices, 0),
SizeOfDimension(ids, 0));
TF_LITE_ENSURE_EQ(context, SizeOfDimension(indices, 0),
SizeOfDimension(weights, 0));
const TfLiteTensor* value = GetInput(context, node, 4);
TF_LITE_ENSURE(context, NumDimensions(value) >= 2);
// Mark the output as a dynamic tensor.
TfLiteTensor* output = GetOutput(context, node, 0);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteFloat32);
output->allocation_type = kTfLiteDynamic;
return kTfLiteOk;
} | 281 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::Prepare | tflite::ops::builtin::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 5);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* ids = GetInput(context, node, 0);
TF_LITE_ENSURE_EQ(context, NumDimensions(ids), 1);
TF_LITE_ENSURE_EQ(context, ids->type, kTfLiteInt32);
const TfLiteTensor* indices = GetInput(context, node, 1);
TF_LITE_ENSURE_EQ(context, NumDimensions(indices), 2);
TF_LITE_ENSURE_EQ(context, indices->type, kTfLiteInt32);
const TfLiteTensor* shape = GetInput(context, node, 2);
TF_LITE_ENSURE_EQ(context, NumDimensions(shape), 1);
TF_LITE_ENSURE_EQ(context, shape->type, kTfLiteInt32);
const TfLiteTensor* weights = GetInput(context, node, 3);
TF_LITE_ENSURE_EQ(context, NumDimensions(weights), 1);
TF_LITE_ENSURE_EQ(context, weights->type, kTfLiteFloat32);
TF_LITE_ENSURE_EQ(context, SizeOfDimension(indices, 0),
SizeOfDimension(ids, 0));
TF_LITE_ENSURE_EQ(context, SizeOfDimension(indices, 0),
SizeOfDimension(weights, 0));
const TfLiteTensor* value = GetInput(context, node, 4);
TF_LITE_ENSURE(context, NumDimensions(value) >= 2);
// Mark the output as a dynamic tensor.
TfLiteTensor* output = GetOutput(context, node, 0);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteFloat32);
output->allocation_type = kTfLiteDynamic;
return kTfLiteOk;
} | 281 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::expand_dims::Eval | tflite::ops::builtin::expand_dims::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
// Just copy input to output.
const TfLiteTensor* input = GetInput(context, node, kInput);
TfLiteTensor* output = GetOutput(context, node, 0);
const TfLiteTensor* axis = GetInput(context, node, kAxis);
if (IsDynamicTensor(output)) {
int axis_value;
TF_LITE_ENSURE_OK(context,
GetAxisValueFromTensor(context, *axis, &axis_value));
TF_LITE_ENSURE_OK(context,
ExpandTensorDim(context, *input, axis_value, output));
}
if (output->type == kTfLiteString) {
TfLiteTensorRealloc(input->bytes, output);
}
memcpy(output->data.raw, input->data.raw, input->bytes);
return kTfLiteOk;
} | 139 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::expand_dims::Eval | tflite::ops::builtin::expand_dims::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
// Just copy input to output.
const TfLiteTensor* input = GetInput(context, node, kInput);
TfLiteTensor* output = GetOutput(context, node, 0);
const TfLiteTensor* axis = GetInput(context, node, kAxis);
if (IsDynamicTensor(output)) {
int axis_value;
TF_LITE_ENSURE_OK(context,
GetAxisValueFromTensor(context, *axis, &axis_value));
TF_LITE_ENSURE_OK(context,
ExpandTensorDim(context, *input, axis_value, output));
}
if (output->type == kTfLiteString) {
TfLiteTensorRealloc(input->bytes, output);
}
memcpy(output->data.raw, input->data.raw, input->bytes);
return kTfLiteOk;
} | 139 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::expand_dims::Prepare | tflite::ops::builtin::expand_dims::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input = GetInput(context, node, kInput);
const TfLiteTensor* axis = GetInput(context, node, kAxis);
TfLiteTensor* output = GetOutput(context, node, 0);
output->type = input->type;
if (IsConstantTensor(axis)) {
int axis_value;
TF_LITE_ENSURE_OK(context,
GetAxisValueFromTensor(context, *axis, &axis_value));
return ExpandTensorDim(context, *input, axis_value, output);
}
SetTensorToDynamic(output);
return kTfLiteOk;
} | 134 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::expand_dims::Prepare | tflite::ops::builtin::expand_dims::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input = GetInput(context, node, kInput);
const TfLiteTensor* axis = GetInput(context, node, kAxis);
TfLiteTensor* output = GetOutput(context, node, 0);
output->type = input->type;
if (IsConstantTensor(axis)) {
int axis_value;
TF_LITE_ENSURE_OK(context,
GetAxisValueFromTensor(context, *axis, &axis_value));
return ExpandTensorDim(context, *input, axis_value, output);
}
SetTensorToDynamic(output);
return kTfLiteOk;
} | 134 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::fill::Eval | tflite::ops::builtin::fill::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* value = GetInput(context, node, kValueTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (IsDynamicTensor(output)) {
const TfLiteTensor* dims = GetInput(context, node, kDimsTensor);
TF_LITE_ENSURE_OK(context, ResizeOutput(context, dims, output));
}
#define TF_LITE_FILL(data_type) \
reference_ops::Fill(GetTensorShape(value), GetTensorData<data_type>(value), \
GetTensorShape(output), \
GetTensorData<data_type>(output))
switch (output->type) {
case kTfLiteInt32:
TF_LITE_FILL(int32_t);
break;
case kTfLiteInt64:
TF_LITE_FILL(int64_t);
break;
case kTfLiteFloat32:
TF_LITE_FILL(float);
break;
case kTfLiteBool:
TF_LITE_FILL(bool);
break;
case kTfLiteString:
FillString(value, output);
break;
default:
context->ReportError(
context,
"Fill only currently supports int32, int64, float32, bool, string "
"for input 1, got %d.",
value->type);
return kTfLiteError;
}
#undef TF_LITE_FILL
return kTfLiteOk;
} | 158 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::fill::Eval | tflite::ops::builtin::fill::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* value = GetInput(context, node, kValueTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (IsDynamicTensor(output)) {
const TfLiteTensor* dims = GetInput(context, node, kDimsTensor);
TF_LITE_ENSURE_OK(context, ResizeOutput(context, dims, output));
}
#define TF_LITE_FILL(data_type) \
reference_ops::Fill(GetTensorShape(value), GetTensorData<data_type>(value), \
GetTensorShape(output), \
GetTensorData<data_type>(output))
switch (output->type) {
case kTfLiteInt32:
TF_LITE_FILL(int32_t);
break;
case kTfLiteInt64:
TF_LITE_FILL(int64_t);
break;
case kTfLiteFloat32:
TF_LITE_FILL(float);
break;
case kTfLiteBool:
TF_LITE_FILL(bool);
break;
case kTfLiteString:
FillString(value, output);
break;
default:
context->ReportError(
context,
"Fill only currently supports int32, int64, float32, bool, string "
"for input 1, got %d.",
value->type);
return kTfLiteError;
}
#undef TF_LITE_FILL
return kTfLiteOk;
} | 158 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::fill::Prepare | tflite::ops::builtin::fill::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* dims = GetInput(context, node, kDimsTensor);
const TfLiteTensor* value = GetInput(context, node, kValueTensor);
// Make sure the 1st input tensor is 1-D.
TF_LITE_ENSURE_EQ(context, NumDimensions(dims), 1);
// Make sure the 1st input tensor is int32 or int64.
const auto dtype = dims->type;
TF_LITE_ENSURE(context, dtype == kTfLiteInt32 || dtype == kTfLiteInt64);
// Make sure the 2nd input tensor is a scalar.
TF_LITE_ENSURE_EQ(context, NumDimensions(value), 0);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
output->type = value->type;
if (IsConstantTensor(dims)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, dims, output));
} else {
SetTensorToDynamic(output);
}
return kTfLiteOk;
} | 164 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::fill::Prepare | tflite::ops::builtin::fill::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* dims = GetInput(context, node, kDimsTensor);
const TfLiteTensor* value = GetInput(context, node, kValueTensor);
// Make sure the 1st input tensor is 1-D.
TF_LITE_ENSURE_EQ(context, NumDimensions(dims), 1);
// Make sure the 1st input tensor is int32 or int64.
const auto dtype = dims->type;
TF_LITE_ENSURE(context, dtype == kTfLiteInt32 || dtype == kTfLiteInt64);
// Make sure the 2nd input tensor is a scalar.
TF_LITE_ENSURE_EQ(context, NumDimensions(value), 0);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
output->type = value->type;
if (IsConstantTensor(dims)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, dims, output));
} else {
SetTensorToDynamic(output);
}
return kTfLiteOk;
} | 164 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::floor::Eval | tflite::ops::builtin::floor::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (type == kGenericOptimized) {
optimized_ops::Floor(GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output), GetTensorData<float>(output));
} else {
reference_ops::Floor(GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output), GetTensorData<float>(output));
}
return kTfLiteOk;
} | 115 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::floor::Eval | tflite::ops::builtin::floor::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (type == kGenericOptimized) {
optimized_ops::Floor(GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output), GetTensorData<float>(output));
} else {
reference_ops::Floor(GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output), GetTensorData<float>(output));
}
return kTfLiteOk;
} | 115 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::floor::Prepare | tflite::ops::builtin::floor::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32);
output->type = input->type;
TfLiteIntArray* output_size = TfLiteIntArrayCopy(input->dims);
return context->ResizeTensor(context, output, output_size);
} | 105 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::floor::Prepare | tflite::ops::builtin::floor::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32);
output->type = input->type;
TfLiteIntArray* output_size = TfLiteIntArrayCopy(input->dims);
return context->ResizeTensor(context, output, output_size);
} | 105 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::floor_div::Eval | tflite::ops::builtin::floor_div::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
switch (input1->type) {
case kTfLiteInt32: {
return EvalImpl<int32_t>(context, data->requires_broadcast, input1,
input2, output);
}
case kTfLiteFloat32: {
return EvalImpl<float>(context, data->requires_broadcast, input1, input2,
output);
}
default: {
context->ReportError(context, "Type '%s' is not supported by floor_div.",
TfLiteTypeGetName(input1->type));
return kTfLiteError;
}
}
} | 147 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::floor_div::Eval | tflite::ops::builtin::floor_div::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
switch (input1->type) {
case kTfLiteInt32: {
return EvalImpl<int32_t>(context, data->requires_broadcast, input1,
input2, output);
}
case kTfLiteFloat32: {
return EvalImpl<float>(context, data->requires_broadcast, input1, input2,
output);
}
default: {
context->ReportError(context, "Type '%s' is not supported by floor_div.",
TfLiteTypeGetName(input1->type));
return kTfLiteError;
}
}
} | 147 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::floor_div::Prepare | tflite::ops::builtin::floor_div::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
// Reinterprete the opaque data provided by user.
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
const TfLiteType type = input1->type;
switch (type) {
case kTfLiteFloat32:
case kTfLiteInt32:
break;
default:
context->ReportError(context, "Type '%s' is not supported by floor_div.",
TfLiteTypeGetName(type));
return kTfLiteError;
}
output->type = type;
data->requires_broadcast = !HaveSameShapes(input1, input2);
TfLiteIntArray* output_size = nullptr;
if (data->requires_broadcast) {
TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
context, input1, input2, &output_size));
} else {
output_size = TfLiteIntArrayCopy(input1->dims);
}
return context->ResizeTensor(context, output, output_size);
} | 219 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::floor_div::Prepare | tflite::ops::builtin::floor_div::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
// Reinterprete the opaque data provided by user.
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
const TfLiteType type = input1->type;
switch (type) {
case kTfLiteFloat32:
case kTfLiteInt32:
break;
default:
context->ReportError(context, "Type '%s' is not supported by floor_div.",
TfLiteTypeGetName(type));
return kTfLiteError;
}
output->type = type;
data->requires_broadcast = !HaveSameShapes(input1, input2);
TfLiteIntArray* output_size = nullptr;
if (data->requires_broadcast) {
TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
context, input1, input2, &output_size));
} else {
output_size = TfLiteIntArrayCopy(input1->dims);
}
return context->ResizeTensor(context, output, output_size);
} | 219 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::floor_mod::Eval | tflite::ops::builtin::floor_mod::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
switch (input1->type) {
case kTfLiteInt32: {
return EvalImpl<int32_t>(context, data->requires_broadcast, input1,
input2, output);
}
case kTfLiteInt64: {
return EvalImpl<int64_t>(context, data->requires_broadcast, input1,
input2, output);
}
case kTfLiteFloat32: {
return EvalImpl<float>(context, data->requires_broadcast, input1, input2,
output);
}
default: {
context->ReportError(context, "Type '%s' is not supported by floor_mod.",
TfLiteTypeGetName(input1->type));
return kTfLiteError;
}
}
} | 171 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::floor_mod::Eval | tflite::ops::builtin::floor_mod::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
switch (input1->type) {
case kTfLiteInt32: {
return EvalImpl<int32_t>(context, data->requires_broadcast, input1,
input2, output);
}
case kTfLiteInt64: {
return EvalImpl<int64_t>(context, data->requires_broadcast, input1,
input2, output);
}
case kTfLiteFloat32: {
return EvalImpl<float>(context, data->requires_broadcast, input1, input2,
output);
}
default: {
context->ReportError(context, "Type '%s' is not supported by floor_mod.",
TfLiteTypeGetName(input1->type));
return kTfLiteError;
}
}
} | 171 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::floor_mod::Prepare | tflite::ops::builtin::floor_mod::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
// Reinterprete the opaque data provided by user.
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
const TfLiteType type = input1->type;
if (type != kTfLiteInt32 && type != kTfLiteFloat32 && type != kTfLiteInt64) {
context->ReportError(context, "Type '%s' is not supported by floor_mod.",
TfLiteTypeGetName(type));
return kTfLiteError;
}
output->type = type;
data->requires_broadcast = !HaveSameShapes(input1, input2);
TfLiteIntArray* output_size = nullptr;
if (data->requires_broadcast) {
TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
context, input1, input2, &output_size));
} else {
output_size = TfLiteIntArrayCopy(input1->dims);
}
return context->ResizeTensor(context, output, output_size);
} | 219 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::floor_mod::Prepare | tflite::ops::builtin::floor_mod::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
// Reinterprete the opaque data provided by user.
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
const TfLiteType type = input1->type;
if (type != kTfLiteInt32 && type != kTfLiteFloat32 && type != kTfLiteInt64) {
context->ReportError(context, "Type '%s' is not supported by floor_mod.",
TfLiteTypeGetName(type));
return kTfLiteError;
}
output->type = type;
data->requires_broadcast = !HaveSameShapes(input1, input2);
TfLiteIntArray* output_size = nullptr;
if (data->requires_broadcast) {
TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
context, input1, input2, &output_size));
} else {
output_size = TfLiteIntArrayCopy(input1->dims);
}
return context->ResizeTensor(context, output, output_size);
} | 219 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::fully_connected::Eval | tflite::ops::builtin::fully_connected::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteFullyConnectedParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor);
const TfLiteTensor* bias =
(node->inputs->size == 3)
? GetOptionalInputTensor(context, node, kBiasTensor)
: nullptr;
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
switch (filter->type) {
case kTfLiteFloat32:
return EvalFloat<kernel_type>(context, node, params, data, input, filter,
bias, output);
case kTfLiteUInt8:
if (params->weights_format ==
kTfLiteFullyConnectedWeightsFormatShuffled4x16Int8) {
TfLiteTensor* shuffled_input_workspace =
GetOutput(context, node, kShuffledInputWorkspaceTensor);
return EvalShuffledQuantized<kernel_type>(context, node, params, data,
input, filter, bias, output,
shuffled_input_workspace);
} else if (params->weights_format ==
kTfLiteFullyConnectedWeightsFormatDefault) {
return EvalQuantized<kernel_type>(context, node, params, data, input,
filter, bias, output);
} else {
context->ReportError(context,
"Unhandled fully-connected weights format");
return kTfLiteError;
}
case kTfLiteInt8:
if (params->weights_format == kTfLiteFullyConnectedWeightsFormatDefault) {
return EvalQuantized<kernel_type>(context, node, params, data, input,
filter, bias, output);
} else {
context->ReportError(context,
"Unhandled fully-connected weights format");
return kTfLiteError;
}
default:
context->ReportError(context,
"Filter data type %s currently not supported.",
TfLiteTypeGetName(filter->type));
return kTfLiteError;
}
return kTfLiteOk;
} | 318 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::fully_connected::Eval | tflite::ops::builtin::fully_connected::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteFullyConnectedParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor);
const TfLiteTensor* bias =
(node->inputs->size == 3)
? GetOptionalInputTensor(context, node, kBiasTensor)
: nullptr;
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
switch (filter->type) {
case kTfLiteFloat32:
return EvalFloat<kernel_type>(context, node, params, data, input, filter,
bias, output);
case kTfLiteUInt8:
if (params->weights_format ==
kTfLiteFullyConnectedWeightsFormatShuffled4x16Int8) {
TfLiteTensor* shuffled_input_workspace =
GetOutput(context, node, kShuffledInputWorkspaceTensor);
return EvalShuffledQuantized<kernel_type>(context, node, params, data,
input, filter, bias, output,
shuffled_input_workspace);
} else if (params->weights_format ==
kTfLiteFullyConnectedWeightsFormatDefault) {
return EvalQuantized<kernel_type>(context, node, params, data, input,
filter, bias, output);
} else {
context->ReportError(context,
"Unhandled fully-connected weights format");
return kTfLiteError;
}
case kTfLiteInt8:
if (params->weights_format == kTfLiteFullyConnectedWeightsFormatDefault) {
return EvalQuantized<kernel_type>(context, node, params, data, input,
filter, bias, output);
} else {
context->ReportError(context,
"Unhandled fully-connected weights format");
return kTfLiteError;
}
default:
context->ReportError(context,
"Filter data type %s currently not supported.",
TfLiteTypeGetName(filter->type));
return kTfLiteError;
}
return kTfLiteOk;
} | 318 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::fully_connected::EvalQuantized | tflite::ops::builtin::fully_connected::EvalQuantized( TfLiteContext * context , TfLiteNode * node , TfLiteFullyConnectedParams * params , OpData * data , const TfLiteTensor * input , const TfLiteTensor * filter , const TfLiteTensor * bias , TfLiteTensor * output) | ['context', 'node', 'params', 'data', 'input', 'filter', 'bias', 'output'] | TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
TfLiteFullyConnectedParams* params, OpData* data,
const TfLiteTensor* input,
const TfLiteTensor* filter, const TfLiteTensor* bias,
TfLiteTensor* output) {
int32_t input_offset = -input->params.zero_point;
int32_t filter_offset = -filter->params.zero_point;
int32_t output_offset = output->params.zero_point;
// Only the Pie path supports quantized models and float inputs/outputs.
if (input->type == kTfLiteFloat32) {
TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/0);
TfLiteTensor* scaling_factors = GetTemporary(context, node, /*index=*/1);
TfLiteTensor* accum_scratch = GetTemporary(context, node, /*index=*/2);
TfLiteTensor* input_offsets = GetTemporary(context, node, /*index=*/3);
TfLiteTensor* row_sums = GetTemporary(context, node, /*index=*/4);
return EvalHybrid(context, node, params, data, input, filter, bias,
input_quantized, scaling_factors, accum_scratch, row_sums,
input_offsets, output);
} else {
FullyConnectedParams op_params;
op_params.input_offset = input_offset;
op_params.weights_offset = filter_offset;
op_params.output_offset = output_offset;
op_params.output_multiplier = data->output_multiplier;
op_params.output_shift = data->output_shift;
op_params.quantized_activation_min = data->output_activation_min;
op_params.quantized_activation_max = data->output_activation_max;
op_params.lhs_cacheable = IsConstantTensor(filter);
op_params.rhs_cacheable = IsConstantTensor(input);
switch (output->type) {
case kTfLiteUInt8:
if (kernel_type == kReference) {
reference_ops::FullyConnected(
op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
GetTensorShape(filter), GetTensorData<uint8_t>(filter),
GetTensorShape(bias), GetTensorData<int32_t>(bias),
GetTensorShape(output), GetTensorData<uint8_t>(output));
} else {
optimized_ops::FullyConnected(
op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
GetTensorShape(filter), GetTensorData<uint8_t>(filter),
GetTensorShape(bias), GetTensorData<int32_t>(bias),
GetTensorShape(output), GetTensorData<uint8_t>(output),
CpuBackendContext::GetFromContext(context));
}
break;
case kTfLiteInt8:
FullyConnectedInt8<kernel_type>(
data, input, filter, bias, output,
CpuBackendContext::GetFromContext(context));
break;
case kTfLiteInt16:
if (input->type == kTfLiteInt16) {
FullyConnectedInt16<kernel_type>(data, input, filter, bias, output);
} else if (kernel_type == kReference) {
reference_ops::FullyConnected(
op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
GetTensorShape(filter), GetTensorData<uint8_t>(filter),
GetTensorShape(bias), GetTensorData<int32_t>(bias),
GetTensorShape(output), GetTensorData<int16_t>(output));
} else {
optimized_ops::FullyConnected(
op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
GetTensorShape(filter), GetTensorData<uint8_t>(filter),
GetTensorShape(bias), GetTensorData<int32_t>(bias),
GetTensorShape(output), GetTensorData<int16_t>(output),
CpuBackendContext::GetFromContext(context));
}
break;
default:
context->ReportError(context,
"Quantized FullyConnected expects output data "
"type uint8, int8 or int16");
return kTfLiteError;
}
}
return kTfLiteOk;
} | 610 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::fully_connected::EvalQuantized | tflite::ops::builtin::fully_connected::EvalQuantized( TfLiteContext * context , TfLiteNode * node , TfLiteFullyConnectedParams * params , OpData * data , const TfLiteTensor * input , const TfLiteTensor * filter , const TfLiteTensor * bias , TfLiteTensor * output) | ['context', 'node', 'params', 'data', 'input', 'filter', 'bias', 'output'] | TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
TfLiteFullyConnectedParams* params, OpData* data,
const TfLiteTensor* input,
const TfLiteTensor* filter, const TfLiteTensor* bias,
TfLiteTensor* output) {
int32_t input_offset = -input->params.zero_point;
int32_t filter_offset = -filter->params.zero_point;
int32_t output_offset = output->params.zero_point;
// Only the Pie path supports quantized models and float inputs/outputs.
if (input->type == kTfLiteFloat32) {
TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/0);
TfLiteTensor* scaling_factors = GetTemporary(context, node, /*index=*/1);
TfLiteTensor* accum_scratch = GetTemporary(context, node, /*index=*/2);
TfLiteTensor* input_offsets = GetTemporary(context, node, /*index=*/3);
TfLiteTensor* row_sums = GetTemporary(context, node, /*index=*/4);
return EvalHybrid(context, node, params, data, input, filter, bias,
input_quantized, scaling_factors, accum_scratch, row_sums,
input_offsets, output);
} else {
FullyConnectedParams op_params;
op_params.input_offset = input_offset;
op_params.weights_offset = filter_offset;
op_params.output_offset = output_offset;
op_params.output_multiplier = data->output_multiplier;
op_params.output_shift = data->output_shift;
op_params.quantized_activation_min = data->output_activation_min;
op_params.quantized_activation_max = data->output_activation_max;
op_params.lhs_cacheable = IsConstantTensor(filter);
op_params.rhs_cacheable = IsConstantTensor(input);
switch (output->type) {
case kTfLiteUInt8:
if (kernel_type == kReference) {
reference_ops::FullyConnected(
op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
GetTensorShape(filter), GetTensorData<uint8_t>(filter),
GetTensorShape(bias), GetTensorData<int32_t>(bias),
GetTensorShape(output), GetTensorData<uint8_t>(output));
} else {
optimized_ops::FullyConnected(
op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
GetTensorShape(filter), GetTensorData<uint8_t>(filter),
GetTensorShape(bias), GetTensorData<int32_t>(bias),
GetTensorShape(output), GetTensorData<uint8_t>(output),
CpuBackendContext::GetFromContext(context));
}
break;
case kTfLiteInt8:
FullyConnectedInt8<kernel_type>(
data, input, filter, bias, output,
CpuBackendContext::GetFromContext(context));
break;
case kTfLiteInt16:
if (input->type == kTfLiteInt16) {
FullyConnectedInt16<kernel_type>(data, input, filter, bias, output);
} else if (kernel_type == kReference) {
reference_ops::FullyConnected(
op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
GetTensorShape(filter), GetTensorData<uint8_t>(filter),
GetTensorShape(bias), GetTensorData<int32_t>(bias),
GetTensorShape(output), GetTensorData<int16_t>(output));
} else {
optimized_ops::FullyConnected(
op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
GetTensorShape(filter), GetTensorData<uint8_t>(filter),
GetTensorShape(bias), GetTensorData<int32_t>(bias),
GetTensorShape(output), GetTensorData<int16_t>(output),
CpuBackendContext::GetFromContext(context));
}
break;
default:
context->ReportError(context,
"Quantized FullyConnected expects output data "
"type uint8, int8 or int16");
return kTfLiteError;
}
}
return kTfLiteOk;
} | 610 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::fully_connected::Prepare | tflite::ops::builtin::fully_connected::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
// Check for supported activation types.
auto* params =
reinterpret_cast<TfLiteFullyConnectedParams*>(node->builtin_data);
const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const bool is_quantized =
((filter->type == kTfLiteUInt8) || (filter->type == kTfLiteInt8));
const bool is_hybrid = is_quantized && (input->type == kTfLiteFloat32);
const bool is_pie = kernel_type == kLegacyPie;
// Pie and hybrid path supports all kinds of fused activations, otherwise only
// clipping activations are supported.
if (!is_pie && !is_hybrid) {
TF_LITE_ENSURE(context, params->activation == kTfLiteActNone ||
params->activation == kTfLiteActRelu ||
params->activation == kTfLiteActReluN1To1 ||
params->activation == kTfLiteActRelu6);
}
return PrepareImpl(context, node);
} | 146 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::fully_connected::Prepare | tflite::ops::builtin::fully_connected::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
// Check for supported activation types.
auto* params =
reinterpret_cast<TfLiteFullyConnectedParams*>(node->builtin_data);
const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const bool is_quantized =
((filter->type == kTfLiteUInt8) || (filter->type == kTfLiteInt8));
const bool is_hybrid = is_quantized && (input->type == kTfLiteFloat32);
const bool is_pie = kernel_type == kLegacyPie;
// Pie and hybrid path supports all kinds of fused activations, otherwise only
// clipping activations are supported.
if (!is_pie && !is_hybrid) {
TF_LITE_ENSURE(context, params->activation == kTfLiteActNone ||
params->activation == kTfLiteActRelu ||
params->activation == kTfLiteActReluN1To1 ||
params->activation == kTfLiteActRelu6);
}
return PrepareImpl(context, node);
} | 146 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::fully_connected::PrepareImpl | tflite::ops::builtin::fully_connected::PrepareImpl( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus PrepareImpl(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteFullyConnectedParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
// Check we have all the inputs and outputs we need.
TF_LITE_ENSURE(context, node->inputs->size == 2 || node->inputs->size == 3);
// Shuffled formats need a workspace to store the shuffled input activations.
const int expected_outputs_count =
params->weights_format == kTfLiteFullyConnectedWeightsFormatDefault ? 1
: 2;
TF_LITE_ENSURE_EQ(context, node->outputs->size, expected_outputs_count);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor);
const TfLiteTensor* bias =
(node->inputs->size == 3)
? GetOptionalInputTensor(context, node, kBiasTensor)
: nullptr;
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// Check proper datatype match among all Input Tensors
TF_LITE_ENSURE_STATUS(
CheckTypes(context, input, filter, bias, output, params));
// Check all the parameters of tensor match within themselves and match the
// input configuration.
int input_size = 1;
for (int i = 0; i < input->dims->size; i++) {
input_size *= input->dims->data[i];
}
TF_LITE_ENSURE_EQ(context, NumDimensions(filter), 2);
const int batch_size = input_size / filter->dims->data[1];
const int num_units = filter->dims->data[0];
if (bias) {
TF_LITE_ENSURE_EQ(context, NumElements(bias), SizeOfDimension(filter, 0));
}
// Note that quantized inference requires that all tensors have their
// parameters set. This is usually done during quantized training.
if (input->type == kTfLiteUInt8 || input->type == kTfLiteInt8 ||
input->type == kTfLiteInt16) {
double real_multiplier = 0.0;
TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler(
context, input, filter, bias, output, &real_multiplier));
int exponent;
QuantizeMultiplier(real_multiplier, &data->output_multiplier, &exponent);
data->output_shift = exponent;
TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
context, params->activation, output, &data->output_activation_min,
&data->output_activation_max));
}
if (input->type == kTfLiteInt16 && output->type == kTfLiteInt16) {
TF_LITE_ENSURE_EQ(context, input->params.zero_point, 0);
TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
}
// If we have to perform on-the-fly quantization (with quantized weights and
// float inputs) first we need to quantize the inputs. Allocate a temporary
// buffer to store the intermediate quantized values.
// Additionally, we allocate a temporary buffer to store the accumulated
// quantized values prior to multiplication by the scaling factor.
if (input->type == kTfLiteFloat32 &&
(filter->type == kTfLiteUInt8 || filter->type == kTfLiteInt8)) {
TfLiteIntArrayFree(node->temporaries);
data->compute_row_sums = true;
node->temporaries = TfLiteIntArrayCreate(5);
node->temporaries->data[0] = data->scratch_tensor_index;
TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/0);
input_quantized->type = filter->type;
input_quantized->allocation_type = kTfLiteArenaRw;
TfLiteIntArray* input_quantized_size = TfLiteIntArrayCopy(input->dims);
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, input_quantized,
input_quantized_size));
node->temporaries->data[1] = data->scratch_tensor_index + 1;
TfLiteTensor* scaling_factors = GetTemporary(context, node, /*index=*/1);
scaling_factors->type = kTfLiteFloat32;
scaling_factors->allocation_type = kTfLiteArenaRw;
int scaling_dims[1] = {batch_size};
if (!TfLiteIntArrayEqualsArray(scaling_factors->dims, 1, scaling_dims)) {
TfLiteIntArray* scaling_factors_size = TfLiteIntArrayCreate(1);
scaling_factors_size->data[0] = batch_size;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scaling_factors,
scaling_factors_size));
}
node->temporaries->data[2] = data->scratch_tensor_index + 2;
TfLiteTensor* accum_scratch = GetTemporary(context, node, /*index=*/2);
accum_scratch->type = kTfLiteInt32;
accum_scratch->allocation_type = kTfLiteArenaRw;
int accum_scratch_dims[2] = {num_units, batch_size};
if (!TfLiteIntArrayEqualsArray(accum_scratch->dims, 2,
accum_scratch_dims)) {
TfLiteIntArray* accum_size = TfLiteIntArrayCreate(2);
accum_size->data[0] = num_units;
accum_size->data[1] = batch_size;
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, accum_scratch, accum_size));
}
node->temporaries->data[3] = data->scratch_tensor_index + 3;
TfLiteTensor* input_offsets = GetTemporary(context, node, /*index=*/3);
input_offsets->type = kTfLiteInt32;
input_offsets->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqualsArray(input_offsets->dims, 1, scaling_dims)) {
TfLiteIntArray* input_offsets_size = TfLiteIntArrayCreate(1);
input_offsets_size->data[0] = batch_size;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, input_offsets,
input_offsets_size));
}
node->temporaries->data[4] = data->scratch_tensor_index + 4;
TfLiteTensor* row_sums = GetTemporary(context, node, /*index=*/4);
row_sums->type = kTfLiteInt32;
row_sums->allocation_type = kTfLiteArenaRwPersistent;
int row_sums_dims[1] = {num_units};
if (!TfLiteIntArrayEqualsArray(row_sums->dims, 1, row_sums_dims)) {
TfLiteIntArray* row_sums_size = TfLiteIntArrayCreate(1);
row_sums_size->data[0] = row_sums_dims[0];
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, row_sums, row_sums_size));
}
}
// Resize output.
TfLiteIntArray* output_size_array = nullptr;
if (params->keep_num_dims) {
// When number of dimensions are kept the filter operates along the last
// dimensions. In other words, for an input tensor with shape
// [batch_size, ..., n_inputs] and a filter of shape [n_inputs, n_units]
// this Op produces an output of shape [batch_size, ..., n_units].
TF_LITE_ENSURE_EQ(context, input->dims->data[input->dims->size - 1],
SizeOfDimension(filter, 1));
output_size_array = TfLiteIntArrayCopy(input->dims);
output_size_array->data[output_size_array->size - 1] = num_units;
} else {
// Otherwise, the output is (potentially flattened to) a 2-D matrix.
output_size_array = TfLiteIntArrayCreate(2);
output_size_array->data[0] = batch_size;
output_size_array->data[1] = num_units;
}
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, output, output_size_array));
return kTfLiteOk;
} | 1038 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::fully_connected::PrepareImpl | tflite::ops::builtin::fully_connected::PrepareImpl( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus PrepareImpl(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteFullyConnectedParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
// Check we have all the inputs and outputs we need.
TF_LITE_ENSURE(context, node->inputs->size == 2 || node->inputs->size == 3);
// Shuffled formats need a workspace to store the shuffled input activations.
const int expected_outputs_count =
params->weights_format == kTfLiteFullyConnectedWeightsFormatDefault ? 1
: 2;
TF_LITE_ENSURE_EQ(context, node->outputs->size, expected_outputs_count);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor);
const TfLiteTensor* bias =
(node->inputs->size == 3)
? GetOptionalInputTensor(context, node, kBiasTensor)
: nullptr;
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// Check proper datatype match among all Input Tensors
TF_LITE_ENSURE_STATUS(
CheckTypes(context, input, filter, bias, output, params));
// Check all the parameters of tensor match within themselves and match the
// input configuration.
int input_size = 1;
for (int i = 0; i < input->dims->size; i++) {
input_size *= input->dims->data[i];
}
TF_LITE_ENSURE_EQ(context, NumDimensions(filter), 2);
const int batch_size = input_size / filter->dims->data[1];
const int num_units = filter->dims->data[0];
if (bias) {
TF_LITE_ENSURE_EQ(context, NumElements(bias), SizeOfDimension(filter, 0));
}
// Note that quantized inference requires that all tensors have their
// parameters set. This is usually done during quantized training.
if (input->type == kTfLiteUInt8 || input->type == kTfLiteInt8 ||
input->type == kTfLiteInt16) {
double real_multiplier = 0.0;
TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler(
context, input, filter, bias, output, &real_multiplier));
int exponent;
QuantizeMultiplier(real_multiplier, &data->output_multiplier, &exponent);
data->output_shift = exponent;
TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
context, params->activation, output, &data->output_activation_min,
&data->output_activation_max));
}
if (input->type == kTfLiteInt16 && output->type == kTfLiteInt16) {
TF_LITE_ENSURE_EQ(context, input->params.zero_point, 0);
TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
}
// If we have to perform on-the-fly quantization (with quantized weights and
// float inputs) first we need to quantize the inputs. Allocate a temporary
// buffer to store the intermediate quantized values.
// Additionally, we allocate a temporary buffer to store the accumulated
// quantized values prior to multiplication by the scaling factor.
if (input->type == kTfLiteFloat32 &&
(filter->type == kTfLiteUInt8 || filter->type == kTfLiteInt8)) {
TfLiteIntArrayFree(node->temporaries);
data->compute_row_sums = true;
node->temporaries = TfLiteIntArrayCreate(5);
node->temporaries->data[0] = data->scratch_tensor_index;
TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/0);
input_quantized->type = filter->type;
input_quantized->allocation_type = kTfLiteArenaRw;
TfLiteIntArray* input_quantized_size = TfLiteIntArrayCopy(input->dims);
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, input_quantized,
input_quantized_size));
node->temporaries->data[1] = data->scratch_tensor_index + 1;
TfLiteTensor* scaling_factors = GetTemporary(context, node, /*index=*/1);
scaling_factors->type = kTfLiteFloat32;
scaling_factors->allocation_type = kTfLiteArenaRw;
int scaling_dims[1] = {batch_size};
if (!TfLiteIntArrayEqualsArray(scaling_factors->dims, 1, scaling_dims)) {
TfLiteIntArray* scaling_factors_size = TfLiteIntArrayCreate(1);
scaling_factors_size->data[0] = batch_size;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scaling_factors,
scaling_factors_size));
}
node->temporaries->data[2] = data->scratch_tensor_index + 2;
TfLiteTensor* accum_scratch = GetTemporary(context, node, /*index=*/2);
accum_scratch->type = kTfLiteInt32;
accum_scratch->allocation_type = kTfLiteArenaRw;
int accum_scratch_dims[2] = {num_units, batch_size};
if (!TfLiteIntArrayEqualsArray(accum_scratch->dims, 2,
accum_scratch_dims)) {
TfLiteIntArray* accum_size = TfLiteIntArrayCreate(2);
accum_size->data[0] = num_units;
accum_size->data[1] = batch_size;
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, accum_scratch, accum_size));
}
node->temporaries->data[3] = data->scratch_tensor_index + 3;
TfLiteTensor* input_offsets = GetTemporary(context, node, /*index=*/3);
input_offsets->type = kTfLiteInt32;
input_offsets->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqualsArray(input_offsets->dims, 1, scaling_dims)) {
TfLiteIntArray* input_offsets_size = TfLiteIntArrayCreate(1);
input_offsets_size->data[0] = batch_size;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, input_offsets,
input_offsets_size));
}
node->temporaries->data[4] = data->scratch_tensor_index + 4;
TfLiteTensor* row_sums = GetTemporary(context, node, /*index=*/4);
row_sums->type = kTfLiteInt32;
row_sums->allocation_type = kTfLiteArenaRwPersistent;
int row_sums_dims[1] = {num_units};
if (!TfLiteIntArrayEqualsArray(row_sums->dims, 1, row_sums_dims)) {
TfLiteIntArray* row_sums_size = TfLiteIntArrayCreate(1);
row_sums_size->data[0] = row_sums_dims[0];
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, row_sums, row_sums_size));
}
}
// Resize output.
TfLiteIntArray* output_size_array = nullptr;
if (params->keep_num_dims) {
// When number of dimensions are kept the filter operates along the last
// dimensions. In other words, for an input tensor with shape
// [batch_size, ..., n_inputs] and a filter of shape [n_inputs, n_units]
// this Op produces an output of shape [batch_size, ..., n_units].
TF_LITE_ENSURE_EQ(context, input->dims->data[input->dims->size - 1],
SizeOfDimension(filter, 1));
output_size_array = TfLiteIntArrayCopy(input->dims);
output_size_array->data[output_size_array->size - 1] = num_units;
} else {
// Otherwise, the output is (potentially flattened to) a 2-D matrix.
output_size_array = TfLiteIntArrayCreate(2);
output_size_array->data[0] = batch_size;
output_size_array->data[1] = num_units;
}
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, output, output_size_array));
return kTfLiteOk;
} | 1038 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::gather::Eval | tflite::ops::builtin::gather::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const auto* params =
reinterpret_cast<const TfLiteGatherParams*>(node->builtin_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* positions = GetInput(context, node, kInputPositions);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (positions->type == kTfLiteInt32) {
switch (input->type) {
case kTfLiteFloat32:
return Gather<float, int32_t>(*params, input, positions, output);
case kTfLiteUInt8:
return Gather<uint8_t, int32_t>(*params, input, positions, output);
case kTfLiteInt8:
return Gather<int8_t, int32_t>(*params, input, positions, output);
case kTfLiteInt16:
return Gather<int16_t, int32_t>(*params, input, positions, output);
case kTfLiteInt32:
return Gather<int32_t, int32_t>(*params, input, positions, output);
case kTfLiteInt64:
return Gather<int64_t, int32_t>(*params, input, positions, output);
case kTfLiteBool:
return Gather<bool, int32_t>(*params, input, positions, output);
case kTfLiteString:
return GatherStrings<int32_t>(context, input, positions, output);
default:
context->ReportError(context, "Type '%s' is not supported by gather.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
}
if (positions->type == kTfLiteInt64) {
switch (input->type) {
case kTfLiteFloat32:
return Gather<float, int64_t>(*params, input, positions, output);
case kTfLiteUInt8:
return Gather<uint8_t, int64_t>(*params, input, positions, output);
case kTfLiteInt8:
return Gather<int8_t, int64_t>(*params, input, positions, output);
case kTfLiteInt16:
return Gather<int16_t, int64_t>(*params, input, positions, output);
case kTfLiteInt32:
return Gather<int32_t, int64_t>(*params, input, positions, output);
case kTfLiteInt64:
return Gather<int64_t, int64_t>(*params, input, positions, output);
case kTfLiteBool:
return Gather<bool, int64_t>(*params, input, positions, output);
case kTfLiteString:
return GatherStrings<int64_t>(context, input, positions, output);
default:
context->ReportError(context, "Type '%s' is not supported by gather.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
}
context->ReportError(context,
"Positions of type '%s' are not supported by gather.",
TfLiteTypeGetName(positions->type));
return kTfLiteError;
} | 497 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::gather::Eval | tflite::ops::builtin::gather::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const auto* params =
reinterpret_cast<const TfLiteGatherParams*>(node->builtin_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* positions = GetInput(context, node, kInputPositions);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (positions->type == kTfLiteInt32) {
switch (input->type) {
case kTfLiteFloat32:
return Gather<float, int32_t>(*params, input, positions, output);
case kTfLiteUInt8:
return Gather<uint8_t, int32_t>(*params, input, positions, output);
case kTfLiteInt8:
return Gather<int8_t, int32_t>(*params, input, positions, output);
case kTfLiteInt16:
return Gather<int16_t, int32_t>(*params, input, positions, output);
case kTfLiteInt32:
return Gather<int32_t, int32_t>(*params, input, positions, output);
case kTfLiteInt64:
return Gather<int64_t, int32_t>(*params, input, positions, output);
case kTfLiteBool:
return Gather<bool, int32_t>(*params, input, positions, output);
case kTfLiteString:
return GatherStrings<int32_t>(context, input, positions, output);
default:
context->ReportError(context, "Type '%s' is not supported by gather.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
}
if (positions->type == kTfLiteInt64) {
switch (input->type) {
case kTfLiteFloat32:
return Gather<float, int64_t>(*params, input, positions, output);
case kTfLiteUInt8:
return Gather<uint8_t, int64_t>(*params, input, positions, output);
case kTfLiteInt8:
return Gather<int8_t, int64_t>(*params, input, positions, output);
case kTfLiteInt16:
return Gather<int16_t, int64_t>(*params, input, positions, output);
case kTfLiteInt32:
return Gather<int32_t, int64_t>(*params, input, positions, output);
case kTfLiteInt64:
return Gather<int64_t, int64_t>(*params, input, positions, output);
case kTfLiteBool:
return Gather<bool, int64_t>(*params, input, positions, output);
case kTfLiteString:
return GatherStrings<int64_t>(context, input, positions, output);
default:
context->ReportError(context, "Type '%s' is not supported by gather.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
}
context->ReportError(context,
"Positions of type '%s' are not supported by gather.",
TfLiteTypeGetName(positions->type));
return kTfLiteError;
} | 497 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::gather::Prepare | tflite::ops::builtin::gather::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const auto* params =
reinterpret_cast<const TfLiteGatherParams*>(node->builtin_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* positions = GetInput(context, node, kInputPositions);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
switch (positions->type) {
case kTfLiteInt64:
case kTfLiteInt32:
break;
default:
context->ReportError(
context, "Positions of type '%s' are not supported by gather.",
TfLiteTypeGetName(positions->type));
return kTfLiteError;
}
// Assign to output the input type.
output->type = input->type;
// Check conditions for different types.
switch (input->type) {
case kTfLiteFloat32:
case kTfLiteUInt8:
case kTfLiteInt8:
case kTfLiteInt16:
case kTfLiteInt64:
case kTfLiteInt32:
case kTfLiteBool:
break;
case kTfLiteString: {
// Only 1D input is supported.
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 1);
} break;
default:
context->ReportError(context, "Type '%s' is not supported by gather.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
int axis = params->axis;
if (axis < 0) {
axis += NumDimensions(input);
}
TF_LITE_ENSURE(context, 0 <= axis && axis < NumDimensions(input));
const int num_dimensions =
NumDimensions(input) + NumDimensions(positions) - 1;
TfLiteIntArray* output_shape = TfLiteIntArrayCreate(num_dimensions);
int output_index = 0;
for (int i = 0; i < axis; ++i) {
output_shape->data[output_index++] = input->dims->data[i];
}
for (int i = 0; i < positions->dims->size; ++i) {
output_shape->data[output_index++] = positions->dims->data[i];
}
for (int i = axis + 1; i < input->dims->size; ++i) {
output_shape->data[output_index++] = input->dims->data[i];
}
return context->ResizeTensor(context, output, output_shape);
} | 399 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::gather::Prepare | tflite::ops::builtin::gather::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const auto* params =
reinterpret_cast<const TfLiteGatherParams*>(node->builtin_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* positions = GetInput(context, node, kInputPositions);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
switch (positions->type) {
case kTfLiteInt64:
case kTfLiteInt32:
break;
default:
context->ReportError(
context, "Positions of type '%s' are not supported by gather.",
TfLiteTypeGetName(positions->type));
return kTfLiteError;
}
// Assign to output the input type.
output->type = input->type;
// Check conditions for different types.
switch (input->type) {
case kTfLiteFloat32:
case kTfLiteUInt8:
case kTfLiteInt8:
case kTfLiteInt16:
case kTfLiteInt64:
case kTfLiteInt32:
case kTfLiteBool:
break;
case kTfLiteString: {
// Only 1D input is supported.
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 1);
} break;
default:
context->ReportError(context, "Type '%s' is not supported by gather.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
int axis = params->axis;
if (axis < 0) {
axis += NumDimensions(input);
}
TF_LITE_ENSURE(context, 0 <= axis && axis < NumDimensions(input));
const int num_dimensions =
NumDimensions(input) + NumDimensions(positions) - 1;
TfLiteIntArray* output_shape = TfLiteIntArrayCreate(num_dimensions);
int output_index = 0;
for (int i = 0; i < axis; ++i) {
output_shape->data[output_index++] = input->dims->data[i];
}
for (int i = 0; i < positions->dims->size; ++i) {
output_shape->data[output_index++] = positions->dims->data[i];
}
for (int i = axis + 1; i < input->dims->size; ++i) {
output_shape->data[output_index++] = input->dims->data[i];
}
return context->ResizeTensor(context, output, output_shape);
} | 399 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::Eval | tflite::ops::builtin::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
TfLiteTensor* output = GetOutput(context, node, 0);
TfLiteTensor* hits = GetOutput(context, node, 1);
const TfLiteTensor* lookup = GetInput(context, node, 0);
const TfLiteTensor* key = GetInput(context, node, 1);
const TfLiteTensor* value = GetInput(context, node, 2);
const int num_rows = SizeOfDimension(value, 0);
const int row_bytes = value->bytes / num_rows;
void* pointer = nullptr;
DynamicBuffer buf;
for (int i = 0; i < SizeOfDimension(lookup, 0); i++) {
int idx = -1;
pointer = bsearch(&(lookup->data.i32[i]), key->data.i32, num_rows,
sizeof(int32_t), greater);
if (pointer != nullptr) {
idx = (reinterpret_cast<char*>(pointer) - (key->data.raw)) /
sizeof(int32_t);
}
if (idx >= num_rows || idx < 0) {
if (output->type == kTfLiteString) {
buf.AddString(nullptr, 0);
} else {
memset(output->data.raw + i * row_bytes, 0, row_bytes);
}
hits->data.uint8[i] = 0;
} else {
if (output->type == kTfLiteString) {
buf.AddString(GetString(value, idx));
} else {
memcpy(output->data.raw + i * row_bytes,
value->data.raw + idx * row_bytes, row_bytes);
}
hits->data.uint8[i] = 1;
}
}
if (output->type == kTfLiteString) {
buf.WriteToTensorAsVector(output);
}
return kTfLiteOk;
} | 349 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::Eval | tflite::ops::builtin::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
TfLiteTensor* output = GetOutput(context, node, 0);
TfLiteTensor* hits = GetOutput(context, node, 1);
const TfLiteTensor* lookup = GetInput(context, node, 0);
const TfLiteTensor* key = GetInput(context, node, 1);
const TfLiteTensor* value = GetInput(context, node, 2);
const int num_rows = SizeOfDimension(value, 0);
const int row_bytes = value->bytes / num_rows;
void* pointer = nullptr;
DynamicBuffer buf;
for (int i = 0; i < SizeOfDimension(lookup, 0); i++) {
int idx = -1;
pointer = bsearch(&(lookup->data.i32[i]), key->data.i32, num_rows,
sizeof(int32_t), greater);
if (pointer != nullptr) {
idx = (reinterpret_cast<char*>(pointer) - (key->data.raw)) /
sizeof(int32_t);
}
if (idx >= num_rows || idx < 0) {
if (output->type == kTfLiteString) {
buf.AddString(nullptr, 0);
} else {
memset(output->data.raw + i * row_bytes, 0, row_bytes);
}
hits->data.uint8[i] = 0;
} else {
if (output->type == kTfLiteString) {
buf.AddString(GetString(value, idx));
} else {
memcpy(output->data.raw + i * row_bytes,
value->data.raw + idx * row_bytes, row_bytes);
}
hits->data.uint8[i] = 1;
}
}
if (output->type == kTfLiteString) {
buf.WriteToTensorAsVector(output);
}
return kTfLiteOk;
} | 349 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::Prepare | tflite::ops::builtin::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
const TfLiteTensor* lookup = GetInput(context, node, 0);
TF_LITE_ENSURE_EQ(context, NumDimensions(lookup), 1);
TF_LITE_ENSURE_EQ(context, lookup->type, kTfLiteInt32);
const TfLiteTensor* key = GetInput(context, node, 1);
TF_LITE_ENSURE_EQ(context, NumDimensions(key), 1);
TF_LITE_ENSURE_EQ(context, key->type, kTfLiteInt32);
const TfLiteTensor* value = GetInput(context, node, 2);
TF_LITE_ENSURE(context, NumDimensions(value) >= 1);
TF_LITE_ENSURE_EQ(context, SizeOfDimension(key, 0),
SizeOfDimension(value, 0));
if (value->type == kTfLiteString) {
TF_LITE_ENSURE_EQ(context, NumDimensions(value), 1);
}
TfLiteTensor* hits = GetOutput(context, node, 1);
TF_LITE_ENSURE_EQ(context, hits->type, kTfLiteUInt8);
TfLiteIntArray* hitSize = TfLiteIntArrayCreate(1);
hitSize->data[0] = SizeOfDimension(lookup, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
TF_LITE_ENSURE_EQ(context, value->type, output->type);
TfLiteStatus status = kTfLiteOk;
if (output->type != kTfLiteString) {
TfLiteIntArray* outputSize = TfLiteIntArrayCreate(NumDimensions(value));
outputSize->data[0] = SizeOfDimension(lookup, 0);
for (int i = 1; i < NumDimensions(value); i++) {
outputSize->data[i] = SizeOfDimension(value, i);
}
status = context->ResizeTensor(context, output, outputSize);
}
if (context->ResizeTensor(context, hits, hitSize) != kTfLiteOk) {
status = kTfLiteError;
}
return status;
} | 361 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::Prepare | tflite::ops::builtin::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
const TfLiteTensor* lookup = GetInput(context, node, 0);
TF_LITE_ENSURE_EQ(context, NumDimensions(lookup), 1);
TF_LITE_ENSURE_EQ(context, lookup->type, kTfLiteInt32);
const TfLiteTensor* key = GetInput(context, node, 1);
TF_LITE_ENSURE_EQ(context, NumDimensions(key), 1);
TF_LITE_ENSURE_EQ(context, key->type, kTfLiteInt32);
const TfLiteTensor* value = GetInput(context, node, 2);
TF_LITE_ENSURE(context, NumDimensions(value) >= 1);
TF_LITE_ENSURE_EQ(context, SizeOfDimension(key, 0),
SizeOfDimension(value, 0));
if (value->type == kTfLiteString) {
TF_LITE_ENSURE_EQ(context, NumDimensions(value), 1);
}
TfLiteTensor* hits = GetOutput(context, node, 1);
TF_LITE_ENSURE_EQ(context, hits->type, kTfLiteUInt8);
TfLiteIntArray* hitSize = TfLiteIntArrayCreate(1);
hitSize->data[0] = SizeOfDimension(lookup, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
TF_LITE_ENSURE_EQ(context, value->type, output->type);
TfLiteStatus status = kTfLiteOk;
if (output->type != kTfLiteString) {
TfLiteIntArray* outputSize = TfLiteIntArrayCreate(NumDimensions(value));
outputSize->data[0] = SizeOfDimension(lookup, 0);
for (int i = 1; i < NumDimensions(value); i++) {
outputSize->data[i] = SizeOfDimension(value, i);
}
status = context->ResizeTensor(context, output, outputSize);
}
if (context->ResizeTensor(context, hits, hitSize) != kTfLiteOk) {
status = kTfLiteError;
}
return status;
} | 361 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::if_kernel::Eval | tflite::ops::builtin::if_kernel::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* cond = GetInput(context, node, 0);
bool cond_value = cond->data.b[0];
Subgraph* this_subgraph = reinterpret_cast<Subgraph*>(context->impl_);
auto* subgraphs = this_subgraph->GetSubgraphs();
// Currently we copy the input / output between the subgraphs. This isn't
// optimized yet.
// TODO(b/120234921): Optimize and avoid copying tensors between subgraphs.
int active_branch_subgraph_index =
cond_value ? op_data->then_subgraph_index : op_data->else_subgraph_index;
Subgraph& active_branch_subgraph =
*(*subgraphs)[active_branch_subgraph_index];
for (int i = 0; i < active_branch_subgraph.inputs().size(); ++i) {
const TfLiteTensor* input = GetInput(context, node, i + 1);
TfLiteTensor* subgraph_input =
active_branch_subgraph.tensor(active_branch_subgraph.inputs()[i]);
TF_LITE_ENSURE_EQ(context, input->bytes, subgraph_input->bytes);
memcpy(subgraph_input->data.raw, input->data.raw, input->bytes);
}
// Note: It's guaranteed that the subgraphs' `AllocateTensors` are called
// in `Prepare`, so we don't need to do it here again.
TF_LITE_ENSURE_OK(context, active_branch_subgraph.Invoke());
for (int tensor_index : active_branch_subgraph.outputs()) {
active_branch_subgraph.EnsureTensorDataIsReadable(tensor_index);
}
bool has_dynamic_output_tensors = false;
for (int i = 0; i < node->outputs->size; ++i) {
TfLiteTensor* output = GetOutput(context, node, i);
if (IsDynamicTensor(output)) {
has_dynamic_output_tensors = true;
break;
}
}
if (has_dynamic_output_tensors) {
for (int i = 0; i < node->outputs->size; ++i) {
TfLiteTensor* output = GetOutput(context, node, i);
TfLiteTensor* subgraph_output =
active_branch_subgraph.tensor(active_branch_subgraph.outputs()[i]);
TfLiteIntArray* output_size = TfLiteIntArrayCopy(subgraph_output->dims);
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, output, output_size));
}
}
for (int i = 0; i < active_branch_subgraph.outputs().size(); ++i) {
const TfLiteTensor* subgraph_output =
active_branch_subgraph.tensor(active_branch_subgraph.outputs()[i]);
TfLiteTensor* output = GetOutput(context, node, i);
TF_LITE_ENSURE_EQ(context, output->bytes, subgraph_output->bytes);
memcpy(output->data.raw, subgraph_output->data.raw, output->bytes);
}
return kTfLiteOk;
} | 454 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::if_kernel::Eval | tflite::ops::builtin::if_kernel::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* cond = GetInput(context, node, 0);
bool cond_value = cond->data.b[0];
Subgraph* this_subgraph = reinterpret_cast<Subgraph*>(context->impl_);
auto* subgraphs = this_subgraph->GetSubgraphs();
// Currently we copy the input / output between the subgraphs. This isn't
// optimized yet.
// TODO(b/120234921): Optimize and avoid copying tensors between subgraphs.
int active_branch_subgraph_index =
cond_value ? op_data->then_subgraph_index : op_data->else_subgraph_index;
Subgraph& active_branch_subgraph =
*(*subgraphs)[active_branch_subgraph_index];
for (int i = 0; i < active_branch_subgraph.inputs().size(); ++i) {
const TfLiteTensor* input = GetInput(context, node, i + 1);
TfLiteTensor* subgraph_input =
active_branch_subgraph.tensor(active_branch_subgraph.inputs()[i]);
TF_LITE_ENSURE_EQ(context, input->bytes, subgraph_input->bytes);
memcpy(subgraph_input->data.raw, input->data.raw, input->bytes);
}
// Note: It's guaranteed that the subgraphs' `AllocateTensors` are called
// in `Prepare`, so we don't need to do it here again.
TF_LITE_ENSURE_OK(context, active_branch_subgraph.Invoke());
for (int tensor_index : active_branch_subgraph.outputs()) {
active_branch_subgraph.EnsureTensorDataIsReadable(tensor_index);
}
bool has_dynamic_output_tensors = false;
for (int i = 0; i < node->outputs->size; ++i) {
TfLiteTensor* output = GetOutput(context, node, i);
if (IsDynamicTensor(output)) {
has_dynamic_output_tensors = true;
break;
}
}
if (has_dynamic_output_tensors) {
for (int i = 0; i < node->outputs->size; ++i) {
TfLiteTensor* output = GetOutput(context, node, i);
TfLiteTensor* subgraph_output =
active_branch_subgraph.tensor(active_branch_subgraph.outputs()[i]);
TfLiteIntArray* output_size = TfLiteIntArrayCopy(subgraph_output->dims);
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, output, output_size));
}
}
for (int i = 0; i < active_branch_subgraph.outputs().size(); ++i) {
const TfLiteTensor* subgraph_output =
active_branch_subgraph.tensor(active_branch_subgraph.outputs()[i]);
TfLiteTensor* output = GetOutput(context, node, i);
TF_LITE_ENSURE_EQ(context, output->bytes, subgraph_output->bytes);
memcpy(output->data.raw, subgraph_output->data.raw, output->bytes);
}
return kTfLiteOk;
} | 454 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::if_kernel::Prepare | tflite::ops::builtin::if_kernel::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
const OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
TF_LITE_ENSURE(context, node->inputs->size > 0);
// The first input is the condition.
const TfLiteTensor* cond = GetInput(context, node, 0);
// Currently only bool is supported.
// TODO(ycling): Support other types since TensorFlow also support
// non-bool types as condition.
TF_LITE_ENSURE_EQ(context, cond->type, kTfLiteBool);
TF_LITE_ENSURE_EQ(context, NumElements(cond), 1);
// The first input of the node is the condition. The rest of inputs are
// passed to the branch subgraphs. Therefore, the number of subgraph inputs
// will be the number of node inputs - 1.
int num_inputs = node->inputs->size - 1;
int num_outputs = node->outputs->size;
Subgraph* this_subgraph = reinterpret_cast<Subgraph*>(context->impl_);
auto* subgraphs = this_subgraph->GetSubgraphs();
TF_LITE_ENSURE(context, op_data->then_subgraph_index < subgraphs->size());
TF_LITE_ENSURE(context, op_data->else_subgraph_index < subgraphs->size());
Subgraph* then_subgraph = (*subgraphs)[op_data->then_subgraph_index].get();
Subgraph* else_subgraph = (*subgraphs)[op_data->else_subgraph_index].get();
for (auto* subgraph : {then_subgraph, else_subgraph}) {
TF_LITE_ENSURE_EQ(context, num_inputs, subgraph->inputs().size());
TF_LITE_ENSURE_EQ(context, num_outputs, subgraph->outputs().size());
}
bool has_dynamic_output_tensors = false;
for (auto* subgraph : {then_subgraph, else_subgraph}) {
for (int i = 0; i < num_inputs; ++i) {
// The first input of the node is the condition. The indices of the inputs
// passed to the subgraphs are offset by 1.
const TfLiteTensor* input = GetInput(context, node, i + 1);
std::vector<int> dims(input->dims->data,
input->dims->data + input->dims->size);
subgraph->ResizeInputTensor(i, dims);
TfLiteTensor* subgraph_input = subgraph->tensor(subgraph->inputs()[i]);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, subgraph_input->type);
}
// Note: The `Prepare` function is responsible to run `AllocateTensors` on
// both subgraphs. It's intentionally not to break out of the loop when
// finding a dynamic output tensor.
TF_LITE_ENSURE_OK(context, subgraph->AllocateTensors());
has_dynamic_output_tensors |= subgraph->HasDynamicTensors();
}
if (!has_dynamic_output_tensors) {
for (int i = 0; i < num_outputs; ++i) {
TfLiteTensor* then_output =
then_subgraph->tensor(then_subgraph->outputs()[i]);
TfLiteTensor* else_output =
else_subgraph->tensor(else_subgraph->outputs()[i]);
// If the 2 subgraphs have static but different output shapes, the output
// tensors of the IF op have dynamic sizes.
if (!TfLiteIntArrayEqual(then_output->dims, else_output->dims)) {
has_dynamic_output_tensors = true;
break;
}
}
}
for (int i = 0; i < num_outputs; ++i) {
TfLiteTensor* output = GetOutput(context, node, i);
if (has_dynamic_output_tensors) {
SetTensorToDynamic(output);
} else {
// When there's no dynamic output tensors, the 2 subgraph has exactly
// the same static sized outputs.
TfLiteTensor* then_output =
then_subgraph->tensor(then_subgraph->outputs()[i]);
TfLiteIntArray* output_size = TfLiteIntArrayCopy(then_output->dims);
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, output, output_size));
}
}
return kTfLiteOk;
} | 546 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::if_kernel::Prepare | tflite::ops::builtin::if_kernel::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
const OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
TF_LITE_ENSURE(context, node->inputs->size > 0);
// The first input is the condition.
const TfLiteTensor* cond = GetInput(context, node, 0);
// Currently only bool is supported.
// TODO(ycling): Support other types since TensorFlow also support
// non-bool types as condition.
TF_LITE_ENSURE_EQ(context, cond->type, kTfLiteBool);
TF_LITE_ENSURE_EQ(context, NumElements(cond), 1);
// The first input of the node is the condition. The rest of inputs are
// passed to the branch subgraphs. Therefore, the number of subgraph inputs
// will be the number of node inputs - 1.
int num_inputs = node->inputs->size - 1;
int num_outputs = node->outputs->size;
Subgraph* this_subgraph = reinterpret_cast<Subgraph*>(context->impl_);
auto* subgraphs = this_subgraph->GetSubgraphs();
TF_LITE_ENSURE(context, op_data->then_subgraph_index < subgraphs->size());
TF_LITE_ENSURE(context, op_data->else_subgraph_index < subgraphs->size());
Subgraph* then_subgraph = (*subgraphs)[op_data->then_subgraph_index].get();
Subgraph* else_subgraph = (*subgraphs)[op_data->else_subgraph_index].get();
for (auto* subgraph : {then_subgraph, else_subgraph}) {
TF_LITE_ENSURE_EQ(context, num_inputs, subgraph->inputs().size());
TF_LITE_ENSURE_EQ(context, num_outputs, subgraph->outputs().size());
}
bool has_dynamic_output_tensors = false;
for (auto* subgraph : {then_subgraph, else_subgraph}) {
for (int i = 0; i < num_inputs; ++i) {
// The first input of the node is the condition. The indices of the inputs
// passed to the subgraphs are offset by 1.
const TfLiteTensor* input = GetInput(context, node, i + 1);
std::vector<int> dims(input->dims->data,
input->dims->data + input->dims->size);
subgraph->ResizeInputTensor(i, dims);
TfLiteTensor* subgraph_input = subgraph->tensor(subgraph->inputs()[i]);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, subgraph_input->type);
}
// Note: The `Prepare` function is responsible to run `AllocateTensors` on
// both subgraphs. It's intentionally not to break out of the loop when
// finding a dynamic output tensor.
TF_LITE_ENSURE_OK(context, subgraph->AllocateTensors());
has_dynamic_output_tensors |= subgraph->HasDynamicTensors();
}
if (!has_dynamic_output_tensors) {
for (int i = 0; i < num_outputs; ++i) {
TfLiteTensor* then_output =
then_subgraph->tensor(then_subgraph->outputs()[i]);
TfLiteTensor* else_output =
else_subgraph->tensor(else_subgraph->outputs()[i]);
// If the 2 subgraphs have static but different output shapes, the output
// tensors of the IF op have dynamic sizes.
if (!TfLiteIntArrayEqual(then_output->dims, else_output->dims)) {
has_dynamic_output_tensors = true;
break;
}
}
}
for (int i = 0; i < num_outputs; ++i) {
TfLiteTensor* output = GetOutput(context, node, i);
if (has_dynamic_output_tensors) {
SetTensorToDynamic(output);
} else {
// When there's no dynamic output tensors, the 2 subgraph has exactly
// the same static sized outputs.
TfLiteTensor* then_output =
then_subgraph->tensor(then_subgraph->outputs()[i]);
TfLiteIntArray* output_size = TfLiteIntArrayCopy(then_output->dims);
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, output, output_size));
}
}
return kTfLiteOk;
} | 546 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::l2norm::Eval | tflite::ops::builtin::l2norm::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// TODO(b/143912164): instead of hardcode the epsilon here, we should read it
// from tensorflow, i.e., adding a params.
// We don't compute epsilon for quantized kernel:
//
// epsilon_float = (epsilon_quant - zp) * scale
// so
// espsilon_quant = epsilon_float / scale + zp
// We know epsilon_float is just a very small number to avoid division by
// zero error, and scale is > 1, so the integer value of epsilon for quant
// is just dominated by the zero point.
// Also, GetInvSqrtQuantizedMultiplierExp handles the scenario where the sum
// of input value squared is zero case well.
// So we don't even need to do handle the epsilon for quantized kernel case.
const float epsilon = 1e-6f;
if (output->type == kTfLiteFloat32) {
#define TF_LITE_L2NORM(type) \
tflite::L2NormalizationParams op_params; \
op_params.input_zero_point = 0; \
type::L2Normalization(op_params, GetTensorShape(input), \
GetTensorData<float>(input), GetTensorShape(output), \
GetTensorData<float>(output), epsilon)
if (kernel_type == kReference) {
TF_LITE_L2NORM(reference_ops);
}
if (kernel_type == kGenericOptimized) {
TF_LITE_L2NORM(optimized_ops);
}
#undef TF_LITE_L2NORM
} else if (output->type == kTfLiteUInt8) {
#define TF_LITE_L2NORM(type) \
tflite::L2NormalizationParams op_params; \
op_params.input_zero_point = input->params.zero_point; \
type::L2Normalization(op_params, GetTensorShape(input), \
GetTensorData<uint8>(input), GetTensorShape(output), \
GetTensorData<uint8>(output))
if (kernel_type == kReference) {
TF_LITE_L2NORM(reference_ops);
}
if (kernel_type == kGenericOptimized) {
TF_LITE_L2NORM(optimized_ops);
}
#undef TF_LITE_L2NORM
} else if (output->type == kTfLiteInt8) {
const auto input_shape = GetTensorShape(input);
const auto output_shape = GetTensorShape(output);
const int trailing_dim = input_shape.DimensionsCount() - 1;
const int depth =
MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim);
const int outer_size =
MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape);
reference_integer_ops::L2Normalization(input->params.zero_point, outer_size,
depth, GetTensorData<int8>(input),
GetTensorData<int8>(output));
} else {
TF_LITE_KERNEL_LOG(context, "Output type is %s, requires float.",
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
return kTfLiteOk;
} | 243 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::l2norm::Eval | tflite::ops::builtin::l2norm::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// TODO(b/143912164): instead of hardcode the epsilon here, we should read it
// from tensorflow, i.e., adding a params.
// We don't compute epsilon for quantized kernel:
//
// epsilon_float = (epsilon_quant - zp) * scale
// so
// espsilon_quant = epsilon_float / scale + zp
// We know epsilon_float is just a very small number to avoid division by
// zero error, and scale is > 1, so the integer value of epsilon for quant
// is just dominated by the zero point.
// Also, GetInvSqrtQuantizedMultiplierExp handles the scenario where the sum
// of input value squared is zero case well.
// So we don't even need to do handle the epsilon for quantized kernel case.
const float epsilon = 1e-6f;
if (output->type == kTfLiteFloat32) {
#define TF_LITE_L2NORM(type) \
tflite::L2NormalizationParams op_params; \
op_params.input_zero_point = 0; \
type::L2Normalization(op_params, GetTensorShape(input), \
GetTensorData<float>(input), GetTensorShape(output), \
GetTensorData<float>(output), epsilon)
if (kernel_type == kReference) {
TF_LITE_L2NORM(reference_ops);
}
if (kernel_type == kGenericOptimized) {
TF_LITE_L2NORM(optimized_ops);
}
#undef TF_LITE_L2NORM
} else if (output->type == kTfLiteUInt8) {
#define TF_LITE_L2NORM(type) \
tflite::L2NormalizationParams op_params; \
op_params.input_zero_point = input->params.zero_point; \
type::L2Normalization(op_params, GetTensorShape(input), \
GetTensorData<uint8>(input), GetTensorShape(output), \
GetTensorData<uint8>(output))
if (kernel_type == kReference) {
TF_LITE_L2NORM(reference_ops);
}
if (kernel_type == kGenericOptimized) {
TF_LITE_L2NORM(optimized_ops);
}
#undef TF_LITE_L2NORM
} else if (output->type == kTfLiteInt8) {
const auto input_shape = GetTensorShape(input);
const auto output_shape = GetTensorShape(output);
const int trailing_dim = input_shape.DimensionsCount() - 1;
const int depth =
MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim);
const int outer_size =
MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape);
reference_integer_ops::L2Normalization(input->params.zero_point, outer_size,
depth, GetTensorData<int8>(input),
GetTensorData<int8>(output));
} else {
TF_LITE_KERNEL_LOG(context, "Output type is %s, requires float.",
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
return kTfLiteOk;
} | 243 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::l2norm::Prepare | tflite::ops::builtin::l2norm::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteL2NormParams*>(node->builtin_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE(context, NumDimensions(input) <= 4);
TF_LITE_ENSURE(context, output->type == kTfLiteFloat32 ||
output->type == kTfLiteUInt8 ||
output->type == kTfLiteInt8);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) {
TF_LITE_ENSURE_EQ(context, output->params.scale, (1. / 128.));
if (output->type == kTfLiteUInt8) {
TF_LITE_ENSURE_EQ(context, output->params.zero_point, 128);
}
if (output->type == kTfLiteInt8) {
TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
}
}
// TODO(ahentz): For some reason our implementations don't support
// activations.
TF_LITE_ENSURE_EQ(context, params->activation, kTfLiteActNone);
TfLiteIntArray* output_size = TfLiteIntArrayCopy(input->dims);
return context->ResizeTensor(context, output, output_size);
} | 241 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::l2norm::Prepare | tflite::ops::builtin::l2norm::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteL2NormParams*>(node->builtin_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE(context, NumDimensions(input) <= 4);
TF_LITE_ENSURE(context, output->type == kTfLiteFloat32 ||
output->type == kTfLiteUInt8 ||
output->type == kTfLiteInt8);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) {
TF_LITE_ENSURE_EQ(context, output->params.scale, (1. / 128.));
if (output->type == kTfLiteUInt8) {
TF_LITE_ENSURE_EQ(context, output->params.zero_point, 128);
}
if (output->type == kTfLiteInt8) {
TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
}
}
// TODO(ahentz): For some reason our implementations don't support
// activations.
TF_LITE_ENSURE_EQ(context, params->activation, kTfLiteActNone);
TfLiteIntArray* output_size = TfLiteIntArrayCopy(input->dims);
return context->ResizeTensor(context, output, output_size);
} | 241 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::local_response_norm::Eval | tflite::ops::builtin::local_response_norm::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteLocalResponseNormParams*>(node->builtin_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (output->type == kTfLiteFloat32) {
#define TF_LITE_LOCAL_RESPONSE_NORM(type) \
tflite::LocalResponseNormalizationParams op_params; \
op_params.range = params->radius; \
op_params.bias = params->bias; \
op_params.alpha = params->alpha; \
op_params.beta = params->beta; \
type::LocalResponseNormalization( \
op_params, GetTensorShape(input), GetTensorData<float>(input), \
GetTensorShape(output), GetTensorData<float>(output))
if (kernel_type == kReference) {
TF_LITE_LOCAL_RESPONSE_NORM(reference_ops);
}
if (kernel_type == kGenericOptimized) {
TF_LITE_LOCAL_RESPONSE_NORM(optimized_ops);
}
#undef TF_LITE_LOCAL_RESPONSE_NORM
} else {
context->ReportError(context, "Output type is %d, requires float.",
output->type);
return kTfLiteError;
}
return kTfLiteOk;
} | 112 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::local_response_norm::Eval | tflite::ops::builtin::local_response_norm::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteLocalResponseNormParams*>(node->builtin_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (output->type == kTfLiteFloat32) {
#define TF_LITE_LOCAL_RESPONSE_NORM(type) \
tflite::LocalResponseNormalizationParams op_params; \
op_params.range = params->radius; \
op_params.bias = params->bias; \
op_params.alpha = params->alpha; \
op_params.beta = params->beta; \
type::LocalResponseNormalization( \
op_params, GetTensorShape(input), GetTensorData<float>(input), \
GetTensorShape(output), GetTensorData<float>(output))
if (kernel_type == kReference) {
TF_LITE_LOCAL_RESPONSE_NORM(reference_ops);
}
if (kernel_type == kGenericOptimized) {
TF_LITE_LOCAL_RESPONSE_NORM(optimized_ops);
}
#undef TF_LITE_LOCAL_RESPONSE_NORM
} else {
context->ReportError(context, "Output type is %d, requires float.",
output->type);
return kTfLiteError;
}
return kTfLiteOk;
} | 112 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::local_response_norm::Prepare | tflite::ops::builtin::local_response_norm::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteFloat32);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
TfLiteIntArray* output_size = TfLiteIntArrayCreate(4);
output_size->data[0] = input->dims->data[0];
output_size->data[1] = input->dims->data[1];
output_size->data[2] = input->dims->data[2];
output_size->data[3] = input->dims->data[3];
return context->ResizeTensor(context, output, output_size);
} | 184 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::local_response_norm::Prepare | tflite::ops::builtin::local_response_norm::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteFloat32);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
TfLiteIntArray* output_size = TfLiteIntArrayCreate(4);
output_size->data[0] = input->dims->data[0];
output_size->data[1] = input->dims->data[1];
output_size->data[2] = input->dims->data[2];
output_size->data[3] = input->dims->data[3];
return context->ResizeTensor(context, output, output_size);
} | 184 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::logical::LogicalImpl | tflite::ops::builtin::logical::LogicalImpl( TfLiteContext * context , TfLiteNode * node , bool(*func)(bool,bool)) | ['context', 'node', 'bool'] | TfLiteStatus LogicalImpl(TfLiteContext* context, TfLiteNode* node,
bool (*func)(bool, bool)) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (data->requires_broadcast) {
reference_ops::BroadcastBinaryFunction4DSlow<bool, bool, bool>(
GetTensorShape(input1), GetTensorData<bool>(input1),
GetTensorShape(input2), GetTensorData<bool>(input2),
GetTensorShape(output), GetTensorData<bool>(output), func);
} else {
reference_ops::BinaryFunction<bool, bool, bool>(
GetTensorShape(input1), GetTensorData<bool>(input1),
GetTensorShape(input2), GetTensorData<bool>(input2),
GetTensorShape(output), GetTensorData<bool>(output), func);
}
return kTfLiteOk;
} | 199 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::logical::LogicalImpl | tflite::ops::builtin::logical::LogicalImpl( TfLiteContext * context , TfLiteNode * node , bool(*func)(bool,bool)) | ['context', 'node', 'bool'] | TfLiteStatus LogicalImpl(TfLiteContext* context, TfLiteNode* node,
bool (*func)(bool, bool)) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (data->requires_broadcast) {
reference_ops::BroadcastBinaryFunction4DSlow<bool, bool, bool>(
GetTensorShape(input1), GetTensorData<bool>(input1),
GetTensorShape(input2), GetTensorData<bool>(input2),
GetTensorShape(output), GetTensorData<bool>(output), func);
} else {
reference_ops::BinaryFunction<bool, bool, bool>(
GetTensorShape(input1), GetTensorData<bool>(input1),
GetTensorShape(input2), GetTensorData<bool>(input2),
GetTensorShape(output), GetTensorData<bool>(output), func);
}
return kTfLiteOk;
} | 199 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::logical::Prepare | tflite::ops::builtin::logical::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
// Reinterprete the opaque data provided by user.
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
const TfLiteType type = input1->type;
if (type != kTfLiteBool) {
context->ReportError(context, "Logical ops only support bool type.");
return kTfLiteError;
}
output->type = type;
data->requires_broadcast = !HaveSameShapes(input1, input2);
TfLiteIntArray* output_size = nullptr;
if (data->requires_broadcast) {
TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
context, input1, input2, &output_size));
} else {
output_size = TfLiteIntArrayCopy(input1->dims);
}
return context->ResizeTensor(context, output, output_size);
} | 206 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::logical::Prepare | tflite::ops::builtin::logical::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
// Reinterprete the opaque data provided by user.
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
const TfLiteType type = input1->type;
if (type != kTfLiteBool) {
context->ReportError(context, "Logical ops only support bool type.");
return kTfLiteError;
}
output->type = type;
data->requires_broadcast = !HaveSameShapes(input1, input2);
TfLiteIntArray* output_size = nullptr;
if (data->requires_broadcast) {
TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
context, input1, input2, &output_size));
} else {
output_size = TfLiteIntArrayCopy(input1->dims);
}
return context->ResizeTensor(context, output, output_size);
} | 206 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::lsh_projection::Eval | tflite::ops::builtin::lsh_projection::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteLSHProjectionParams*>(node->builtin_data);
int32_t* out_buf = GetOutput(context, node, 0)->data.i32;
const TfLiteTensor* hash = GetInput(context, node, 0);
const TfLiteTensor* input = GetInput(context, node, 1);
const TfLiteTensor* weight =
NumInputs(node) == 2 ? nullptr : GetInput(context, node, 2);
switch (params->type) {
case kTfLiteLshProjectionDense:
DenseLshProjection(hash, input, weight, out_buf);
break;
case kTfLiteLshProjectionSparse:
SparseLshProjection(hash, input, weight, out_buf);
break;
default:
return kTfLiteError;
}
return kTfLiteOk;
} | 143 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::lsh_projection::Eval | tflite::ops::builtin::lsh_projection::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteLSHProjectionParams*>(node->builtin_data);
int32_t* out_buf = GetOutput(context, node, 0)->data.i32;
const TfLiteTensor* hash = GetInput(context, node, 0);
const TfLiteTensor* input = GetInput(context, node, 1);
const TfLiteTensor* weight =
NumInputs(node) == 2 ? nullptr : GetInput(context, node, 2);
switch (params->type) {
case kTfLiteLshProjectionDense:
DenseLshProjection(hash, input, weight, out_buf);
break;
case kTfLiteLshProjectionSparse:
SparseLshProjection(hash, input, weight, out_buf);
break;
default:
return kTfLiteError;
}
return kTfLiteOk;
} | 143 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::lsh_projection::Resize | tflite::ops::builtin::lsh_projection::Resize( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Resize(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteLSHProjectionParams*>(node->builtin_data);
TF_LITE_ENSURE(context, NumInputs(node) == 2 || NumInputs(node) == 3);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* hash = GetInput(context, node, 0);
TF_LITE_ENSURE_EQ(context, NumDimensions(hash), 2);
// Support up to 32 bits.
TF_LITE_ENSURE(context, SizeOfDimension(hash, 1) <= 32);
const TfLiteTensor* input = GetInput(context, node, 1);
TF_LITE_ENSURE(context, NumDimensions(input) >= 1);
if (NumInputs(node) == 3) {
const TfLiteTensor* weight = GetInput(context, node, 2);
TF_LITE_ENSURE_EQ(context, NumDimensions(weight), 1);
TF_LITE_ENSURE_EQ(context, SizeOfDimension(weight, 0),
SizeOfDimension(input, 0));
}
TfLiteTensor* output = GetOutput(context, node, 0);
TfLiteIntArray* outputSize = TfLiteIntArrayCreate(1);
switch (params->type) {
case kTfLiteLshProjectionSparse:
outputSize->data[0] = SizeOfDimension(hash, 0);
break;
case kTfLiteLshProjectionDense:
outputSize->data[0] = SizeOfDimension(hash, 0) * SizeOfDimension(hash, 1);
break;
default:
return kTfLiteError;
}
return context->ResizeTensor(context, output, outputSize);
} | 272 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::lsh_projection::Resize | tflite::ops::builtin::lsh_projection::Resize( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Resize(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteLSHProjectionParams*>(node->builtin_data);
TF_LITE_ENSURE(context, NumInputs(node) == 2 || NumInputs(node) == 3);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* hash = GetInput(context, node, 0);
TF_LITE_ENSURE_EQ(context, NumDimensions(hash), 2);
// Support up to 32 bits.
TF_LITE_ENSURE(context, SizeOfDimension(hash, 1) <= 32);
const TfLiteTensor* input = GetInput(context, node, 1);
TF_LITE_ENSURE(context, NumDimensions(input) >= 1);
if (NumInputs(node) == 3) {
const TfLiteTensor* weight = GetInput(context, node, 2);
TF_LITE_ENSURE_EQ(context, NumDimensions(weight), 1);
TF_LITE_ENSURE_EQ(context, SizeOfDimension(weight, 0),
SizeOfDimension(input, 0));
}
TfLiteTensor* output = GetOutput(context, node, 0);
TfLiteIntArray* outputSize = TfLiteIntArrayCreate(1);
switch (params->type) {
case kTfLiteLshProjectionSparse:
outputSize->data[0] = SizeOfDimension(hash, 0);
break;
case kTfLiteLshProjectionDense:
outputSize->data[0] = SizeOfDimension(hash, 0) * SizeOfDimension(hash, 1);
break;
default:
return kTfLiteError;
}
return context->ResizeTensor(context, output, outputSize);
} | 272 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::lstm::basic::Eval | tflite::ops::builtin::lstm::basic::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputData);
const TfLiteTensor* prev_activation =
GetInput(context, node, kInputPrevActivation);
const TfLiteTensor* weights = GetInput(context, node, kInputWeights);
const TfLiteTensor* bias = GetInput(context, node, kInputBiases);
const TfLiteTensor* prev_state = GetInput(context, node, kInputPrevState);
TfLiteTensor* activation_out = GetOutput(context, node, kOutputActivation);
TfLiteTensor* state_out = GetOutput(context, node, kOutputState);
TfLiteTensor* concat_temp = GetOutput(context, node, kOutputConcatTemp);
TfLiteTensor* activation_temp =
GetOutput(context, node, kOutputActivationTemp);
if (input->type == kTfLiteFloat32 &&
prev_activation->type == kTfLiteFloat32 &&
weights->type == kTfLiteFloat32 && bias->type == kTfLiteFloat32 &&
prev_state->type == kTfLiteFloat32 && state_out->type == kTfLiteFloat32 &&
activation_out->type == kTfLiteFloat32 &&
concat_temp->type == kTfLiteFloat32 &&
activation_temp->type == kTfLiteFloat32) {
tflite::LstmCellParams op_params;
// Float LSTM cell does not need parameters to be set: leave untouched.
optimized_ops::LstmCell(
op_params,
// Inputs.
GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(prev_activation), GetTensorData<float>(prev_activation),
GetTensorShape(weights), GetTensorData<float>(weights),
GetTensorShape(bias), GetTensorData<float>(bias),
GetTensorShape(prev_state), GetTensorData<float>(prev_state),
// Outputs.
GetTensorShape(state_out), GetTensorData<float>(state_out),
GetTensorShape(activation_out), GetTensorData<float>(activation_out),
GetTensorShape(concat_temp), GetTensorData<float>(concat_temp),
GetTensorShape(activation_temp), GetTensorData<float>(activation_temp),
CpuBackendContext::GetFromContext(context));
} else if (input->type == kTfLiteUInt8 &&
prev_activation->type == kTfLiteUInt8 &&
weights->type == kTfLiteUInt8 && bias->type == kTfLiteInt32 &&
prev_state->type == kTfLiteInt16 &&
state_out->type == kTfLiteInt16 &&
activation_out->type == kTfLiteUInt8 &&
concat_temp->type == kTfLiteUInt8 &&
activation_temp->type == kTfLiteInt16) {
int state_scale_log2_rounded;
if (!CheckedLog2(state_out->params.scale, &state_scale_log2_rounded)) {
context->ReportError(
context,
"The internal state of a LSTM cell must have a power-of-two scale.");
return kTfLiteError;
}
const int state_integer_bits = 15 + state_scale_log2_rounded;
if (state_integer_bits != 4) {
context->ReportError(context,
"The only case of quantized LstmCell currently "
"supported is with StateIntegerBits==4");
return kTfLiteError;
}
double real_accum_multiplier = 4096 * bias->params.scale;
int32 accum_multiplier;
int accum_shift;
tflite::QuantizeMultiplier(real_accum_multiplier, &accum_multiplier,
&accum_shift);
tflite::LstmCellParams op_params;
op_params.weights_zero_point = weights->params.zero_point;
op_params.accum_multiplier = accum_multiplier;
op_params.accum_shift = accum_shift;
optimized_ops::LstmCell<4>(
op_params,
// Inputs.
GetTensorShape(input), GetTensorData<uint8_t>(input),
GetTensorShape(prev_activation),
GetTensorData<uint8_t>(prev_activation), GetTensorShape(weights),
GetTensorData<uint8_t>(weights), GetTensorShape(bias),
GetTensorData<int32_t>(bias), GetTensorShape(prev_state),
GetTensorData<int16_t>(prev_state),
// Outputs.
GetTensorShape(state_out), GetTensorData<int16_t>(state_out),
GetTensorShape(activation_out), GetTensorData<uint8_t>(activation_out),
GetTensorShape(concat_temp), GetTensorData<uint8_t>(concat_temp),
GetTensorShape(activation_temp),
GetTensorData<int16_t>(activation_temp),
CpuBackendContext::GetFromContext(context));
} else {
context->ReportError(context,
"Unsupported combination of data types for LstmCell");
return kTfLiteError;
}
// TODO(ycling): Investigate if this copy can be avoided with the 5-inputs
// LSTM kernel.
memcpy(prev_activation->data.raw, activation_out->data.raw,
activation_out->bytes);
memcpy(prev_state->data.raw, state_out->data.raw, state_out->bytes);
return kTfLiteOk;
} | 695 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::lstm::basic::Eval | tflite::ops::builtin::lstm::basic::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputData);
const TfLiteTensor* prev_activation =
GetInput(context, node, kInputPrevActivation);
const TfLiteTensor* weights = GetInput(context, node, kInputWeights);
const TfLiteTensor* bias = GetInput(context, node, kInputBiases);
const TfLiteTensor* prev_state = GetInput(context, node, kInputPrevState);
TfLiteTensor* activation_out = GetOutput(context, node, kOutputActivation);
TfLiteTensor* state_out = GetOutput(context, node, kOutputState);
TfLiteTensor* concat_temp = GetOutput(context, node, kOutputConcatTemp);
TfLiteTensor* activation_temp =
GetOutput(context, node, kOutputActivationTemp);
if (input->type == kTfLiteFloat32 &&
prev_activation->type == kTfLiteFloat32 &&
weights->type == kTfLiteFloat32 && bias->type == kTfLiteFloat32 &&
prev_state->type == kTfLiteFloat32 && state_out->type == kTfLiteFloat32 &&
activation_out->type == kTfLiteFloat32 &&
concat_temp->type == kTfLiteFloat32 &&
activation_temp->type == kTfLiteFloat32) {
tflite::LstmCellParams op_params;
// Float LSTM cell does not need parameters to be set: leave untouched.
optimized_ops::LstmCell(
op_params,
// Inputs.
GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(prev_activation), GetTensorData<float>(prev_activation),
GetTensorShape(weights), GetTensorData<float>(weights),
GetTensorShape(bias), GetTensorData<float>(bias),
GetTensorShape(prev_state), GetTensorData<float>(prev_state),
// Outputs.
GetTensorShape(state_out), GetTensorData<float>(state_out),
GetTensorShape(activation_out), GetTensorData<float>(activation_out),
GetTensorShape(concat_temp), GetTensorData<float>(concat_temp),
GetTensorShape(activation_temp), GetTensorData<float>(activation_temp),
CpuBackendContext::GetFromContext(context));
} else if (input->type == kTfLiteUInt8 &&
prev_activation->type == kTfLiteUInt8 &&
weights->type == kTfLiteUInt8 && bias->type == kTfLiteInt32 &&
prev_state->type == kTfLiteInt16 &&
state_out->type == kTfLiteInt16 &&
activation_out->type == kTfLiteUInt8 &&
concat_temp->type == kTfLiteUInt8 &&
activation_temp->type == kTfLiteInt16) {
int state_scale_log2_rounded;
if (!CheckedLog2(state_out->params.scale, &state_scale_log2_rounded)) {
context->ReportError(
context,
"The internal state of a LSTM cell must have a power-of-two scale.");
return kTfLiteError;
}
const int state_integer_bits = 15 + state_scale_log2_rounded;
if (state_integer_bits != 4) {
context->ReportError(context,
"The only case of quantized LstmCell currently "
"supported is with StateIntegerBits==4");
return kTfLiteError;
}
double real_accum_multiplier = 4096 * bias->params.scale;
int32 accum_multiplier;
int accum_shift;
tflite::QuantizeMultiplier(real_accum_multiplier, &accum_multiplier,
&accum_shift);
tflite::LstmCellParams op_params;
op_params.weights_zero_point = weights->params.zero_point;
op_params.accum_multiplier = accum_multiplier;
op_params.accum_shift = accum_shift;
optimized_ops::LstmCell<4>(
op_params,
// Inputs.
GetTensorShape(input), GetTensorData<uint8_t>(input),
GetTensorShape(prev_activation),
GetTensorData<uint8_t>(prev_activation), GetTensorShape(weights),
GetTensorData<uint8_t>(weights), GetTensorShape(bias),
GetTensorData<int32_t>(bias), GetTensorShape(prev_state),
GetTensorData<int16_t>(prev_state),
// Outputs.
GetTensorShape(state_out), GetTensorData<int16_t>(state_out),
GetTensorShape(activation_out), GetTensorData<uint8_t>(activation_out),
GetTensorShape(concat_temp), GetTensorData<uint8_t>(concat_temp),
GetTensorShape(activation_temp),
GetTensorData<int16_t>(activation_temp),
CpuBackendContext::GetFromContext(context));
} else {
context->ReportError(context,
"Unsupported combination of data types for LstmCell");
return kTfLiteError;
}
// TODO(ycling): Investigate if this copy can be avoided with the 5-inputs
// LSTM kernel.
memcpy(prev_activation->data.raw, activation_out->data.raw,
activation_out->bytes);
memcpy(prev_state->data.raw, state_out->data.raw, state_out->bytes);
return kTfLiteOk;
} | 695 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::lstm::basic::Prepare | tflite::ops::builtin::lstm::basic::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE(context, node->inputs->size == kInputNum);
TF_LITE_ENSURE(context, node->outputs->size == kOutputNum);
const TfLiteTensor* input = GetInput(context, node, kInputData);
const TfLiteTensor* prev_activation =
GetInput(context, node, kInputPrevActivation);
const TfLiteTensor* weights = GetInput(context, node, kInputWeights);
const TfLiteTensor* bias = GetInput(context, node, kInputBiases);
const TfLiteTensor* prev_state = GetInput(context, node, kInputPrevState);
TF_LITE_ENSURE_EQ(context, input->dims->size, 2);
const int num_batches = input->dims->data[0];
const int input_depth = input->dims->data[1];
TF_LITE_ENSURE_EQ(context, prev_activation->dims->size, 2);
TF_LITE_ENSURE_EQ(context, prev_activation->dims->data[0], num_batches);
const int activation_depth = prev_activation->dims->data[1];
const int total_depth = input_depth + activation_depth;
TF_LITE_ENSURE_EQ(context, weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, weights->dims->data[0], 4 * activation_depth);
TF_LITE_ENSURE_EQ(context, weights->dims->data[1], total_depth);
TF_LITE_ENSURE_EQ(context, bias->dims->size, 1);
TF_LITE_ENSURE_EQ(context, bias->dims->data[0], 4 * activation_depth);
TF_LITE_ENSURE_EQ(context, prev_state->dims->size, 2);
TF_LITE_ENSURE_EQ(context, prev_state->dims->data[0], num_batches);
TF_LITE_ENSURE_EQ(context, prev_state->dims->data[1], activation_depth);
TfLiteTensor* activation_out = GetOutput(context, node, kOutputActivation);
TfLiteTensor* state_out = GetOutput(context, node, kOutputState);
TfLiteTensor* concat_temp = GetOutput(context, node, kOutputConcatTemp);
TfLiteTensor* activation_temp =
GetOutput(context, node, kOutputActivationTemp);
TF_LITE_ENSURE_OK(context, context->ResizeTensor(
context, activation_out,
TfLiteIntArrayCopy(prev_activation->dims)));
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, state_out,
TfLiteIntArrayCopy(prev_state->dims)));
TfLiteIntArray* concat_temp_size = TfLiteIntArrayCreate(2);
concat_temp_size->data[0] = num_batches;
concat_temp_size->data[1] = total_depth;
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, concat_temp, concat_temp_size));
TfLiteIntArray* activation_temp_size = TfLiteIntArrayCreate(2);
activation_temp_size->data[0] = num_batches;
activation_temp_size->data[1] = 4 * activation_depth;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, activation_temp,
activation_temp_size));
// Set the state tensors as persistent.
for (auto index : {kInputPrevActivation, kInputPrevState}) {
TfLiteTensor* tensor = &context->tensors[node->inputs->data[index]];
tensor->allocation_type = kTfLiteArenaRwPersistent;
}
return kTfLiteOk;
} | 543 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::lstm::basic::Prepare | tflite::ops::builtin::lstm::basic::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE(context, node->inputs->size == kInputNum);
TF_LITE_ENSURE(context, node->outputs->size == kOutputNum);
const TfLiteTensor* input = GetInput(context, node, kInputData);
const TfLiteTensor* prev_activation =
GetInput(context, node, kInputPrevActivation);
const TfLiteTensor* weights = GetInput(context, node, kInputWeights);
const TfLiteTensor* bias = GetInput(context, node, kInputBiases);
const TfLiteTensor* prev_state = GetInput(context, node, kInputPrevState);
TF_LITE_ENSURE_EQ(context, input->dims->size, 2);
const int num_batches = input->dims->data[0];
const int input_depth = input->dims->data[1];
TF_LITE_ENSURE_EQ(context, prev_activation->dims->size, 2);
TF_LITE_ENSURE_EQ(context, prev_activation->dims->data[0], num_batches);
const int activation_depth = prev_activation->dims->data[1];
const int total_depth = input_depth + activation_depth;
TF_LITE_ENSURE_EQ(context, weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, weights->dims->data[0], 4 * activation_depth);
TF_LITE_ENSURE_EQ(context, weights->dims->data[1], total_depth);
TF_LITE_ENSURE_EQ(context, bias->dims->size, 1);
TF_LITE_ENSURE_EQ(context, bias->dims->data[0], 4 * activation_depth);
TF_LITE_ENSURE_EQ(context, prev_state->dims->size, 2);
TF_LITE_ENSURE_EQ(context, prev_state->dims->data[0], num_batches);
TF_LITE_ENSURE_EQ(context, prev_state->dims->data[1], activation_depth);
TfLiteTensor* activation_out = GetOutput(context, node, kOutputActivation);
TfLiteTensor* state_out = GetOutput(context, node, kOutputState);
TfLiteTensor* concat_temp = GetOutput(context, node, kOutputConcatTemp);
TfLiteTensor* activation_temp =
GetOutput(context, node, kOutputActivationTemp);
TF_LITE_ENSURE_OK(context, context->ResizeTensor(
context, activation_out,
TfLiteIntArrayCopy(prev_activation->dims)));
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, state_out,
TfLiteIntArrayCopy(prev_state->dims)));
TfLiteIntArray* concat_temp_size = TfLiteIntArrayCreate(2);
concat_temp_size->data[0] = num_batches;
concat_temp_size->data[1] = total_depth;
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, concat_temp, concat_temp_size));
TfLiteIntArray* activation_temp_size = TfLiteIntArrayCreate(2);
activation_temp_size->data[0] = num_batches;
activation_temp_size->data[1] = 4 * activation_depth;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, activation_temp,
activation_temp_size));
// Set the state tensors as persistent.
for (auto index : {kInputPrevActivation, kInputPrevState}) {
TfLiteTensor* tensor = &context->tensors[node->inputs->data[index]];
tensor->allocation_type = kTfLiteArenaRwPersistent;
}
return kTfLiteOk;
} | 543 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Read | The software reads data past the end, or before the beginning, of the intended buffer. | Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash. A crash can occur when the code reads a variable amount of data and assumes that a sentinel exists to stop the read operation, such as a NUL in a string. The expected sentinel might not be located in the out-of-bounds memory, causing excessive data to be read, leading to a segmentation fault or a buffer overflow. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent read operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/125.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::lstm::full::CheckInputTensorDimensions | tflite::ops::builtin::lstm::full::CheckInputTensorDimensions( TfLiteContext * context , TfLiteNode * node , int n_input , int n_output , int n_cell , bool use_layer_norm , bool is_integer) | ['context', 'node', 'n_input', 'n_output', 'n_cell', 'use_layer_norm', 'is_integer'] | TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
TfLiteNode* node, int n_input,
int n_output, int n_cell,
bool use_layer_norm, bool is_integer) {
const auto* params = static_cast<TfLiteLSTMParams*>(node->builtin_data);
// Making sure clipping parameters have valid values.
// == 0 means no clipping
// > 0 means clipping
TF_LITE_ENSURE(context, params->cell_clip >= 0);
TF_LITE_ENSURE(context, params->proj_clip >= 0);
const TfLiteTensor* input_to_forget_weights =
GetInput(context, node, kInputToForgetWeightsTensor);
TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[0], n_cell);
TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[1], n_input);
TF_LITE_ENSURE(context, (input_to_forget_weights->type == kTfLiteFloat32) ||
(input_to_forget_weights->type == kTfLiteUInt8) ||
(input_to_forget_weights->type == kTfLiteInt8));
const TfLiteTensor* input_to_input_weights =
GetOptionalInputTensor(context, node, kInputToInputWeightsTensor);
const bool use_cifg = (input_to_input_weights == nullptr);
if (!use_cifg) {
TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[0], n_cell);
TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[1], n_input);
TF_LITE_ENSURE_TYPES_EQ(context, input_to_input_weights->type,
input_to_forget_weights->type);
}
const TfLiteTensor* input_to_cell_weights =
GetInput(context, node, kInputToCellWeightsTensor);
TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[0], n_cell);
TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[1], n_input);
TF_LITE_ENSURE_TYPES_EQ(context, input_to_cell_weights->type,
input_to_forget_weights->type);
const TfLiteTensor* recurrent_to_input_weights =
GetOptionalInputTensor(context, node, kRecurrentToInputWeightsTensor);
if (recurrent_to_input_weights != nullptr) {
TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[0],
n_cell);
TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[1],
n_output);
TF_LITE_ENSURE_TYPES_EQ(context, recurrent_to_input_weights->type,
input_to_forget_weights->type);
}
const TfLiteTensor* recurrent_to_forget_weights =
GetInput(context, node, kRecurrentToForgetWeightsTensor);
TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[0],
n_cell);
TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[1],
n_output);
TF_LITE_ENSURE_TYPES_EQ(context, recurrent_to_forget_weights->type,
input_to_forget_weights->type);
const TfLiteTensor* recurrent_to_cell_weights =
GetInput(context, node, kRecurrentToCellWeightsTensor);
TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[0], n_cell);
TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[1],
n_output);
TF_LITE_ENSURE_TYPES_EQ(context, recurrent_to_cell_weights->type,
input_to_forget_weights->type);
// We make sure the input-gate's parameters are either both present (regular
// LSTM) or not at all (CIFG-LSTM).
const bool cifg_weights_all_or_none =
((input_to_input_weights != nullptr) &&
(recurrent_to_input_weights != nullptr)) ||
((input_to_input_weights == nullptr) &&
(recurrent_to_input_weights == nullptr));
TF_LITE_ENSURE(context, cifg_weights_all_or_none == true);
const TfLiteTensor* cell_to_input_weights =
GetOptionalInputTensor(context, node, kCellToInputWeightsTensor);
if (cell_to_input_weights) {
TF_LITE_ENSURE_EQ(context, cell_to_input_weights->dims->size, 1);
TF_LITE_ENSURE_EQ(context, cell_to_input_weights->dims->data[0], n_cell);
TF_LITE_ENSURE_TYPES_EQ(
context, cell_to_input_weights->type,
is_integer ? kTfLiteInt16 : input_to_forget_weights->type);
}
const TfLiteTensor* cell_to_forget_weights =
GetOptionalInputTensor(context, node, kCellToForgetWeightsTensor);
if (cell_to_forget_weights) {
TF_LITE_ENSURE_EQ(context, cell_to_forget_weights->dims->size, 1);
TF_LITE_ENSURE_EQ(context, cell_to_forget_weights->dims->data[0], n_cell);
TF_LITE_ENSURE_TYPES_EQ(
context, cell_to_forget_weights->type,
is_integer ? kTfLiteInt16 : input_to_forget_weights->type);
}
const TfLiteTensor* cell_to_output_weights =
GetOptionalInputTensor(context, node, kCellToOutputWeightsTensor);
if (cell_to_output_weights) {
TF_LITE_ENSURE_EQ(context, cell_to_output_weights->dims->size, 1);
TF_LITE_ENSURE_EQ(context, cell_to_output_weights->dims->data[0], n_cell);
TF_LITE_ENSURE_TYPES_EQ(
context, cell_to_output_weights->type,
is_integer ? kTfLiteInt16 : input_to_forget_weights->type);
}
// Making sure the peephole weights are there all or none.
const bool peephole_weights_all_or_none =
((cell_to_input_weights != nullptr || use_cifg) &&
(cell_to_forget_weights != nullptr) &&
(cell_to_output_weights != nullptr)) ||
((cell_to_input_weights == nullptr) &&
(cell_to_forget_weights == nullptr) &&
(cell_to_output_weights == nullptr));
TF_LITE_ENSURE(context, peephole_weights_all_or_none == true);
// Make sure the input gate bias is present only when not a CIFG-LSTM.
const TfLiteTensor* input_gate_bias =
GetOptionalInputTensor(context, node, kInputGateBiasTensor);
if (use_cifg) {
TF_LITE_ENSURE_EQ(context, input_gate_bias, nullptr);
} else {
TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->size, 1);
TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->data[0], n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, input_gate_bias->type, kTfLiteInt32);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, input_gate_bias->type, kTfLiteFloat32);
}
}
const TfLiteTensor* forget_gate_bias =
GetInput(context, node, kForgetGateBiasTensor);
TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->size, 1);
TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->data[0], n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, forget_gate_bias->type, kTfLiteInt32);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, forget_gate_bias->type, kTfLiteFloat32);
}
const TfLiteTensor* cell_gate_bias =
GetInput(context, node, kCellGateBiasTensor);
TF_LITE_ENSURE_EQ(context, cell_gate_bias->dims->size, 1);
TF_LITE_ENSURE_EQ(context, cell_gate_bias->dims->data[0], n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, cell_gate_bias->type, kTfLiteInt32);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, cell_gate_bias->type, kTfLiteFloat32);
}
const TfLiteTensor* output_gate_bias =
GetInput(context, node, kOutputGateBiasTensor);
TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->size, 1);
TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->data[0], n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, output_gate_bias->type, kTfLiteInt32);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, output_gate_bias->type, kTfLiteFloat32);
}
const TfLiteTensor* projection_weights =
GetOptionalInputTensor(context, node, kProjectionWeightsTensor);
if (projection_weights != nullptr) {
TF_LITE_ENSURE_EQ(context, projection_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[0], n_output);
TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[1], n_cell);
TF_LITE_ENSURE_TYPES_EQ(context, projection_weights->type,
input_to_forget_weights->type);
}
const TfLiteTensor* projection_bias =
GetOptionalInputTensor(context, node, kProjectionBiasTensor);
if (projection_bias != nullptr) {
TF_LITE_ENSURE_EQ(context, projection_bias->dims->size, 1);
TF_LITE_ENSURE_EQ(context, projection_bias->dims->data[0], n_output);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, projection_bias->type, kTfLiteInt32);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, projection_bias->type, kTfLiteFloat32);
}
}
// Making sure the projection tensors are consistent:
// 1) If projection weight is not present, then projection bias should not be
// present.
// 2) If projection weight is present, then projection bias is optional.
// TODO(ghodrat): make sure this is correct.
const bool projection_tensors_consistent =
((projection_weights != nullptr) || (projection_bias == nullptr));
TF_LITE_ENSURE(context, projection_tensors_consistent == true);
if (use_layer_norm) {
const TfLiteTensor* input_layer_norm_coefficients = GetOptionalInputTensor(
context, node, kInputLayerNormCoefficientsTensor);
if (use_cifg) {
TF_LITE_ENSURE_EQ(context, input_layer_norm_coefficients, nullptr);
} else {
TF_LITE_ENSURE(context, input_layer_norm_coefficients != nullptr);
TF_LITE_ENSURE_EQ(context, input_layer_norm_coefficients->dims->size, 1);
TF_LITE_ENSURE_EQ(context, input_layer_norm_coefficients->dims->data[0],
n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, input_layer_norm_coefficients->type,
kTfLiteInt16);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, input_layer_norm_coefficients->type,
kTfLiteFloat32);
}
}
const TfLiteTensor* forget_layer_norm_coefficients = GetOptionalInputTensor(
context, node, kForgetLayerNormCoefficientsTensor);
TF_LITE_ENSURE(context, forget_layer_norm_coefficients != nullptr);
TF_LITE_ENSURE_EQ(context, forget_layer_norm_coefficients->dims->size, 1);
TF_LITE_ENSURE_EQ(context, forget_layer_norm_coefficients->dims->data[0],
n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, forget_layer_norm_coefficients->type,
kTfLiteInt16);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, forget_layer_norm_coefficients->type,
kTfLiteFloat32);
}
const TfLiteTensor* cell_layer_norm_coefficients =
GetOptionalInputTensor(context, node, kCellLayerNormCoefficientsTensor);
TF_LITE_ENSURE(context, cell_layer_norm_coefficients != nullptr);
TF_LITE_ENSURE_EQ(context, cell_layer_norm_coefficients->dims->size, 1);
TF_LITE_ENSURE_EQ(context, cell_layer_norm_coefficients->dims->data[0],
n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, cell_layer_norm_coefficients->type,
kTfLiteInt16);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, cell_layer_norm_coefficients->type,
kTfLiteFloat32);
}
const TfLiteTensor* output_layer_norm_coefficients = GetOptionalInputTensor(
context, node, kOutputLayerNormCoefficientsTensor);
TF_LITE_ENSURE(context, output_layer_norm_coefficients != nullptr);
TF_LITE_ENSURE_EQ(context, output_layer_norm_coefficients->dims->size, 1);
TF_LITE_ENSURE_EQ(context, output_layer_norm_coefficients->dims->data[0],
n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, output_layer_norm_coefficients->type,
kTfLiteInt16);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, output_layer_norm_coefficients->type,
kTfLiteFloat32);
}
}
return kTfLiteOk;
} | 1694 | True | 1 |
CVE-2020-15211 | False | False | False | False | AV:N/AC:M/Au:N/C:P/I:P/A:N | NETWORK | MEDIUM | NONE | PARTIAL | PARTIAL | NONE | 5.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N | NETWORK | HIGH | NONE | NONE | UNCHANGED | LOW | LOW | NONE | 4.8 | MEDIUM | 2.2 | 2.5 | False | [{'url': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'name': 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'name': 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'name': 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'name': 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'name': 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'refsource': 'CONFIRM', 'tags': ['Exploit', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'name': 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'name': 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'refsource': 'MISC', 'tags': ['Third Party Advisory']}, {'url': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'name': 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'refsource': 'MISC', 'tags': ['Patch', 'Third Party Advisory']}, {'url': 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'name': 'openSUSE-SU-2020:1766', 'refsource': 'SUSE', 'tags': ['Mailing List', 'Third Party Advisory']}] | [{'description': [{'lang': 'en', 'value': 'CWE-125'}, {'lang': 'en', 'value': 'CWE-787'}]}] | MEDIUM | [{'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionEndExcluding': '1.15.4', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.0.0', 'versionEndExcluding': '2.0.3', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.1.0', 'versionEndExcluding': '2.1.2', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.2.0', 'versionEndExcluding': '2.2.1', 'cpe_name': []}, {'vulnerable': True, 'cpe23Uri': 'cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*', 'versionStartIncluding': '2.3.0', 'versionEndExcluding': '2.3.1', 'cpe_name': []}]}, {'operator': 'OR', 'children': [], 'cpe_match': [{'vulnerable': True, 'cpe23Uri': 'cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*', 'cpe_name': []}]}] | [{'lang': 'en', 'value': "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."}] | 2021-09-16T15:45Z | 2020-09-25T19:15Z | Out-of-bounds Write | The software writes data past the end, or before the beginning, of the intended buffer. | Typically, this can result in corruption of data, a crash, or code execution. The software may modify an index or perform pointer arithmetic that references a memory location that is outside of the boundaries of the buffer. A subsequent write operation then produces undefined or unexpected results.
| https://cwe.mitre.org/data/definitions/787.html | 0 | Mihai Maruseac | 2020-09-18 13:56:43-07:00 | [tflite]: Insert `nullptr` checks when obtaining tensors.
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.
We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).
PiperOrigin-RevId: 332521299
Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56 | 1970c2158b1ffa416d159d03c3370b9a462aee35 | False | tensorflow/tensorflow | An Open Source Machine Learning Framework for Everyone | 2015-11-07 01:19:20 | 2022-08-27 17:32:40 | https://tensorflow.org | tensorflow | 167391.0 | 87115.0 | tflite::ops::builtin::lstm::full::CheckInputTensorDimensions | tflite::ops::builtin::lstm::full::CheckInputTensorDimensions( TfLiteContext * context , TfLiteNode * node , int n_input , int n_output , int n_cell , bool use_layer_norm , bool is_integer) | ['context', 'node', 'n_input', 'n_output', 'n_cell', 'use_layer_norm', 'is_integer'] | TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
TfLiteNode* node, int n_input,
int n_output, int n_cell,
bool use_layer_norm, bool is_integer) {
const auto* params = static_cast<TfLiteLSTMParams*>(node->builtin_data);
// Making sure clipping parameters have valid values.
// == 0 means no clipping
// > 0 means clipping
TF_LITE_ENSURE(context, params->cell_clip >= 0);
TF_LITE_ENSURE(context, params->proj_clip >= 0);
const TfLiteTensor* input_to_forget_weights =
GetInput(context, node, kInputToForgetWeightsTensor);
TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[0], n_cell);
TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[1], n_input);
TF_LITE_ENSURE(context, (input_to_forget_weights->type == kTfLiteFloat32) ||
(input_to_forget_weights->type == kTfLiteUInt8) ||
(input_to_forget_weights->type == kTfLiteInt8));
const TfLiteTensor* input_to_input_weights =
GetOptionalInputTensor(context, node, kInputToInputWeightsTensor);
const bool use_cifg = (input_to_input_weights == nullptr);
if (!use_cifg) {
TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[0], n_cell);
TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[1], n_input);
TF_LITE_ENSURE_TYPES_EQ(context, input_to_input_weights->type,
input_to_forget_weights->type);
}
const TfLiteTensor* input_to_cell_weights =
GetInput(context, node, kInputToCellWeightsTensor);
TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[0], n_cell);
TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[1], n_input);
TF_LITE_ENSURE_TYPES_EQ(context, input_to_cell_weights->type,
input_to_forget_weights->type);
const TfLiteTensor* recurrent_to_input_weights =
GetOptionalInputTensor(context, node, kRecurrentToInputWeightsTensor);
if (recurrent_to_input_weights != nullptr) {
TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[0],
n_cell);
TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[1],
n_output);
TF_LITE_ENSURE_TYPES_EQ(context, recurrent_to_input_weights->type,
input_to_forget_weights->type);
}
const TfLiteTensor* recurrent_to_forget_weights =
GetInput(context, node, kRecurrentToForgetWeightsTensor);
TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[0],
n_cell);
TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[1],
n_output);
TF_LITE_ENSURE_TYPES_EQ(context, recurrent_to_forget_weights->type,
input_to_forget_weights->type);
const TfLiteTensor* recurrent_to_cell_weights =
GetInput(context, node, kRecurrentToCellWeightsTensor);
TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[0], n_cell);
TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[1],
n_output);
TF_LITE_ENSURE_TYPES_EQ(context, recurrent_to_cell_weights->type,
input_to_forget_weights->type);
// We make sure the input-gate's parameters are either both present (regular
// LSTM) or not at all (CIFG-LSTM).
const bool cifg_weights_all_or_none =
((input_to_input_weights != nullptr) &&
(recurrent_to_input_weights != nullptr)) ||
((input_to_input_weights == nullptr) &&
(recurrent_to_input_weights == nullptr));
TF_LITE_ENSURE(context, cifg_weights_all_or_none == true);
const TfLiteTensor* cell_to_input_weights =
GetOptionalInputTensor(context, node, kCellToInputWeightsTensor);
if (cell_to_input_weights) {
TF_LITE_ENSURE_EQ(context, cell_to_input_weights->dims->size, 1);
TF_LITE_ENSURE_EQ(context, cell_to_input_weights->dims->data[0], n_cell);
TF_LITE_ENSURE_TYPES_EQ(
context, cell_to_input_weights->type,
is_integer ? kTfLiteInt16 : input_to_forget_weights->type);
}
const TfLiteTensor* cell_to_forget_weights =
GetOptionalInputTensor(context, node, kCellToForgetWeightsTensor);
if (cell_to_forget_weights) {
TF_LITE_ENSURE_EQ(context, cell_to_forget_weights->dims->size, 1);
TF_LITE_ENSURE_EQ(context, cell_to_forget_weights->dims->data[0], n_cell);
TF_LITE_ENSURE_TYPES_EQ(
context, cell_to_forget_weights->type,
is_integer ? kTfLiteInt16 : input_to_forget_weights->type);
}
const TfLiteTensor* cell_to_output_weights =
GetOptionalInputTensor(context, node, kCellToOutputWeightsTensor);
if (cell_to_output_weights) {
TF_LITE_ENSURE_EQ(context, cell_to_output_weights->dims->size, 1);
TF_LITE_ENSURE_EQ(context, cell_to_output_weights->dims->data[0], n_cell);
TF_LITE_ENSURE_TYPES_EQ(
context, cell_to_output_weights->type,
is_integer ? kTfLiteInt16 : input_to_forget_weights->type);
}
// Making sure the peephole weights are there all or none.
const bool peephole_weights_all_or_none =
((cell_to_input_weights != nullptr || use_cifg) &&
(cell_to_forget_weights != nullptr) &&
(cell_to_output_weights != nullptr)) ||
((cell_to_input_weights == nullptr) &&
(cell_to_forget_weights == nullptr) &&
(cell_to_output_weights == nullptr));
TF_LITE_ENSURE(context, peephole_weights_all_or_none == true);
// Make sure the input gate bias is present only when not a CIFG-LSTM.
const TfLiteTensor* input_gate_bias =
GetOptionalInputTensor(context, node, kInputGateBiasTensor);
if (use_cifg) {
TF_LITE_ENSURE_EQ(context, input_gate_bias, nullptr);
} else {
TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->size, 1);
TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->data[0], n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, input_gate_bias->type, kTfLiteInt32);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, input_gate_bias->type, kTfLiteFloat32);
}
}
const TfLiteTensor* forget_gate_bias =
GetInput(context, node, kForgetGateBiasTensor);
TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->size, 1);
TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->data[0], n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, forget_gate_bias->type, kTfLiteInt32);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, forget_gate_bias->type, kTfLiteFloat32);
}
const TfLiteTensor* cell_gate_bias =
GetInput(context, node, kCellGateBiasTensor);
TF_LITE_ENSURE_EQ(context, cell_gate_bias->dims->size, 1);
TF_LITE_ENSURE_EQ(context, cell_gate_bias->dims->data[0], n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, cell_gate_bias->type, kTfLiteInt32);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, cell_gate_bias->type, kTfLiteFloat32);
}
const TfLiteTensor* output_gate_bias =
GetInput(context, node, kOutputGateBiasTensor);
TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->size, 1);
TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->data[0], n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, output_gate_bias->type, kTfLiteInt32);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, output_gate_bias->type, kTfLiteFloat32);
}
const TfLiteTensor* projection_weights =
GetOptionalInputTensor(context, node, kProjectionWeightsTensor);
if (projection_weights != nullptr) {
TF_LITE_ENSURE_EQ(context, projection_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[0], n_output);
TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[1], n_cell);
TF_LITE_ENSURE_TYPES_EQ(context, projection_weights->type,
input_to_forget_weights->type);
}
const TfLiteTensor* projection_bias =
GetOptionalInputTensor(context, node, kProjectionBiasTensor);
if (projection_bias != nullptr) {
TF_LITE_ENSURE_EQ(context, projection_bias->dims->size, 1);
TF_LITE_ENSURE_EQ(context, projection_bias->dims->data[0], n_output);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, projection_bias->type, kTfLiteInt32);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, projection_bias->type, kTfLiteFloat32);
}
}
// Making sure the projection tensors are consistent:
// 1) If projection weight is not present, then projection bias should not be
// present.
// 2) If projection weight is present, then projection bias is optional.
// TODO(ghodrat): make sure this is correct.
const bool projection_tensors_consistent =
((projection_weights != nullptr) || (projection_bias == nullptr));
TF_LITE_ENSURE(context, projection_tensors_consistent == true);
if (use_layer_norm) {
const TfLiteTensor* input_layer_norm_coefficients = GetOptionalInputTensor(
context, node, kInputLayerNormCoefficientsTensor);
if (use_cifg) {
TF_LITE_ENSURE_EQ(context, input_layer_norm_coefficients, nullptr);
} else {
TF_LITE_ENSURE(context, input_layer_norm_coefficients != nullptr);
TF_LITE_ENSURE_EQ(context, input_layer_norm_coefficients->dims->size, 1);
TF_LITE_ENSURE_EQ(context, input_layer_norm_coefficients->dims->data[0],
n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, input_layer_norm_coefficients->type,
kTfLiteInt16);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, input_layer_norm_coefficients->type,
kTfLiteFloat32);
}
}
const TfLiteTensor* forget_layer_norm_coefficients = GetOptionalInputTensor(
context, node, kForgetLayerNormCoefficientsTensor);
TF_LITE_ENSURE(context, forget_layer_norm_coefficients != nullptr);
TF_LITE_ENSURE_EQ(context, forget_layer_norm_coefficients->dims->size, 1);
TF_LITE_ENSURE_EQ(context, forget_layer_norm_coefficients->dims->data[0],
n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, forget_layer_norm_coefficients->type,
kTfLiteInt16);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, forget_layer_norm_coefficients->type,
kTfLiteFloat32);
}
const TfLiteTensor* cell_layer_norm_coefficients =
GetOptionalInputTensor(context, node, kCellLayerNormCoefficientsTensor);
TF_LITE_ENSURE(context, cell_layer_norm_coefficients != nullptr);
TF_LITE_ENSURE_EQ(context, cell_layer_norm_coefficients->dims->size, 1);
TF_LITE_ENSURE_EQ(context, cell_layer_norm_coefficients->dims->data[0],
n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, cell_layer_norm_coefficients->type,
kTfLiteInt16);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, cell_layer_norm_coefficients->type,
kTfLiteFloat32);
}
const TfLiteTensor* output_layer_norm_coefficients = GetOptionalInputTensor(
context, node, kOutputLayerNormCoefficientsTensor);
TF_LITE_ENSURE(context, output_layer_norm_coefficients != nullptr);
TF_LITE_ENSURE_EQ(context, output_layer_norm_coefficients->dims->size, 1);
TF_LITE_ENSURE_EQ(context, output_layer_norm_coefficients->dims->data[0],
n_cell);
if (is_integer) {
TF_LITE_ENSURE_TYPES_EQ(context, output_layer_norm_coefficients->type,
kTfLiteInt16);
} else {
TF_LITE_ENSURE_TYPES_EQ(context, output_layer_norm_coefficients->type,
kTfLiteFloat32);
}
}
return kTfLiteOk;
} | 1694 | True | 1 |
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