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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::reverse::Prepare | tflite::ops::builtin::reverse::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, kInputTensor);
const TfLiteTensor* axis = GetInput(context, node, kAxisTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(axis), 1);
TF_LITE_ENSURE(context, NumDimensions(input) >= NumElements(axis));
if (input->type != kTfLiteInt32 && input->type != kTfLiteFloat32 &&
input->type != kTfLiteUInt8 && input->type != kTfLiteInt16 &&
input->type != kTfLiteInt64 && input->type != kTfLiteBool) {
context->ReportError(context, "Type '%s' is not supported by reverse.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
if (axis->type != kTfLiteInt32) {
context->ReportError(context, "Axis Type '%s' is not supported by reverse.",
TfLiteTypeGetName(axis->type));
return kTfLiteError;
}
// TODO(renjieliu): support multi-axis case.
if (NumElements(axis) > 1) {
context->ReportError(context, "Current does not support more than 1 axis.");
}
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TfLiteIntArray* output_shape = TfLiteIntArrayCopy(input->dims);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, input->type);
return context->ResizeTensor(context, output, output_shape);
} | 248 | 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::reverse::Prepare | tflite::ops::builtin::reverse::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, kInputTensor);
const TfLiteTensor* axis = GetInput(context, node, kAxisTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(axis), 1);
TF_LITE_ENSURE(context, NumDimensions(input) >= NumElements(axis));
if (input->type != kTfLiteInt32 && input->type != kTfLiteFloat32 &&
input->type != kTfLiteUInt8 && input->type != kTfLiteInt16 &&
input->type != kTfLiteInt64 && input->type != kTfLiteBool) {
context->ReportError(context, "Type '%s' is not supported by reverse.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
if (axis->type != kTfLiteInt32) {
context->ReportError(context, "Axis Type '%s' is not supported by reverse.",
TfLiteTypeGetName(axis->type));
return kTfLiteError;
}
// TODO(renjieliu): support multi-axis case.
if (NumElements(axis) > 1) {
context->ReportError(context, "Current does not support more than 1 axis.");
}
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TfLiteIntArray* output_shape = TfLiteIntArrayCopy(input->dims);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, input->type);
return context->ResizeTensor(context, output, output_shape);
} | 248 | 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::reverse_sequence::Eval | tflite::ops::builtin::reverse_sequence::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
switch (output->type) {
case kTfLiteFloat32: {
return ReverseSequenceHelper<float>(context, node);
}
case kTfLiteUInt8: {
return ReverseSequenceHelper<uint8_t>(context, node);
}
case kTfLiteInt16: {
return ReverseSequenceHelper<int16_t>(context, node);
}
case kTfLiteInt32: {
return ReverseSequenceHelper<int32_t>(context, node);
}
case kTfLiteInt64: {
return ReverseSequenceHelper<int64_t>(context, node);
}
default: {
context->ReportError(context,
"Type '%s' is not supported by reverse_sequence.",
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
}
return kTfLiteOk;
} // namespace | 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::reverse_sequence::Eval | tflite::ops::builtin::reverse_sequence::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
switch (output->type) {
case kTfLiteFloat32: {
return ReverseSequenceHelper<float>(context, node);
}
case kTfLiteUInt8: {
return ReverseSequenceHelper<uint8_t>(context, node);
}
case kTfLiteInt16: {
return ReverseSequenceHelper<int16_t>(context, node);
}
case kTfLiteInt32: {
return ReverseSequenceHelper<int32_t>(context, node);
}
case kTfLiteInt64: {
return ReverseSequenceHelper<int64_t>(context, node);
}
default: {
context->ReportError(context,
"Type '%s' is not supported by reverse_sequence.",
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
}
return kTfLiteOk;
} // namespace | 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::reverse_sequence::Prepare | tflite::ops::builtin::reverse_sequence::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, kInputTensor);
const TfLiteTensor* seq_lengths = GetInput(context, node, kSeqLengthsTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(seq_lengths), 1);
if (input->type != kTfLiteInt32 && input->type != kTfLiteFloat32 &&
input->type != kTfLiteUInt8 && input->type != kTfLiteInt16 &&
input->type != kTfLiteInt64) {
context->ReportError(context,
"Type '%s' is not supported by reverse_sequence.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
if (seq_lengths->type != kTfLiteInt32 && seq_lengths->type != kTfLiteInt64) {
context->ReportError(
context, "Seq_lengths type '%s' is not supported by reverse_sequence.",
TfLiteTypeGetName(seq_lengths->type));
return kTfLiteError;
}
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TfLiteIntArray* output_shape = TfLiteIntArrayCopy(input->dims);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, input->type);
return context->ResizeTensor(context, output, output_shape);
} | 213 | 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::reverse_sequence::Prepare | tflite::ops::builtin::reverse_sequence::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, kInputTensor);
const TfLiteTensor* seq_lengths = GetInput(context, node, kSeqLengthsTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(seq_lengths), 1);
if (input->type != kTfLiteInt32 && input->type != kTfLiteFloat32 &&
input->type != kTfLiteUInt8 && input->type != kTfLiteInt16 &&
input->type != kTfLiteInt64) {
context->ReportError(context,
"Type '%s' is not supported by reverse_sequence.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
if (seq_lengths->type != kTfLiteInt32 && seq_lengths->type != kTfLiteInt64) {
context->ReportError(
context, "Seq_lengths type '%s' is not supported by reverse_sequence.",
TfLiteTypeGetName(seq_lengths->type));
return kTfLiteError;
}
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TfLiteIntArray* output_shape = TfLiteIntArrayCopy(input->dims);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, input->type);
return context->ResizeTensor(context, output, output_shape);
} | 213 | 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::reverse_sequence::ReverseSequenceHelper | tflite::ops::builtin::reverse_sequence::ReverseSequenceHelper( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus ReverseSequenceHelper(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* seq_lengths_tensor =
GetInput(context, node, kSeqLengthsTensor);
switch (seq_lengths_tensor->type) {
case kTfLiteInt32: {
return ReverseSequenceImpl<T, int32_t>(context, node);
}
case kTfLiteInt64: {
return ReverseSequenceImpl<T, int64_t>(context, node);
}
default: {
context->ReportError(
context,
"Seq_lengths type '%s' is not supported by reverse_sequence.",
TfLiteTypeGetName(seq_lengths_tensor->type));
return kTfLiteError;
}
}
return kTfLiteOk;
} | 96 | 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::reverse_sequence::ReverseSequenceHelper | tflite::ops::builtin::reverse_sequence::ReverseSequenceHelper( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus ReverseSequenceHelper(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* seq_lengths_tensor =
GetInput(context, node, kSeqLengthsTensor);
switch (seq_lengths_tensor->type) {
case kTfLiteInt32: {
return ReverseSequenceImpl<T, int32_t>(context, node);
}
case kTfLiteInt64: {
return ReverseSequenceImpl<T, int64_t>(context, node);
}
default: {
context->ReportError(
context,
"Seq_lengths type '%s' is not supported by reverse_sequence.",
TfLiteTypeGetName(seq_lengths_tensor->type));
return kTfLiteError;
}
}
return kTfLiteOk;
} | 96 | 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::reverse_sequence::ReverseSequenceImpl | tflite::ops::builtin::reverse_sequence::ReverseSequenceImpl( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus ReverseSequenceImpl(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* seq_lengths_tensor =
GetInput(context, node, kSeqLengthsTensor);
const TS* seq_lengths = GetTensorData<TS>(seq_lengths_tensor);
auto* params =
reinterpret_cast<TfLiteReverseSequenceParams*>(node->builtin_data);
int seq_dim = params->seq_dim;
int batch_dim = params->batch_dim;
TF_LITE_ENSURE(context, seq_dim >= 0);
TF_LITE_ENSURE(context, batch_dim >= 0);
TF_LITE_ENSURE(context, seq_dim != batch_dim);
TF_LITE_ENSURE(context, seq_dim < NumDimensions(input));
TF_LITE_ENSURE(context, batch_dim < NumDimensions(input));
TF_LITE_ENSURE_EQ(context, SizeOfDimension(seq_lengths_tensor, 0),
SizeOfDimension(input, batch_dim));
for (int i = 0; i < NumDimensions(seq_lengths_tensor); ++i) {
TF_LITE_ENSURE(context, seq_lengths[i] <= SizeOfDimension(input, seq_dim));
}
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
reference_ops::ReverseSequence<T, TS>(
seq_lengths, seq_dim, batch_dim, GetTensorShape(input),
GetTensorData<T>(input), GetTensorShape(output),
GetTensorData<T>(output));
return kTfLiteOk;
} | 246 | 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::reverse_sequence::ReverseSequenceImpl | tflite::ops::builtin::reverse_sequence::ReverseSequenceImpl( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus ReverseSequenceImpl(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* seq_lengths_tensor =
GetInput(context, node, kSeqLengthsTensor);
const TS* seq_lengths = GetTensorData<TS>(seq_lengths_tensor);
auto* params =
reinterpret_cast<TfLiteReverseSequenceParams*>(node->builtin_data);
int seq_dim = params->seq_dim;
int batch_dim = params->batch_dim;
TF_LITE_ENSURE(context, seq_dim >= 0);
TF_LITE_ENSURE(context, batch_dim >= 0);
TF_LITE_ENSURE(context, seq_dim != batch_dim);
TF_LITE_ENSURE(context, seq_dim < NumDimensions(input));
TF_LITE_ENSURE(context, batch_dim < NumDimensions(input));
TF_LITE_ENSURE_EQ(context, SizeOfDimension(seq_lengths_tensor, 0),
SizeOfDimension(input, batch_dim));
for (int i = 0; i < NumDimensions(seq_lengths_tensor); ++i) {
TF_LITE_ENSURE(context, seq_lengths[i] <= SizeOfDimension(input, seq_dim));
}
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
reference_ops::ReverseSequence<T, TS>(
seq_lengths, seq_dim, batch_dim, GetTensorShape(input),
GetTensorData<T>(input), GetTensorShape(output),
GetTensorData<T>(output));
return kTfLiteOk;
} | 246 | 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::rfft2d::Eval | tflite::ops::custom::rfft2d::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* fft_length = GetInput(context, node, kFftLengthTensor);
const int32_t* fft_length_data = GetTensorData<int32_t>(fft_length);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (output->type != kTfLiteComplex64) {
context->ReportError(context,
"Type '%s' for output is not supported by rfft2d.",
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
// Resize the output tensor if the fft_length tensor is not constant.
// Otherwise, check if the output shape is correct.
if (!IsConstantTensor(fft_length)) {
TF_LITE_ENSURE_STATUS(ResizeOutputandTemporaryTensors(context, node));
} else {
int num_dims_output = NumDimensions(output);
const RuntimeShape output_shape = GetTensorShape(output);
TF_LITE_ENSURE_EQ(context, num_dims_output, NumDimensions(input));
TF_LITE_ENSURE(context, num_dims_output >= 2);
TF_LITE_ENSURE_EQ(context, output_shape.Dims(num_dims_output - 2),
fft_length_data[0]);
TF_LITE_ENSURE_EQ(context, output_shape.Dims(num_dims_output - 1),
fft_length_data[1] / 2 + 1);
}
return Rfft2dHelper(context, node);
} | 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::custom::rfft2d::Eval | tflite::ops::custom::rfft2d::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* fft_length = GetInput(context, node, kFftLengthTensor);
const int32_t* fft_length_data = GetTensorData<int32_t>(fft_length);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (output->type != kTfLiteComplex64) {
context->ReportError(context,
"Type '%s' for output is not supported by rfft2d.",
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
// Resize the output tensor if the fft_length tensor is not constant.
// Otherwise, check if the output shape is correct.
if (!IsConstantTensor(fft_length)) {
TF_LITE_ENSURE_STATUS(ResizeOutputandTemporaryTensors(context, node));
} else {
int num_dims_output = NumDimensions(output);
const RuntimeShape output_shape = GetTensorShape(output);
TF_LITE_ENSURE_EQ(context, num_dims_output, NumDimensions(input));
TF_LITE_ENSURE(context, num_dims_output >= 2);
TF_LITE_ENSURE_EQ(context, output_shape.Dims(num_dims_output - 2),
fft_length_data[0]);
TF_LITE_ENSURE_EQ(context, output_shape.Dims(num_dims_output - 1),
fft_length_data[1] / 2 + 1);
}
return Rfft2dHelper(context, node);
} | 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::custom::rfft2d::InitTemporaryTensors | tflite::ops::custom::rfft2d::InitTemporaryTensors( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | static TfLiteStatus InitTemporaryTensors(TfLiteContext* context,
TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
// The prepare function may be executed multiple times. But temporary tensors
// only need to be initiated once.
if (data->fft_integer_working_area_id != kTensorNotAllocated &&
data->fft_double_working_area_id != kTensorNotAllocated) {
return kTfLiteOk;
}
TfLiteIntArrayFree(node->temporaries);
// Create two temporary tensors.
node->temporaries = TfLiteIntArrayCreate(2);
int first_new_index;
TF_LITE_ENSURE_STATUS(context->AddTensors(context, 2, &first_new_index));
node->temporaries->data[kFftIntegerWorkingAreaTensor] = first_new_index;
data->fft_integer_working_area_id = first_new_index;
node->temporaries->data[kFftDoubleWorkingAreaTensor] = first_new_index + 1;
data->fft_double_working_area_id = first_new_index + 1;
// Set up FFT integer working area buffer.
TfLiteTensor* fft_integer_working_area =
GetTemporary(context, node, kFftIntegerWorkingAreaTensor);
fft_integer_working_area->type = kTfLiteInt32;
// If fft_length is not a constant tensor, fft_integer_working_area will be
// set to dynamic later in Prepare.
fft_integer_working_area->allocation_type = kTfLiteArenaRw;
// Set up FFT double working area buffer.
TfLiteTensor* fft_double_working_area =
GetTemporary(context, node, kFftDoubleWorkingAreaTensor);
// fft_double_working_area is a double tensor. Ideally, double should be
// added into tflite data types. However, since fft_double_working_area is a
// temporary tensor, and there are no ops having double input/output tensors
// in tflite at this point, adding double as a tflite data type may confuse
// users that double is supported. As a results, kTfLiteInt64 is used here
// for memory allocation. And it will be cast into double in Eval when being
// used.
fft_double_working_area->type = kTfLiteInt64;
// If fft_length is not a constant tensor, fft_double_working_area will be
// set to dynamic later in Prepare.
fft_double_working_area->allocation_type = kTfLiteArenaRw;
return kTfLiteOk;
} | 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::custom::rfft2d::InitTemporaryTensors | tflite::ops::custom::rfft2d::InitTemporaryTensors( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | static TfLiteStatus InitTemporaryTensors(TfLiteContext* context,
TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
// The prepare function may be executed multiple times. But temporary tensors
// only need to be initiated once.
if (data->fft_integer_working_area_id != kTensorNotAllocated &&
data->fft_double_working_area_id != kTensorNotAllocated) {
return kTfLiteOk;
}
TfLiteIntArrayFree(node->temporaries);
// Create two temporary tensors.
node->temporaries = TfLiteIntArrayCreate(2);
int first_new_index;
TF_LITE_ENSURE_STATUS(context->AddTensors(context, 2, &first_new_index));
node->temporaries->data[kFftIntegerWorkingAreaTensor] = first_new_index;
data->fft_integer_working_area_id = first_new_index;
node->temporaries->data[kFftDoubleWorkingAreaTensor] = first_new_index + 1;
data->fft_double_working_area_id = first_new_index + 1;
// Set up FFT integer working area buffer.
TfLiteTensor* fft_integer_working_area =
GetTemporary(context, node, kFftIntegerWorkingAreaTensor);
fft_integer_working_area->type = kTfLiteInt32;
// If fft_length is not a constant tensor, fft_integer_working_area will be
// set to dynamic later in Prepare.
fft_integer_working_area->allocation_type = kTfLiteArenaRw;
// Set up FFT double working area buffer.
TfLiteTensor* fft_double_working_area =
GetTemporary(context, node, kFftDoubleWorkingAreaTensor);
// fft_double_working_area is a double tensor. Ideally, double should be
// added into tflite data types. However, since fft_double_working_area is a
// temporary tensor, and there are no ops having double input/output tensors
// in tflite at this point, adding double as a tflite data type may confuse
// users that double is supported. As a results, kTfLiteInt64 is used here
// for memory allocation. And it will be cast into double in Eval when being
// used.
fft_double_working_area->type = kTfLiteInt64;
// If fft_length is not a constant tensor, fft_double_working_area will be
// set to dynamic later in Prepare.
fft_double_working_area->allocation_type = kTfLiteArenaRw;
return kTfLiteOk;
} | 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::custom::rfft2d::Prepare | tflite::ops::custom::rfft2d::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);
// Check type and shape of the input tensor
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TF_LITE_ENSURE(context, NumDimensions(input) >= 2);
if (input->type != kTfLiteFloat32) {
context->ReportError(context,
"Type '%s' for input is not supported by rfft2d.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
// Check type and shape of the fft_length tensor
const TfLiteTensor* fft_length = GetInput(context, node, kFftLengthTensor);
const RuntimeShape fft_length_shape = GetTensorShape(fft_length);
TF_LITE_ENSURE_EQ(context, NumDimensions(fft_length), 1);
TF_LITE_ENSURE_EQ(context, fft_length_shape.Dims(0), 2);
if (fft_length->type != kTfLiteInt32) {
context->ReportError(context,
"Type '%s' for fft_length is not supported by rfft2d.",
TfLiteTypeGetName(fft_length->type));
return kTfLiteError;
}
// Setup temporary tensors for fft computation.
TF_LITE_ENSURE_STATUS(InitTemporaryTensors(context, node));
// Set output type
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
output->type = kTfLiteComplex64;
// Exit early if fft_length is a non-const tensor. Set output tensor and
// temporary tensors to dynamic, so that their tensor sizes can be determined
// in Eval.
if (!IsConstantTensor(fft_length)) {
TfLiteTensor* fft_integer_working_area =
GetTemporary(context, node, kFftIntegerWorkingAreaTensor);
TfLiteTensor* fft_double_working_area =
GetTemporary(context, node, kFftDoubleWorkingAreaTensor);
SetTensorToDynamic(fft_integer_working_area);
SetTensorToDynamic(fft_double_working_area);
SetTensorToDynamic(output);
return kTfLiteOk;
}
TF_LITE_ENSURE_STATUS(ResizeOutputandTemporaryTensors(context, node));
return kTfLiteOk;
} | 265 | 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::rfft2d::Prepare | tflite::ops::custom::rfft2d::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);
// Check type and shape of the input tensor
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TF_LITE_ENSURE(context, NumDimensions(input) >= 2);
if (input->type != kTfLiteFloat32) {
context->ReportError(context,
"Type '%s' for input is not supported by rfft2d.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
// Check type and shape of the fft_length tensor
const TfLiteTensor* fft_length = GetInput(context, node, kFftLengthTensor);
const RuntimeShape fft_length_shape = GetTensorShape(fft_length);
TF_LITE_ENSURE_EQ(context, NumDimensions(fft_length), 1);
TF_LITE_ENSURE_EQ(context, fft_length_shape.Dims(0), 2);
if (fft_length->type != kTfLiteInt32) {
context->ReportError(context,
"Type '%s' for fft_length is not supported by rfft2d.",
TfLiteTypeGetName(fft_length->type));
return kTfLiteError;
}
// Setup temporary tensors for fft computation.
TF_LITE_ENSURE_STATUS(InitTemporaryTensors(context, node));
// Set output type
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
output->type = kTfLiteComplex64;
// Exit early if fft_length is a non-const tensor. Set output tensor and
// temporary tensors to dynamic, so that their tensor sizes can be determined
// in Eval.
if (!IsConstantTensor(fft_length)) {
TfLiteTensor* fft_integer_working_area =
GetTemporary(context, node, kFftIntegerWorkingAreaTensor);
TfLiteTensor* fft_double_working_area =
GetTemporary(context, node, kFftDoubleWorkingAreaTensor);
SetTensorToDynamic(fft_integer_working_area);
SetTensorToDynamic(fft_double_working_area);
SetTensorToDynamic(output);
return kTfLiteOk;
}
TF_LITE_ENSURE_STATUS(ResizeOutputandTemporaryTensors(context, node));
return kTfLiteOk;
} | 265 | 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::rfft2d::ResizeOutputandTemporaryTensors | tflite::ops::custom::rfft2d::ResizeOutputandTemporaryTensors( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus ResizeOutputandTemporaryTensors(TfLiteContext* context,
TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const int num_dims = NumDimensions(input);
TF_LITE_ENSURE(context, num_dims >= 2);
const TfLiteTensor* fft_length = GetInput(context, node, kFftLengthTensor);
const int32_t* fft_length_data = GetTensorData<int32_t>(fft_length);
// The lib, fft2d, can only handle fft_lengths of power of 2.
TF_LITE_ENSURE(context, IsPowerOfTwo(fft_length_data[0]));
TF_LITE_ENSURE(context, IsPowerOfTwo(fft_length_data[1]));
int fft_height, fft_width;
fft_height = fft_length_data[0];
fft_width = fft_length_data[1];
int fft_working_length = std::max(fft_height, fft_width / 2);
int half_fft_working_length = fft_working_length / 2;
// Resize output tensor.
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TfLiteIntArray* output_shape = TfLiteIntArrayCopy(input->dims);
output_shape->data[num_dims - 2] = fft_length_data[0];
output_shape->data[num_dims - 1] = fft_length_data[1] / 2 + 1;
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_shape));
// Resize temporary tensors, fft_integer_working_area.
TfLiteTensor* fft_integer_working_area =
GetTemporary(context, node, kFftIntegerWorkingAreaTensor);
TfLiteIntArray* fft_integer_working_area_shape = TfLiteIntArrayCreate(1);
fft_integer_working_area_shape->data[0] =
2 + static_cast<int>(sqrt(fft_working_length));
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, fft_integer_working_area,
fft_integer_working_area_shape));
// Resize temporary tensors, fft_double_working_area.
TfLiteTensor* fft_double_working_area =
GetTemporary(context, node, kFftDoubleWorkingAreaTensor);
TfLiteIntArray* fft_double_working_area_shape = TfLiteIntArrayCreate(1);
fft_double_working_area_shape->data[0] =
half_fft_working_length + fft_width / 4;
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, fft_double_working_area,
fft_double_working_area_shape));
return kTfLiteOk;
} | 315 | 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::rfft2d::ResizeOutputandTemporaryTensors | tflite::ops::custom::rfft2d::ResizeOutputandTemporaryTensors( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus ResizeOutputandTemporaryTensors(TfLiteContext* context,
TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const int num_dims = NumDimensions(input);
TF_LITE_ENSURE(context, num_dims >= 2);
const TfLiteTensor* fft_length = GetInput(context, node, kFftLengthTensor);
const int32_t* fft_length_data = GetTensorData<int32_t>(fft_length);
// The lib, fft2d, can only handle fft_lengths of power of 2.
TF_LITE_ENSURE(context, IsPowerOfTwo(fft_length_data[0]));
TF_LITE_ENSURE(context, IsPowerOfTwo(fft_length_data[1]));
int fft_height, fft_width;
fft_height = fft_length_data[0];
fft_width = fft_length_data[1];
int fft_working_length = std::max(fft_height, fft_width / 2);
int half_fft_working_length = fft_working_length / 2;
// Resize output tensor.
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TfLiteIntArray* output_shape = TfLiteIntArrayCopy(input->dims);
output_shape->data[num_dims - 2] = fft_length_data[0];
output_shape->data[num_dims - 1] = fft_length_data[1] / 2 + 1;
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_shape));
// Resize temporary tensors, fft_integer_working_area.
TfLiteTensor* fft_integer_working_area =
GetTemporary(context, node, kFftIntegerWorkingAreaTensor);
TfLiteIntArray* fft_integer_working_area_shape = TfLiteIntArrayCreate(1);
fft_integer_working_area_shape->data[0] =
2 + static_cast<int>(sqrt(fft_working_length));
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, fft_integer_working_area,
fft_integer_working_area_shape));
// Resize temporary tensors, fft_double_working_area.
TfLiteTensor* fft_double_working_area =
GetTemporary(context, node, kFftDoubleWorkingAreaTensor);
TfLiteIntArray* fft_double_working_area_shape = TfLiteIntArrayCreate(1);
fft_double_working_area_shape->data[0] =
half_fft_working_length + fft_width / 4;
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, fft_double_working_area,
fft_double_working_area_shape));
return kTfLiteOk;
} | 315 | 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::rfft2d::Rfft2dHelper | tflite::ops::custom::rfft2d::Rfft2dHelper( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Rfft2dHelper(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const float* input_data = GetTensorData<float>(input);
const TfLiteTensor* fft_length = GetInput(context, node, kFftLengthTensor);
const int32_t* fft_length_data = GetTensorData<int32_t>(fft_length);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
complex<float>* output_data = GetTensorData<complex<float>>(output);
int fft_height, fft_width;
fft_height = fft_length_data[0];
fft_width = fft_length_data[1];
// FFT is processed for every slice on the inner most 2 dimensions.
// Count the number of slices in the input tensor.
const RuntimeShape input_shape = GetTensorShape(input);
const int input_dims_count = input_shape.DimensionsCount();
const auto* input_dims_data = input_shape.DimsData();
int num_slices = 1;
for (int i = 0; i < input_dims_count - 2; ++i) {
num_slices *= input_dims_data[i];
}
int input_height = input_dims_data[input_dims_count - 2];
int input_width = input_dims_data[input_dims_count - 1];
int input_slice_size = input_height * input_width;
int output_slice_size = fft_height * (fft_width / 2 + 1);
// Create input/output buffer for FFT
double** fft_input_output = new double*[fft_height];
for (int i = 0; i < fft_height; ++i) {
fft_input_output[i] = new double[fft_width + 2];
}
// Get buffer for integer working area.
TfLiteTensor* fft_integer_working_area =
GetTemporary(context, node, kFftIntegerWorkingAreaTensor);
int* fft_integer_working_area_data =
GetTensorData<int>(fft_integer_working_area);
// Get buffer for double working area.
TfLiteTensor* fft_double_working_area =
GetTemporary(context, node, kFftDoubleWorkingAreaTensor);
// Get double value out of the memory of fft_double_working_area_data.
double* fft_double_working_area_data = reinterpret_cast<double*>(
GetTensorData<int64_t>(fft_double_working_area));
// Process every slice in the input buffer
for (int i = 0; i < num_slices; ++i) {
PrepareInputBuffer(input_data, input_height, input_width, fft_height,
fft_width, fft_input_output);
memset(fft_integer_working_area_data, 0, fft_integer_working_area->bytes);
memset(fft_double_working_area_data, 0, fft_double_working_area->bytes);
Rfft2dImpl(fft_height, fft_width, fft_input_output,
fft_integer_working_area_data, fft_double_working_area_data);
PrepareOutputBuffer(output_data, fft_height, fft_width, fft_input_output);
input_data += input_slice_size;
output_data += output_slice_size;
}
// Delete the input buffer
for (int i = 0; i < fft_height; ++i) {
delete[] fft_input_output[i];
}
delete[] fft_input_output;
return kTfLiteOk;
} | 431 | 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::rfft2d::Rfft2dHelper | tflite::ops::custom::rfft2d::Rfft2dHelper( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Rfft2dHelper(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const float* input_data = GetTensorData<float>(input);
const TfLiteTensor* fft_length = GetInput(context, node, kFftLengthTensor);
const int32_t* fft_length_data = GetTensorData<int32_t>(fft_length);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
complex<float>* output_data = GetTensorData<complex<float>>(output);
int fft_height, fft_width;
fft_height = fft_length_data[0];
fft_width = fft_length_data[1];
// FFT is processed for every slice on the inner most 2 dimensions.
// Count the number of slices in the input tensor.
const RuntimeShape input_shape = GetTensorShape(input);
const int input_dims_count = input_shape.DimensionsCount();
const auto* input_dims_data = input_shape.DimsData();
int num_slices = 1;
for (int i = 0; i < input_dims_count - 2; ++i) {
num_slices *= input_dims_data[i];
}
int input_height = input_dims_data[input_dims_count - 2];
int input_width = input_dims_data[input_dims_count - 1];
int input_slice_size = input_height * input_width;
int output_slice_size = fft_height * (fft_width / 2 + 1);
// Create input/output buffer for FFT
double** fft_input_output = new double*[fft_height];
for (int i = 0; i < fft_height; ++i) {
fft_input_output[i] = new double[fft_width + 2];
}
// Get buffer for integer working area.
TfLiteTensor* fft_integer_working_area =
GetTemporary(context, node, kFftIntegerWorkingAreaTensor);
int* fft_integer_working_area_data =
GetTensorData<int>(fft_integer_working_area);
// Get buffer for double working area.
TfLiteTensor* fft_double_working_area =
GetTemporary(context, node, kFftDoubleWorkingAreaTensor);
// Get double value out of the memory of fft_double_working_area_data.
double* fft_double_working_area_data = reinterpret_cast<double*>(
GetTensorData<int64_t>(fft_double_working_area));
// Process every slice in the input buffer
for (int i = 0; i < num_slices; ++i) {
PrepareInputBuffer(input_data, input_height, input_width, fft_height,
fft_width, fft_input_output);
memset(fft_integer_working_area_data, 0, fft_integer_working_area->bytes);
memset(fft_double_working_area_data, 0, fft_double_working_area->bytes);
Rfft2dImpl(fft_height, fft_width, fft_input_output,
fft_integer_working_area_data, fft_double_working_area_data);
PrepareOutputBuffer(output_data, fft_height, fft_width, fft_input_output);
input_data += input_slice_size;
output_data += output_slice_size;
}
// Delete the input buffer
for (int i = 0; i < fft_height; ++i) {
delete[] fft_input_output[i];
}
delete[] fft_input_output;
return kTfLiteOk;
} | 431 | 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::round::Eval | tflite::ops::builtin::round::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);
optimized_ops::Round(GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output), GetTensorData<float>(output));
return kTfLiteOk;
} | 73 | 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::round::Eval | tflite::ops::builtin::round::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);
optimized_ops::Round(GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output), GetTensorData<float>(output));
return kTfLiteOk;
} | 73 | 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::round::Prepare | tflite::ops::builtin::round::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::round::Prepare | tflite::ops::builtin::round::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::segment_sum::Eval | tflite::ops::builtin::segment_sum::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* data = GetInput(context, node, kInputDataTensor);
const TfLiteTensor* segment_ids =
GetInput(context, node, kInputSegmentIdsTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (IsDynamicTensor(output)) {
TF_LITE_ENSURE_OK(context,
ResizeOutputTensor(context, data, segment_ids, output));
}
#define TF_LITE_SEGMENT_SUM(dtype) \
reference_ops::SegmentSum<dtype>( \
GetTensorShape(data), GetTensorData<dtype>(data), \
GetTensorShape(segment_ids), GetTensorData<int32_t>(segment_ids), \
GetTensorShape(output), GetTensorData<dtype>(output));
switch (data->type) {
case kTfLiteInt32:
TF_LITE_SEGMENT_SUM(int32_t);
break;
case kTfLiteFloat32:
TF_LITE_SEGMENT_SUM(float);
break;
default:
context->ReportError(context,
"Currently SegmentSum doesn't support type: %s",
TfLiteTypeGetName(data->type));
return kTfLiteError;
}
#undef TF_LITE_SEGMENT_SUM
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::segment_sum::Eval | tflite::ops::builtin::segment_sum::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* data = GetInput(context, node, kInputDataTensor);
const TfLiteTensor* segment_ids =
GetInput(context, node, kInputSegmentIdsTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (IsDynamicTensor(output)) {
TF_LITE_ENSURE_OK(context,
ResizeOutputTensor(context, data, segment_ids, output));
}
#define TF_LITE_SEGMENT_SUM(dtype) \
reference_ops::SegmentSum<dtype>( \
GetTensorShape(data), GetTensorData<dtype>(data), \
GetTensorShape(segment_ids), GetTensorData<int32_t>(segment_ids), \
GetTensorShape(output), GetTensorData<dtype>(output));
switch (data->type) {
case kTfLiteInt32:
TF_LITE_SEGMENT_SUM(int32_t);
break;
case kTfLiteFloat32:
TF_LITE_SEGMENT_SUM(float);
break;
default:
context->ReportError(context,
"Currently SegmentSum doesn't support type: %s",
TfLiteTypeGetName(data->type));
return kTfLiteError;
}
#undef TF_LITE_SEGMENT_SUM
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::segment_sum::Prepare | tflite::ops::builtin::segment_sum::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* data = GetInput(context, node, kInputDataTensor);
const TfLiteTensor* segment_ids =
GetInput(context, node, kInputSegmentIdsTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE(context,
data->type == kTfLiteInt32 || data->type == kTfLiteFloat32);
TF_LITE_ENSURE_EQ(context, segment_ids->type, kTfLiteInt32);
if (!IsConstantTensor(data) || !IsConstantTensor(segment_ids)) {
SetTensorToDynamic(output);
return kTfLiteOk;
}
return ResizeOutputTensor(context, data, segment_ids, output);
} | 141 | 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::segment_sum::Prepare | tflite::ops::builtin::segment_sum::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* data = GetInput(context, node, kInputDataTensor);
const TfLiteTensor* segment_ids =
GetInput(context, node, kInputSegmentIdsTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE(context,
data->type == kTfLiteInt32 || data->type == kTfLiteFloat32);
TF_LITE_ENSURE_EQ(context, segment_ids->type, kTfLiteInt32);
if (!IsConstantTensor(data) || !IsConstantTensor(segment_ids)) {
SetTensorToDynamic(output);
return kTfLiteOk;
}
return ResizeOutputTensor(context, data, segment_ids, output);
} | 141 | 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::select::SelectEval | tflite::ops::builtin::select::SelectEval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus SelectEval(TfLiteContext* context, TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input_condition =
GetInput(context, node, kInputTensorCondition);
const TfLiteTensor* input_x = GetInput(context, node, kInputTensorX);
const TfLiteTensor* input_y = GetInput(context, node, kInputTensorY);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
#define TF_LITE_SELECT(type, op) \
reference_ops::op(GetTensorShape(input_condition), \
GetTensorData<bool>(input_condition), \
GetTensorShape(input_x), GetTensorData<type>(input_x), \
GetTensorShape(input_y), GetTensorData<type>(input_y), \
GetTensorShape(output), GetTensorData<type>(output));
#define TF_LITE_SWITCH(type, op) \
switch (type) { \
break; \
case kTfLiteBool: \
TF_LITE_SELECT(bool, op); \
break; \
case kTfLiteFloat32: \
TF_LITE_SELECT(float, op); \
break; \
case kTfLiteUInt8: \
TF_LITE_SELECT(uint8_t, op); \
break; \
case kTfLiteInt8: \
TF_LITE_SELECT(int8_t, op); \
break; \
case kTfLiteInt16: \
TF_LITE_SELECT(int16_t, op); \
break; \
case kTfLiteInt32: \
TF_LITE_SELECT(int32_t, op); \
break; \
case kTfLiteInt64: \
TF_LITE_SELECT(int64_t, op); \
break; \
default: \
context->ReportError(context, \
"Does not support type other than bool|float|int, " \
"got %d", \
type); \
return kTfLiteError; \
}
if (data->has_low_rank_input_condition) {
TF_LITE_SWITCH(input_x->type, RankOneSelect);
} else if (data->requires_broadcast) {
TF_LITE_SWITCH(input_x->type, BroadcastSelect4DSlow);
} else {
TF_LITE_SWITCH(input_x->type, Select);
}
#undef TF_LITE_SELECT
#undef TF_LITE_SWITCH
return kTfLiteOk;
} | 132 | 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::select::SelectEval | tflite::ops::builtin::select::SelectEval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus SelectEval(TfLiteContext* context, TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input_condition =
GetInput(context, node, kInputTensorCondition);
const TfLiteTensor* input_x = GetInput(context, node, kInputTensorX);
const TfLiteTensor* input_y = GetInput(context, node, kInputTensorY);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
#define TF_LITE_SELECT(type, op) \
reference_ops::op(GetTensorShape(input_condition), \
GetTensorData<bool>(input_condition), \
GetTensorShape(input_x), GetTensorData<type>(input_x), \
GetTensorShape(input_y), GetTensorData<type>(input_y), \
GetTensorShape(output), GetTensorData<type>(output));
#define TF_LITE_SWITCH(type, op) \
switch (type) { \
break; \
case kTfLiteBool: \
TF_LITE_SELECT(bool, op); \
break; \
case kTfLiteFloat32: \
TF_LITE_SELECT(float, op); \
break; \
case kTfLiteUInt8: \
TF_LITE_SELECT(uint8_t, op); \
break; \
case kTfLiteInt8: \
TF_LITE_SELECT(int8_t, op); \
break; \
case kTfLiteInt16: \
TF_LITE_SELECT(int16_t, op); \
break; \
case kTfLiteInt32: \
TF_LITE_SELECT(int32_t, op); \
break; \
case kTfLiteInt64: \
TF_LITE_SELECT(int64_t, op); \
break; \
default: \
context->ReportError(context, \
"Does not support type other than bool|float|int, " \
"got %d", \
type); \
return kTfLiteError; \
}
if (data->has_low_rank_input_condition) {
TF_LITE_SWITCH(input_x->type, RankOneSelect);
} else if (data->requires_broadcast) {
TF_LITE_SWITCH(input_x->type, BroadcastSelect4DSlow);
} else {
TF_LITE_SWITCH(input_x->type, Select);
}
#undef TF_LITE_SELECT
#undef TF_LITE_SWITCH
return kTfLiteOk;
} | 132 | 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::select::SelectPrepare | tflite::ops::builtin::select::SelectPrepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus SelectPrepare(TfLiteContext* context, TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input_condition =
GetInput(context, node, kInputTensorCondition);
const TfLiteTensor* input_x = GetInput(context, node, kInputTensorX);
const TfLiteTensor* input_y = GetInput(context, node, kInputTensorY);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// Input must be bool.
TF_LITE_ENSURE_TYPES_EQ(context, input_condition->type, kTfLiteBool);
TF_LITE_ENSURE_TYPES_EQ(context, input_x->type, input_y->type);
output->type = input_x->type;
bool same_shape = HaveSameShapes(input_condition, input_x) &&
HaveSameShapes(input_x, input_y);
TfLiteIntArray* output_size;
if (!same_shape) {
switch (kernel_type) {
case kVersionOne: {
bool is_input_condition_scalar = NumDimensions(input_condition) == 0;
bool has_rank_one_input_condition =
NumDimensions(input_condition) == 1 &&
SizeOfDimension(input_condition, 0) == SizeOfDimension(input_x, 0);
data->has_low_rank_input_condition =
is_input_condition_scalar || has_rank_one_input_condition;
TF_LITE_ENSURE(context, data->has_low_rank_input_condition);
output_size = TfLiteIntArrayCopy(input_x->dims);
// Input tensors must have the same type and size
TF_LITE_ENSURE(context, HaveSameShapes(input_x, input_y));
break;
}
case kVersionTwo: {
TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
context, input_condition, input_x,
input_y, &output_size));
data->requires_broadcast = true;
break;
}
default:
return kTfLiteError;
}
} else {
output_size = TfLiteIntArrayCopy(input_x->dims);
}
return context->ResizeTensor(context, output, output_size);
} | 312 | 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::select::SelectPrepare | tflite::ops::builtin::select::SelectPrepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus SelectPrepare(TfLiteContext* context, TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input_condition =
GetInput(context, node, kInputTensorCondition);
const TfLiteTensor* input_x = GetInput(context, node, kInputTensorX);
const TfLiteTensor* input_y = GetInput(context, node, kInputTensorY);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// Input must be bool.
TF_LITE_ENSURE_TYPES_EQ(context, input_condition->type, kTfLiteBool);
TF_LITE_ENSURE_TYPES_EQ(context, input_x->type, input_y->type);
output->type = input_x->type;
bool same_shape = HaveSameShapes(input_condition, input_x) &&
HaveSameShapes(input_x, input_y);
TfLiteIntArray* output_size;
if (!same_shape) {
switch (kernel_type) {
case kVersionOne: {
bool is_input_condition_scalar = NumDimensions(input_condition) == 0;
bool has_rank_one_input_condition =
NumDimensions(input_condition) == 1 &&
SizeOfDimension(input_condition, 0) == SizeOfDimension(input_x, 0);
data->has_low_rank_input_condition =
is_input_condition_scalar || has_rank_one_input_condition;
TF_LITE_ENSURE(context, data->has_low_rank_input_condition);
output_size = TfLiteIntArrayCopy(input_x->dims);
// Input tensors must have the same type and size
TF_LITE_ENSURE(context, HaveSameShapes(input_x, input_y));
break;
}
case kVersionTwo: {
TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
context, input_condition, input_x,
input_y, &output_size));
data->requires_broadcast = true;
break;
}
default:
return kTfLiteError;
}
} else {
output_size = TfLiteIntArrayCopy(input_x->dims);
}
return context->ResizeTensor(context, output, output_size);
} | 312 | 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::shape::Prepare | tflite::ops::builtin::shape::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);
auto* params = reinterpret_cast<TfLiteShapeParams*>(node->builtin_data);
switch (params->out_type) {
case kTfLiteInt32:
output->type = kTfLiteInt32;
break;
case kTfLiteInt64:
output->type = kTfLiteInt64;
break;
default:
context->ReportError(context, "Unknown shape output data type: %d",
params->out_type);
return kTfLiteError;
}
// By design, the input shape is always known at the time of Prepare, even
// if the preceding op that generates |input| is dynamic. Thus, we can
// always compute the shape immediately, without waiting for Eval.
SetTensorToPersistentRo(output);
// Shape always produces a 1-dimensional output tensor, where each output
// element is the length of the corresponding input tensor's dimension.
TfLiteIntArray* output_size = TfLiteIntArrayCreate(1);
output_size->data[0] = NumDimensions(input);
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_size));
TFLITE_DCHECK_EQ(NumDimensions(output), 1);
TFLITE_DCHECK_EQ(SizeOfDimension(output, 0), NumDimensions(input));
// Immediately propagate the known shape to the output tensor. This allows
// downstream ops that rely on the value to use it during prepare.
switch (output->type) {
case kTfLiteInt32:
ExtractShape(input, GetTensorData<int32_t>(output));
break;
case kTfLiteInt64:
ExtractShape(input, GetTensorData<int64_t>(output));
break;
default:
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::shape::Prepare | tflite::ops::builtin::shape::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);
auto* params = reinterpret_cast<TfLiteShapeParams*>(node->builtin_data);
switch (params->out_type) {
case kTfLiteInt32:
output->type = kTfLiteInt32;
break;
case kTfLiteInt64:
output->type = kTfLiteInt64;
break;
default:
context->ReportError(context, "Unknown shape output data type: %d",
params->out_type);
return kTfLiteError;
}
// By design, the input shape is always known at the time of Prepare, even
// if the preceding op that generates |input| is dynamic. Thus, we can
// always compute the shape immediately, without waiting for Eval.
SetTensorToPersistentRo(output);
// Shape always produces a 1-dimensional output tensor, where each output
// element is the length of the corresponding input tensor's dimension.
TfLiteIntArray* output_size = TfLiteIntArrayCreate(1);
output_size->data[0] = NumDimensions(input);
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_size));
TFLITE_DCHECK_EQ(NumDimensions(output), 1);
TFLITE_DCHECK_EQ(SizeOfDimension(output, 0), NumDimensions(input));
// Immediately propagate the known shape to the output tensor. This allows
// downstream ops that rely on the value to use it during prepare.
switch (output->type) {
case kTfLiteInt32:
ExtractShape(input, GetTensorData<int32_t>(output));
break;
case kTfLiteInt64:
ExtractShape(input, GetTensorData<int64_t>(output));
break;
default:
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::Eval | tflite::ops::builtin::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteSkipGramParams*>(node->builtin_data);
// Split sentence to words.
std::vector<StringRef> words;
tflite::StringRef strref = tflite::GetString(GetInput(context, node, 0), 0);
int prev_idx = 0;
for (int i = 1; i < strref.len; i++) {
if (isspace(*(strref.str + i))) {
if (i > prev_idx && !isspace(*(strref.str + prev_idx))) {
words.push_back({strref.str + prev_idx, i - prev_idx});
}
prev_idx = i + 1;
}
}
if (strref.len > prev_idx) {
words.push_back({strref.str + prev_idx, strref.len - prev_idx});
}
// Generate n-grams recursively.
tflite::DynamicBuffer buf;
if (words.size() < params->ngram_size) {
buf.WriteToTensorAsVector(GetOutput(context, node, 0));
return kTfLiteOk;
}
// Stack stores the index of word used to generate ngram.
// The size of stack is the size of ngram.
std::vector<int> stack(params->ngram_size, 0);
// Stack index that indicates which depth the recursion is operating at.
int stack_idx = 1;
int num_words = words.size();
while (stack_idx >= 0) {
if (ShouldStepInRecursion(params, stack, stack_idx, num_words)) {
// When current depth can fill with a new word
// and the new word is within the max range to skip,
// fill this word to stack, recurse into next depth.
stack[stack_idx]++;
stack_idx++;
if (stack_idx < params->ngram_size) {
stack[stack_idx] = stack[stack_idx - 1];
}
} else {
if (ShouldIncludeCurrentNgram(params, stack_idx)) {
// Add n-gram to tensor buffer when the stack has filled with enough
// words to generate the ngram.
std::vector<StringRef> gram(stack_idx);
for (int i = 0; i < stack_idx; i++) {
gram[i] = words[stack[i]];
}
buf.AddJoinedString(gram, ' ');
}
// When current depth cannot fill with a valid new word,
// and not in last depth to generate ngram,
// step back to previous depth to iterate to next possible word.
stack_idx--;
}
}
buf.WriteToTensorAsVector(GetOutput(context, node, 0));
return kTfLiteOk;
} | 370 | 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<TfLiteSkipGramParams*>(node->builtin_data);
// Split sentence to words.
std::vector<StringRef> words;
tflite::StringRef strref = tflite::GetString(GetInput(context, node, 0), 0);
int prev_idx = 0;
for (int i = 1; i < strref.len; i++) {
if (isspace(*(strref.str + i))) {
if (i > prev_idx && !isspace(*(strref.str + prev_idx))) {
words.push_back({strref.str + prev_idx, i - prev_idx});
}
prev_idx = i + 1;
}
}
if (strref.len > prev_idx) {
words.push_back({strref.str + prev_idx, strref.len - prev_idx});
}
// Generate n-grams recursively.
tflite::DynamicBuffer buf;
if (words.size() < params->ngram_size) {
buf.WriteToTensorAsVector(GetOutput(context, node, 0));
return kTfLiteOk;
}
// Stack stores the index of word used to generate ngram.
// The size of stack is the size of ngram.
std::vector<int> stack(params->ngram_size, 0);
// Stack index that indicates which depth the recursion is operating at.
int stack_idx = 1;
int num_words = words.size();
while (stack_idx >= 0) {
if (ShouldStepInRecursion(params, stack, stack_idx, num_words)) {
// When current depth can fill with a new word
// and the new word is within the max range to skip,
// fill this word to stack, recurse into next depth.
stack[stack_idx]++;
stack_idx++;
if (stack_idx < params->ngram_size) {
stack[stack_idx] = stack[stack_idx - 1];
}
} else {
if (ShouldIncludeCurrentNgram(params, stack_idx)) {
// Add n-gram to tensor buffer when the stack has filled with enough
// words to generate the ngram.
std::vector<StringRef> gram(stack_idx);
for (int i = 0; i < stack_idx; i++) {
gram[i] = words[stack[i]];
}
buf.AddJoinedString(gram, ' ');
}
// When current depth cannot fill with a valid new word,
// and not in last depth to generate ngram,
// step back to previous depth to iterate to next possible word.
stack_idx--;
}
}
buf.WriteToTensorAsVector(GetOutput(context, node, 0));
return kTfLiteOk;
} | 370 | 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), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
TF_LITE_ENSURE_TYPES_EQ(context, GetInput(context, node, 0)->type,
kTfLiteString);
TF_LITE_ENSURE_TYPES_EQ(context, GetOutput(context, node, 0)->type,
kTfLiteString);
return kTfLiteOk;
} | 75 | 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), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
TF_LITE_ENSURE_TYPES_EQ(context, GetInput(context, node, 0)->type,
kTfLiteString);
TF_LITE_ENSURE_TYPES_EQ(context, GetOutput(context, node, 0)->type,
kTfLiteString);
return kTfLiteOk;
} | 75 | 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::slice::Eval | tflite::ops::builtin::slice::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* begin = GetInput(context, node, kBeginTensor);
const TfLiteTensor* size = GetInput(context, node, kSizeTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (IsDynamicTensor(output)) {
TF_LITE_ENSURE_OK(context,
ResizeOutputShape(context, input, begin, size, output));
}
std::vector<int> begins;
begins.reserve(kMaxDim);
std::vector<int> sizes;
sizes.reserve(kMaxDim);
for (int i = NumDimensions(input); i < kMaxDim; ++i) {
begins.push_back(0);
sizes.push_back(1);
}
if (begin->type == kTfLiteInt32) {
GetBeginAndSizeVectors<int32_t>(NumDimensions(input), begin, size, &begins,
&sizes);
} else if (begin->type == kTfLiteInt64) {
GetBeginAndSizeVectors<int64_t>(NumDimensions(input), begin, size, &begins,
&sizes);
} else {
context->ReportError(
context, "Type %d is currently not supported by Slice.", begin->type);
return kTfLiteError;
}
// The original Slice op implementation only accepted 4-D sizes. That
// constraint is, for the present, maintained here.
//
// The dimensions in the kernel used to be in reverse-order, and TFLite
// arranged the begins and sizes vectors accordingly. This macro incorporates
// the needed reversing.
#define TF_LITE_SLICE(data_type, kernel_type) \
{ \
TF_LITE_ENSURE_EQ(context, begins.size(), 4); \
TF_LITE_ENSURE_EQ(context, sizes.size(), 4); \
tflite::SliceParams op_params; \
op_params.begin_count = 4; \
op_params.size_count = 4; \
for (int i = 0; i < 4; ++i) { \
op_params.begin[i] = begins[i]; \
op_params.size[i] = sizes[i]; \
} \
\
if (kernel_type == kGenericOptimized) { \
optimized_ops::Slice<data_type>(op_params, GetTensorShape(input), input, \
GetTensorShape(output), output); \
} else { \
reference_ops::Slice<data_type>(op_params, GetTensorShape(input), input, \
GetTensorShape(output), output); \
} \
}
switch (input->type) {
case kTfLiteFloat32:
TF_LITE_SLICE(float, kernel_type);
break;
case kTfLiteInt32:
TF_LITE_SLICE(int32_t, kernel_type);
break;
case kTfLiteInt64:
TF_LITE_SLICE(int64_t, kernel_type);
break;
case kTfLiteInt8:
TF_LITE_SLICE(int8_t, kernel_type);
break;
case kTfLiteInt16:
TF_LITE_SLICE(int16_t, kernel_type);
break;
case kTfLiteUInt8:
TF_LITE_SLICE(uint8_t, kernel_type);
break;
case kTfLiteBool:
TF_LITE_SLICE(bool, kernel_type);
break;
case kTfLiteString:
TF_LITE_SLICE(string, kernel_type);
break;
default:
context->ReportError(
context, "Type %d is currently not supported by Slice.", input->type);
return kTfLiteError;
}
#undef TF_LITE_SLICE
return kTfLiteOk;
} | 364 | 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::slice::Eval | tflite::ops::builtin::slice::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* begin = GetInput(context, node, kBeginTensor);
const TfLiteTensor* size = GetInput(context, node, kSizeTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (IsDynamicTensor(output)) {
TF_LITE_ENSURE_OK(context,
ResizeOutputShape(context, input, begin, size, output));
}
std::vector<int> begins;
begins.reserve(kMaxDim);
std::vector<int> sizes;
sizes.reserve(kMaxDim);
for (int i = NumDimensions(input); i < kMaxDim; ++i) {
begins.push_back(0);
sizes.push_back(1);
}
if (begin->type == kTfLiteInt32) {
GetBeginAndSizeVectors<int32_t>(NumDimensions(input), begin, size, &begins,
&sizes);
} else if (begin->type == kTfLiteInt64) {
GetBeginAndSizeVectors<int64_t>(NumDimensions(input), begin, size, &begins,
&sizes);
} else {
context->ReportError(
context, "Type %d is currently not supported by Slice.", begin->type);
return kTfLiteError;
}
// The original Slice op implementation only accepted 4-D sizes. That
// constraint is, for the present, maintained here.
//
// The dimensions in the kernel used to be in reverse-order, and TFLite
// arranged the begins and sizes vectors accordingly. This macro incorporates
// the needed reversing.
#define TF_LITE_SLICE(data_type, kernel_type) \
{ \
TF_LITE_ENSURE_EQ(context, begins.size(), 4); \
TF_LITE_ENSURE_EQ(context, sizes.size(), 4); \
tflite::SliceParams op_params; \
op_params.begin_count = 4; \
op_params.size_count = 4; \
for (int i = 0; i < 4; ++i) { \
op_params.begin[i] = begins[i]; \
op_params.size[i] = sizes[i]; \
} \
\
if (kernel_type == kGenericOptimized) { \
optimized_ops::Slice<data_type>(op_params, GetTensorShape(input), input, \
GetTensorShape(output), output); \
} else { \
reference_ops::Slice<data_type>(op_params, GetTensorShape(input), input, \
GetTensorShape(output), output); \
} \
}
switch (input->type) {
case kTfLiteFloat32:
TF_LITE_SLICE(float, kernel_type);
break;
case kTfLiteInt32:
TF_LITE_SLICE(int32_t, kernel_type);
break;
case kTfLiteInt64:
TF_LITE_SLICE(int64_t, kernel_type);
break;
case kTfLiteInt8:
TF_LITE_SLICE(int8_t, kernel_type);
break;
case kTfLiteInt16:
TF_LITE_SLICE(int16_t, kernel_type);
break;
case kTfLiteUInt8:
TF_LITE_SLICE(uint8_t, kernel_type);
break;
case kTfLiteBool:
TF_LITE_SLICE(bool, kernel_type);
break;
case kTfLiteString:
TF_LITE_SLICE(string, kernel_type);
break;
default:
context->ReportError(
context, "Type %d is currently not supported by Slice.", input->type);
return kTfLiteError;
}
#undef TF_LITE_SLICE
return kTfLiteOk;
} | 364 | 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::slice::Prepare | tflite::ops::builtin::slice::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), 1);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* begin = GetInput(context, node, kBeginTensor);
const TfLiteTensor* size = GetInput(context, node, kSizeTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// Ensure validity of input tensor and its dimension.
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
TF_LITE_ENSURE(context,
begin->type == kTfLiteInt32 || begin->type == kTfLiteInt64);
TF_LITE_ENSURE(context,
size->type == kTfLiteInt32 || size->type == kTfLiteInt64);
TF_LITE_ENSURE_EQ(context, NumDimensions(begin), 1);
TF_LITE_ENSURE_EQ(context, NumDimensions(size), 1);
TF_LITE_ENSURE_EQ(context, NumElements(begin), NumElements(size));
TF_LITE_ENSURE_MSG(context, NumDimensions(input) <= kMaxDim,
"Slice op only supports 1D-4D input arrays.");
// Postpone allocation of output if any of the indexing tensors is not
// constant
if (!(IsConstantTensor(begin) && IsConstantTensor(size))) {
SetTensorToDynamic(output);
return kTfLiteOk;
}
return ResizeOutputShape(context, input, begin, size, output);
} | 230 | 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::slice::Prepare | tflite::ops::builtin::slice::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), 1);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* begin = GetInput(context, node, kBeginTensor);
const TfLiteTensor* size = GetInput(context, node, kSizeTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// Ensure validity of input tensor and its dimension.
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
TF_LITE_ENSURE(context,
begin->type == kTfLiteInt32 || begin->type == kTfLiteInt64);
TF_LITE_ENSURE(context,
size->type == kTfLiteInt32 || size->type == kTfLiteInt64);
TF_LITE_ENSURE_EQ(context, NumDimensions(begin), 1);
TF_LITE_ENSURE_EQ(context, NumDimensions(size), 1);
TF_LITE_ENSURE_EQ(context, NumElements(begin), NumElements(size));
TF_LITE_ENSURE_MSG(context, NumDimensions(input) <= kMaxDim,
"Slice op only supports 1D-4D input arrays.");
// Postpone allocation of output if any of the indexing tensors is not
// constant
if (!(IsConstantTensor(begin) && IsConstantTensor(size))) {
SetTensorToDynamic(output);
return kTfLiteOk;
}
return ResizeOutputShape(context, input, begin, size, output);
} | 230 | 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::space_to_depth::Eval | tflite::ops::builtin::space_to_depth::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteSpaceToDepthParams*>(node->builtin_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
#define TF_LITE_SPACE_TO_DEPTH(type, scalar) \
tflite::SpaceToDepthParams op_params; \
op_params.block_size = params->block_size; \
type::SpaceToDepth(op_params, GetTensorShape(input), \
GetTensorData<scalar>(input), GetTensorShape(output), \
GetTensorData<scalar>(output))
switch (input->type) { // Already know in/out types are same.
case kTfLiteFloat32:
if (kernel_type == kReference) {
TF_LITE_SPACE_TO_DEPTH(reference_ops, float);
} else {
TF_LITE_SPACE_TO_DEPTH(optimized_ops, float);
}
break;
case kTfLiteUInt8:
if (kernel_type == kReference) {
TF_LITE_SPACE_TO_DEPTH(reference_ops, uint8_t);
} else {
TF_LITE_SPACE_TO_DEPTH(optimized_ops, uint8_t);
}
break;
case kTfLiteInt8:
if (kernel_type == kReference) {
TF_LITE_SPACE_TO_DEPTH(reference_ops, int8_t);
} else {
TF_LITE_SPACE_TO_DEPTH(optimized_ops, int8_t);
}
break;
case kTfLiteInt32:
if (kernel_type == kReference) {
TF_LITE_SPACE_TO_DEPTH(reference_ops, int32_t);
} else {
TF_LITE_SPACE_TO_DEPTH(optimized_ops, int32_t);
}
break;
case kTfLiteInt64:
if (kernel_type == kReference) {
TF_LITE_SPACE_TO_DEPTH(reference_ops, int64_t);
} else {
TF_LITE_SPACE_TO_DEPTH(optimized_ops, int64_t);
}
break;
default:
context->ReportError(context, "Type '%s' not currently supported.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
#undef TF_LITE_SPACE_TO_DEPTH
return kTfLiteOk;
} | 236 | 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::space_to_depth::Eval | tflite::ops::builtin::space_to_depth::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteSpaceToDepthParams*>(node->builtin_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
#define TF_LITE_SPACE_TO_DEPTH(type, scalar) \
tflite::SpaceToDepthParams op_params; \
op_params.block_size = params->block_size; \
type::SpaceToDepth(op_params, GetTensorShape(input), \
GetTensorData<scalar>(input), GetTensorShape(output), \
GetTensorData<scalar>(output))
switch (input->type) { // Already know in/out types are same.
case kTfLiteFloat32:
if (kernel_type == kReference) {
TF_LITE_SPACE_TO_DEPTH(reference_ops, float);
} else {
TF_LITE_SPACE_TO_DEPTH(optimized_ops, float);
}
break;
case kTfLiteUInt8:
if (kernel_type == kReference) {
TF_LITE_SPACE_TO_DEPTH(reference_ops, uint8_t);
} else {
TF_LITE_SPACE_TO_DEPTH(optimized_ops, uint8_t);
}
break;
case kTfLiteInt8:
if (kernel_type == kReference) {
TF_LITE_SPACE_TO_DEPTH(reference_ops, int8_t);
} else {
TF_LITE_SPACE_TO_DEPTH(optimized_ops, int8_t);
}
break;
case kTfLiteInt32:
if (kernel_type == kReference) {
TF_LITE_SPACE_TO_DEPTH(reference_ops, int32_t);
} else {
TF_LITE_SPACE_TO_DEPTH(optimized_ops, int32_t);
}
break;
case kTfLiteInt64:
if (kernel_type == kReference) {
TF_LITE_SPACE_TO_DEPTH(reference_ops, int64_t);
} else {
TF_LITE_SPACE_TO_DEPTH(optimized_ops, int64_t);
}
break;
default:
context->ReportError(context, "Type '%s' not currently supported.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
#undef TF_LITE_SPACE_TO_DEPTH
return kTfLiteOk;
} | 236 | 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::space_to_depth::Prepare | tflite::ops::builtin::space_to_depth::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteSpaceToDepthParams*>(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];
int output_height = input_height / block_size;
int output_width = input_width / block_size;
TF_LITE_ENSURE_EQ(context, input_height, output_height * block_size);
TF_LITE_ENSURE_EQ(context, input_width, output_width * 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] = input->dims->data[3] * block_size * block_size;
return context->ResizeTensor(context, output, output_size);
} | 280 | 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::space_to_depth::Prepare | tflite::ops::builtin::space_to_depth::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteSpaceToDepthParams*>(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];
int output_height = input_height / block_size;
int output_width = input_width / block_size;
TF_LITE_ENSURE_EQ(context, input_height, output_height * block_size);
TF_LITE_ENSURE_EQ(context, input_width, output_width * 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] = input->dims->data[3] * block_size * block_size;
return context->ResizeTensor(context, output, output_size);
} | 280 | 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::split::Prepare | tflite::ops::builtin::split::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
OpContext op_context(context, node);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), op_context.params->num_splits);
auto input_type = op_context.input->type;
TF_LITE_ENSURE(context,
input_type == kTfLiteFloat32 || input_type == kTfLiteUInt8 ||
input_type == kTfLiteInt8 || input_type == kTfLiteInt16 ||
input_type == kTfLiteInt32);
for (int i = 0; i < NumOutputs(node); ++i) {
GetOutput(context, node, i)->type = input_type;
}
// If we know the contents of the 'axis' tensor, resize all outputs.
// Otherwise, wait until Eval().
if (IsConstantTensor(op_context.axis)) {
return ResizeOutputTensors(context, node, op_context.axis, op_context.input,
op_context.params->num_splits);
} else {
return UseDynamicOutputTensors(context, node);
}
} | 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::split::Prepare | tflite::ops::builtin::split::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
OpContext op_context(context, node);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), op_context.params->num_splits);
auto input_type = op_context.input->type;
TF_LITE_ENSURE(context,
input_type == kTfLiteFloat32 || input_type == kTfLiteUInt8 ||
input_type == kTfLiteInt8 || input_type == kTfLiteInt16 ||
input_type == kTfLiteInt32);
for (int i = 0; i < NumOutputs(node); ++i) {
GetOutput(context, node, i)->type = input_type;
}
// If we know the contents of the 'axis' tensor, resize all outputs.
// Otherwise, wait until Eval().
if (IsConstantTensor(op_context.axis)) {
return ResizeOutputTensors(context, node, op_context.axis, op_context.input,
op_context.params->num_splits);
} else {
return UseDynamicOutputTensors(context, node);
}
} | 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::split::ResizeOutputTensors | tflite::ops::builtin::split::ResizeOutputTensors( TfLiteContext * context , TfLiteNode * node , const TfLiteTensor * axis , const TfLiteTensor * input , int num_splits) | ['context', 'node', 'axis', 'input', 'num_splits'] | TfLiteStatus ResizeOutputTensors(TfLiteContext* context, TfLiteNode* node,
const TfLiteTensor* axis,
const TfLiteTensor* input, int num_splits) {
int axis_value = GetTensorData<int>(axis)[0];
if (axis_value < 0) {
axis_value += NumDimensions(input);
}
TF_LITE_ENSURE(context, axis_value >= 0);
TF_LITE_ENSURE(context, axis_value < NumDimensions(input));
const int input_size = SizeOfDimension(input, axis_value);
TF_LITE_ENSURE_MSG(context, input_size % num_splits == 0,
"Not an even split");
const int slice_size = input_size / num_splits;
for (int i = 0; i < NumOutputs(node); ++i) {
TfLiteIntArray* output_dims = TfLiteIntArrayCopy(input->dims);
output_dims->data[axis_value] = slice_size;
TfLiteTensor* output = GetOutput(context, node, i);
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_dims));
}
return kTfLiteOk;
} | 176 | 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::split::ResizeOutputTensors | tflite::ops::builtin::split::ResizeOutputTensors( TfLiteContext * context , TfLiteNode * node , const TfLiteTensor * axis , const TfLiteTensor * input , int num_splits) | ['context', 'node', 'axis', 'input', 'num_splits'] | TfLiteStatus ResizeOutputTensors(TfLiteContext* context, TfLiteNode* node,
const TfLiteTensor* axis,
const TfLiteTensor* input, int num_splits) {
int axis_value = GetTensorData<int>(axis)[0];
if (axis_value < 0) {
axis_value += NumDimensions(input);
}
TF_LITE_ENSURE(context, axis_value >= 0);
TF_LITE_ENSURE(context, axis_value < NumDimensions(input));
const int input_size = SizeOfDimension(input, axis_value);
TF_LITE_ENSURE_MSG(context, input_size % num_splits == 0,
"Not an even split");
const int slice_size = input_size / num_splits;
for (int i = 0; i < NumOutputs(node); ++i) {
TfLiteIntArray* output_dims = TfLiteIntArrayCopy(input->dims);
output_dims->data[axis_value] = slice_size;
TfLiteTensor* output = GetOutput(context, node, i);
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_dims));
}
return kTfLiteOk;
} | 176 | 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::split::UseDynamicOutputTensors | tflite::ops::builtin::split::UseDynamicOutputTensors( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus UseDynamicOutputTensors(TfLiteContext* context, TfLiteNode* node) {
for (int i = 0; i < NumOutputs(node); ++i) {
SetTensorToDynamic(GetOutput(context, node, i));
}
return kTfLiteOk;
} | 46 | 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::split::UseDynamicOutputTensors | tflite::ops::builtin::split::UseDynamicOutputTensors( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus UseDynamicOutputTensors(TfLiteContext* context, TfLiteNode* node) {
for (int i = 0; i < NumOutputs(node); ++i) {
SetTensorToDynamic(GetOutput(context, node, i));
}
return kTfLiteOk;
} | 46 | 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::split_v::Prepare | tflite::ops::builtin::split_v::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
OpContext op_context(context, node);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), op_context.params->num_splits);
auto input_type = op_context.input->type;
TF_LITE_ENSURE(context,
input_type == kTfLiteFloat32 || input_type == kTfLiteUInt8 ||
input_type == kTfLiteInt16 || input_type == kTfLiteInt32 ||
input_type == kTfLiteInt64 || input_type == kTfLiteInt8);
for (int i = 0; i < NumOutputs(node); ++i) {
GetOutput(context, node, i)->type = input_type;
}
auto size_splits = op_context.size_splits;
TF_LITE_ENSURE_EQ(context, NumDimensions(size_splits), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), NumElements(size_splits));
// If we know the contents of the 'size_splits' tensor and the 'axis' tensor,
// resize all outputs. Otherwise, wait until Eval().
if (IsConstantTensor(op_context.size_splits) &&
IsConstantTensor(op_context.axis)) {
return ResizeOutputTensors(context, node, op_context.input,
op_context.size_splits, op_context.axis);
} else {
return UseDynamicOutputTensors(context, node);
}
} | 201 | 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::split_v::Prepare | tflite::ops::builtin::split_v::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
OpContext op_context(context, node);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), op_context.params->num_splits);
auto input_type = op_context.input->type;
TF_LITE_ENSURE(context,
input_type == kTfLiteFloat32 || input_type == kTfLiteUInt8 ||
input_type == kTfLiteInt16 || input_type == kTfLiteInt32 ||
input_type == kTfLiteInt64 || input_type == kTfLiteInt8);
for (int i = 0; i < NumOutputs(node); ++i) {
GetOutput(context, node, i)->type = input_type;
}
auto size_splits = op_context.size_splits;
TF_LITE_ENSURE_EQ(context, NumDimensions(size_splits), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), NumElements(size_splits));
// If we know the contents of the 'size_splits' tensor and the 'axis' tensor,
// resize all outputs. Otherwise, wait until Eval().
if (IsConstantTensor(op_context.size_splits) &&
IsConstantTensor(op_context.axis)) {
return ResizeOutputTensors(context, node, op_context.input,
op_context.size_splits, op_context.axis);
} else {
return UseDynamicOutputTensors(context, node);
}
} | 201 | 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::split_v::ResizeOutputTensors | tflite::ops::builtin::split_v::ResizeOutputTensors( TfLiteContext * context , TfLiteNode * node , const TfLiteTensor * input , const TfLiteTensor * size_splits , const TfLiteTensor * axis) | ['context', 'node', 'input', 'size_splits', 'axis'] | TfLiteStatus ResizeOutputTensors(TfLiteContext* context, TfLiteNode* node,
const TfLiteTensor* input,
const TfLiteTensor* size_splits,
const TfLiteTensor* axis) {
int axis_value = GetTensorData<int>(axis)[0];
if (axis_value < 0) {
axis_value += NumDimensions(input);
}
std::vector<int64_t> size_splits_vector;
if (size_splits->type == kTfLiteInt32) {
GetSizeSplitsVector<int32_t>(size_splits, &size_splits_vector);
} else if (size_splits->type == kTfLiteInt64) {
GetSizeSplitsVector<int64_t>(size_splits, &size_splits_vector);
} else {
context->ReportError(context, "size_splits only support type int32|int64.");
return kTfLiteError;
}
int minus_one_index = -1;
int64_t size_splits_sum = 0;
for (int i = 0; i < size_splits_vector.size(); ++i) {
if (size_splits_vector.at(i) == -1) {
if (minus_one_index == -1) {
minus_one_index = i;
} else {
context->ReportError(context,
"The size_splits contains more than one -1.");
}
} else {
size_splits_sum += size_splits_vector.at(i);
}
}
const int input_size = SizeOfDimension(input, axis_value);
if (minus_one_index != -1) {
if (size_splits_sum > input_size) {
context->ReportError(
context,
"The sum of size_splits must be less than the dimension of value.");
} else {
size_splits_vector[minus_one_index] = input_size - size_splits_sum;
}
} else if (size_splits_sum != input_size) {
context->ReportError(
context,
"The size_splits must sum to the dimension of value along axis.");
}
for (int i = 0; i < NumOutputs(node); ++i) {
TfLiteIntArray* output_dims = TfLiteIntArrayCopy(input->dims);
output_dims->data[axis_value] = size_splits_vector.at(i);
TfLiteTensor* output = GetOutput(context, node, i);
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_dims));
}
return kTfLiteOk;
} | 345 | 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::split_v::ResizeOutputTensors | tflite::ops::builtin::split_v::ResizeOutputTensors( TfLiteContext * context , TfLiteNode * node , const TfLiteTensor * input , const TfLiteTensor * size_splits , const TfLiteTensor * axis) | ['context', 'node', 'input', 'size_splits', 'axis'] | TfLiteStatus ResizeOutputTensors(TfLiteContext* context, TfLiteNode* node,
const TfLiteTensor* input,
const TfLiteTensor* size_splits,
const TfLiteTensor* axis) {
int axis_value = GetTensorData<int>(axis)[0];
if (axis_value < 0) {
axis_value += NumDimensions(input);
}
std::vector<int64_t> size_splits_vector;
if (size_splits->type == kTfLiteInt32) {
GetSizeSplitsVector<int32_t>(size_splits, &size_splits_vector);
} else if (size_splits->type == kTfLiteInt64) {
GetSizeSplitsVector<int64_t>(size_splits, &size_splits_vector);
} else {
context->ReportError(context, "size_splits only support type int32|int64.");
return kTfLiteError;
}
int minus_one_index = -1;
int64_t size_splits_sum = 0;
for (int i = 0; i < size_splits_vector.size(); ++i) {
if (size_splits_vector.at(i) == -1) {
if (minus_one_index == -1) {
minus_one_index = i;
} else {
context->ReportError(context,
"The size_splits contains more than one -1.");
}
} else {
size_splits_sum += size_splits_vector.at(i);
}
}
const int input_size = SizeOfDimension(input, axis_value);
if (minus_one_index != -1) {
if (size_splits_sum > input_size) {
context->ReportError(
context,
"The sum of size_splits must be less than the dimension of value.");
} else {
size_splits_vector[minus_one_index] = input_size - size_splits_sum;
}
} else if (size_splits_sum != input_size) {
context->ReportError(
context,
"The size_splits must sum to the dimension of value along axis.");
}
for (int i = 0; i < NumOutputs(node); ++i) {
TfLiteIntArray* output_dims = TfLiteIntArrayCopy(input->dims);
output_dims->data[axis_value] = size_splits_vector.at(i);
TfLiteTensor* output = GetOutput(context, node, i);
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_dims));
}
return kTfLiteOk;
} | 345 | 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::split_v::UseDynamicOutputTensors | tflite::ops::builtin::split_v::UseDynamicOutputTensors( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus UseDynamicOutputTensors(TfLiteContext* context, TfLiteNode* node) {
for (int i = 0; i < NumOutputs(node); ++i) {
SetTensorToDynamic(GetOutput(context, node, i));
}
return kTfLiteOk;
} | 46 | 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::split_v::UseDynamicOutputTensors | tflite::ops::builtin::split_v::UseDynamicOutputTensors( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus UseDynamicOutputTensors(TfLiteContext* context, TfLiteNode* node) {
for (int i = 0; i < NumOutputs(node); ++i) {
SetTensorToDynamic(GetOutput(context, node, i));
}
return kTfLiteOk;
} | 46 | 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::squared_difference::Eval | tflite::ops::builtin::squared_difference::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
ruy::profiler::ScopeLabel label("SquaredDifference");
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (output->type == kTfLiteFloat32) {
EvalSquaredDifference<float>(context, node, data, input1, input2, output);
} else if (output->type == kTfLiteInt32) {
EvalSquaredDifference<int32_t>(context, node, data, input1, input2, output);
} else {
context->ReportError(
context,
"SquaredDifference only supports FLOAT32 and INT32 now, got %d.",
output->type);
return kTfLiteError;
}
return kTfLiteOk;
} | 157 | 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::squared_difference::Eval | tflite::ops::builtin::squared_difference::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
ruy::profiler::ScopeLabel label("SquaredDifference");
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (output->type == kTfLiteFloat32) {
EvalSquaredDifference<float>(context, node, data, input1, input2, output);
} else if (output->type == kTfLiteInt32) {
EvalSquaredDifference<int32_t>(context, node, data, input1, input2, output);
} else {
context->ReportError(
context,
"SquaredDifference only supports FLOAT32 and INT32 now, got %d.",
output->type);
return kTfLiteError;
}
return kTfLiteOk;
} | 157 | 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::squared_difference::Prepare | tflite::ops::builtin::squared_difference::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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);
}
return context->ResizeTensor(context, output, output_size);
} | 180 | 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::squared_difference::Prepare | tflite::ops::builtin::squared_difference::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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);
}
return context->ResizeTensor(context, output, output_size);
} | 180 | 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::sub::Eval | tflite::ops::builtin::sub::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteSubParams*>(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 ||
output->type == kTfLiteInt64) {
EvalSub<kernel_type>(context, node, params, data, input1, input2, output);
} else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8 ||
output->type == kTfLiteInt16) {
EvalQuantized<kernel_type>(context, node, params, data, input1, input2,
output);
} else {
context->ReportError(
context,
"output type %d is not supported, requires float|uint8|int32 types.",
output->type);
return kTfLiteError;
}
return kTfLiteOk;
} | 190 | 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::sub::Eval | tflite::ops::builtin::sub::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteSubParams*>(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 ||
output->type == kTfLiteInt64) {
EvalSub<kernel_type>(context, node, params, data, input1, input2, output);
} else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8 ||
output->type == kTfLiteInt16) {
EvalQuantized<kernel_type>(context, node, params, data, input1, input2,
output);
} else {
context->ReportError(
context,
"output type %d is not supported, requires float|uint8|int32 types.",
output->type);
return kTfLiteError;
}
return kTfLiteOk;
} | 190 | 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::sub::Prepare | tflite::ops::builtin::sub::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
auto* params = reinterpret_cast<TfLiteSubParams*>(node->builtin_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);
}
// 8bit -> 8bit general quantized path, with general rescalings
// as well as, 16bit -> 16bit with general rescalings
bool pot_scale_int16 = true;
bool input1_scale_is_pot = false;
bool input2_scale_is_pot = false;
bool output_scale_is_pot = false;
int input1_scale_log2_rounded{0};
int input2_scale_log2_rounded{0};
int output_scale_log2_rounded{0};
if (input1->type == kTfLiteInt16 && input2->type == kTfLiteInt16 &&
output->type == kTfLiteInt16) {
// In case of 16-bit, there are two implementation:
// the scale parameter is a general number
// the scale parameter is POT and
// zero_point is zero for inputs/output.
pot_scale_int16 = (input1->params.zero_point == 0) &&
(input2->params.zero_point == 0) &&
(output->params.zero_point == 0);
input1_scale_is_pot =
CheckedLog2(input1->params.scale, &input1_scale_log2_rounded);
input2_scale_is_pot =
CheckedLog2(input2->params.scale, &input2_scale_log2_rounded);
output_scale_is_pot =
CheckedLog2(output->params.scale, &output_scale_log2_rounded);
pot_scale_int16 &=
input1_scale_is_pot && input2_scale_is_pot && output_scale_is_pot;
}
data->pot_scale_int16 = pot_scale_int16;
if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8 ||
!pot_scale_int16) {
TF_LITE_ENSURE_OK(context, PrepareGeneralSubOp(context, input1, input2,
output, params, data, -1));
} else if (output->type == kTfLiteInt16) {
// LSTM-special case with scale parameter of POT
TF_LITE_ENSURE_OK(context, PrepareInt16SubOpPOT(context, input1, input2,
output, params, data));
}
return context->ResizeTensor(context, output, output_size);
} | 416 | 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::sub::Prepare | tflite::ops::builtin::sub::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
auto* params = reinterpret_cast<TfLiteSubParams*>(node->builtin_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);
}
// 8bit -> 8bit general quantized path, with general rescalings
// as well as, 16bit -> 16bit with general rescalings
bool pot_scale_int16 = true;
bool input1_scale_is_pot = false;
bool input2_scale_is_pot = false;
bool output_scale_is_pot = false;
int input1_scale_log2_rounded{0};
int input2_scale_log2_rounded{0};
int output_scale_log2_rounded{0};
if (input1->type == kTfLiteInt16 && input2->type == kTfLiteInt16 &&
output->type == kTfLiteInt16) {
// In case of 16-bit, there are two implementation:
// the scale parameter is a general number
// the scale parameter is POT and
// zero_point is zero for inputs/output.
pot_scale_int16 = (input1->params.zero_point == 0) &&
(input2->params.zero_point == 0) &&
(output->params.zero_point == 0);
input1_scale_is_pot =
CheckedLog2(input1->params.scale, &input1_scale_log2_rounded);
input2_scale_is_pot =
CheckedLog2(input2->params.scale, &input2_scale_log2_rounded);
output_scale_is_pot =
CheckedLog2(output->params.scale, &output_scale_log2_rounded);
pot_scale_int16 &=
input1_scale_is_pot && input2_scale_is_pot && output_scale_is_pot;
}
data->pot_scale_int16 = pot_scale_int16;
if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8 ||
!pot_scale_int16) {
TF_LITE_ENSURE_OK(context, PrepareGeneralSubOp(context, input1, input2,
output, params, data, -1));
} else if (output->type == kTfLiteInt16) {
// LSTM-special case with scale parameter of POT
TF_LITE_ENSURE_OK(context, PrepareInt16SubOpPOT(context, input1, input2,
output, params, data));
}
return context->ResizeTensor(context, output, output_size);
} | 416 | 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::tile::Eval | tflite::ops::builtin::tile::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);
const TfLiteTensor* multipliers = GetInput(context, node, kInputMultipliers);
if (IsDynamicTensor(output)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
}
switch (output->type) {
case kTfLiteFloat32:
Tile<float>(*(input->dims), input, multipliers, output);
break;
case kTfLiteUInt8:
Tile<uint8_t>(*(input->dims), input, multipliers, output);
break;
case kTfLiteInt32:
Tile<int32_t>(*(input->dims), input, multipliers, output);
break;
case kTfLiteInt64:
Tile<int64_t>(*(input->dims), input, multipliers, output);
break;
case kTfLiteString: {
DynamicBuffer buffer;
TileString(*(input->dims), input, multipliers, &buffer, output);
buffer.WriteToTensor(output, /*new_shape=*/nullptr);
break;
}
case kTfLiteBool:
Tile<bool>(*(input->dims), input, multipliers, output);
break;
default:
context->ReportError(context, "Type '%s' is not supported by tile.",
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
return kTfLiteOk;
} | 264 | 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::tile::Eval | tflite::ops::builtin::tile::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);
const TfLiteTensor* multipliers = GetInput(context, node, kInputMultipliers);
if (IsDynamicTensor(output)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
}
switch (output->type) {
case kTfLiteFloat32:
Tile<float>(*(input->dims), input, multipliers, output);
break;
case kTfLiteUInt8:
Tile<uint8_t>(*(input->dims), input, multipliers, output);
break;
case kTfLiteInt32:
Tile<int32_t>(*(input->dims), input, multipliers, output);
break;
case kTfLiteInt64:
Tile<int64_t>(*(input->dims), input, multipliers, output);
break;
case kTfLiteString: {
DynamicBuffer buffer;
TileString(*(input->dims), input, multipliers, &buffer, output);
buffer.WriteToTensor(output, /*new_shape=*/nullptr);
break;
}
case kTfLiteBool:
Tile<bool>(*(input->dims), input, multipliers, output);
break;
default:
context->ReportError(context, "Type '%s' is not supported by tile.",
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
return kTfLiteOk;
} | 264 | 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::tile::Prepare | tflite::ops::builtin::tile::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, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
const TfLiteTensor* multipliers = GetInput(context, node, kInputMultipliers);
// Only int32 and int64 multipliers type is supported.
if (multipliers->type != kTfLiteInt32 && multipliers->type != kTfLiteInt64) {
context->ReportError(context,
"Multipliers of type '%s' are not supported by tile.",
TfLiteTypeGetName(multipliers->type));
return kTfLiteError;
}
if (IsConstantTensor(multipliers)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
} else {
SetTensorToDynamic(output);
}
return kTfLiteOk;
} | 157 | 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::tile::Prepare | tflite::ops::builtin::tile::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, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
const TfLiteTensor* multipliers = GetInput(context, node, kInputMultipliers);
// Only int32 and int64 multipliers type is supported.
if (multipliers->type != kTfLiteInt32 && multipliers->type != kTfLiteInt64) {
context->ReportError(context,
"Multipliers of type '%s' are not supported by tile.",
TfLiteTypeGetName(multipliers->type));
return kTfLiteError;
}
if (IsConstantTensor(multipliers)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
} else {
SetTensorToDynamic(output);
}
return kTfLiteOk;
} | 157 | 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::tile::ResizeOutput | tflite::ops::builtin::tile::ResizeOutput( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
const TfLiteTensor* multipliers = GetInput(context, node, kInputMultipliers);
const int num_dimensions = NumDimensions(input);
const int num_multipliers = NumElements(multipliers);
TF_LITE_ENSURE_EQ(context, num_dimensions, num_multipliers);
switch (multipliers->type) {
case kTfLiteInt32:
return context->ResizeTensor(
context, output,
MultiplyShapeDims<int32_t>(*input->dims, multipliers,
num_dimensions));
case kTfLiteInt64:
return context->ResizeTensor(
context, output,
MultiplyShapeDims<int64_t>(*input->dims, multipliers,
num_dimensions));
default:
context->ReportError(
context, "Multipliers of type '%s' are not supported by tile.",
TfLiteTypeGetName(multipliers->type));
return kTfLiteError;
}
} | 165 | 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::tile::ResizeOutput | tflite::ops::builtin::tile::ResizeOutput( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
const TfLiteTensor* multipliers = GetInput(context, node, kInputMultipliers);
const int num_dimensions = NumDimensions(input);
const int num_multipliers = NumElements(multipliers);
TF_LITE_ENSURE_EQ(context, num_dimensions, num_multipliers);
switch (multipliers->type) {
case kTfLiteInt32:
return context->ResizeTensor(
context, output,
MultiplyShapeDims<int32_t>(*input->dims, multipliers,
num_dimensions));
case kTfLiteInt64:
return context->ResizeTensor(
context, output,
MultiplyShapeDims<int64_t>(*input->dims, multipliers,
num_dimensions));
default:
context->ReportError(
context, "Multipliers of type '%s' are not supported by tile.",
TfLiteTypeGetName(multipliers->type));
return kTfLiteError;
}
} | 165 | 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::topk_v2::Eval | tflite::ops::builtin::topk_v2::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
TfLiteTensor* output_values = GetOutput(context, node, kOutputValues);
TfLiteTensor* output_indexes = GetOutput(context, node, kOutputIndexes);
if (IsDynamicTensor(output_values)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
}
const TfLiteTensor* top_k = GetInput(context, node, kInputTopK);
const int32 k = top_k->data.i32[0];
// The tensor can have more than 2 dimensions or even be a vector, the code
// anyway calls the internal dimension as row;
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const int32 row_size = input->dims->data[input->dims->size - 1];
int32 num_rows = 1;
for (int i = 0; i < input->dims->size - 1; ++i) {
num_rows *= input->dims->data[i];
}
switch (output_values->type) {
case kTfLiteFloat32:
TopK(row_size, num_rows, GetTensorData<float>(input), k,
output_indexes->data.i32, GetTensorData<float>(output_values));
break;
case kTfLiteUInt8:
TopK(row_size, num_rows, input->data.uint8, k, output_indexes->data.i32,
output_values->data.uint8);
break;
case kTfLiteInt8:
TopK(row_size, num_rows, input->data.int8, k, output_indexes->data.i32,
output_values->data.int8);
break;
case kTfLiteInt32:
TopK(row_size, num_rows, input->data.i32, k, output_indexes->data.i32,
output_values->data.i32);
break;
case kTfLiteInt64:
TopK(row_size, num_rows, input->data.i64, k, output_indexes->data.i32,
output_values->data.i64);
break;
default:
TF_LITE_KERNEL_LOG(context, "Type %s is currently not supported by TopK.",
TfLiteTypeGetName(output_values->type));
return kTfLiteError;
}
return kTfLiteOk;
} | 352 | 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::topk_v2::Eval | tflite::ops::builtin::topk_v2::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
TfLiteTensor* output_values = GetOutput(context, node, kOutputValues);
TfLiteTensor* output_indexes = GetOutput(context, node, kOutputIndexes);
if (IsDynamicTensor(output_values)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
}
const TfLiteTensor* top_k = GetInput(context, node, kInputTopK);
const int32 k = top_k->data.i32[0];
// The tensor can have more than 2 dimensions or even be a vector, the code
// anyway calls the internal dimension as row;
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const int32 row_size = input->dims->data[input->dims->size - 1];
int32 num_rows = 1;
for (int i = 0; i < input->dims->size - 1; ++i) {
num_rows *= input->dims->data[i];
}
switch (output_values->type) {
case kTfLiteFloat32:
TopK(row_size, num_rows, GetTensorData<float>(input), k,
output_indexes->data.i32, GetTensorData<float>(output_values));
break;
case kTfLiteUInt8:
TopK(row_size, num_rows, input->data.uint8, k, output_indexes->data.i32,
output_values->data.uint8);
break;
case kTfLiteInt8:
TopK(row_size, num_rows, input->data.int8, k, output_indexes->data.i32,
output_values->data.int8);
break;
case kTfLiteInt32:
TopK(row_size, num_rows, input->data.i32, k, output_indexes->data.i32,
output_values->data.i32);
break;
case kTfLiteInt64:
TopK(row_size, num_rows, input->data.i64, k, output_indexes->data.i32,
output_values->data.i64);
break;
default:
TF_LITE_KERNEL_LOG(context, "Type %s is currently not supported by TopK.",
TfLiteTypeGetName(output_values->type));
return kTfLiteError;
}
return kTfLiteOk;
} | 352 | 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::topk_v2::Prepare | tflite::ops::builtin::topk_v2::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
// Check that the inputs and outputs have the right sizes and types.
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output_values = GetOutput(context, node, kOutputValues);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output_values->type);
const TfLiteTensor* top_k = GetInput(context, node, kInputTopK);
TF_LITE_ENSURE_TYPES_EQ(context, top_k->type, kTfLiteInt32);
// Set output dynamic if the input is not const.
if (IsConstantTensor(top_k)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
} else {
TfLiteTensor* output_indexes = GetOutput(context, node, kOutputIndexes);
TfLiteTensor* output_values = GetOutput(context, node, kOutputValues);
SetTensorToDynamic(output_indexes);
SetTensorToDynamic(output_values);
}
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::topk_v2::Prepare | tflite::ops::builtin::topk_v2::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
// Check that the inputs and outputs have the right sizes and types.
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output_values = GetOutput(context, node, kOutputValues);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output_values->type);
const TfLiteTensor* top_k = GetInput(context, node, kInputTopK);
TF_LITE_ENSURE_TYPES_EQ(context, top_k->type, kTfLiteInt32);
// Set output dynamic if the input is not const.
if (IsConstantTensor(top_k)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
} else {
TfLiteTensor* output_indexes = GetOutput(context, node, kOutputIndexes);
TfLiteTensor* output_values = GetOutput(context, node, kOutputValues);
SetTensorToDynamic(output_indexes);
SetTensorToDynamic(output_values);
}
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::topk_v2::ResizeOutput | tflite::ops::builtin::topk_v2::ResizeOutput( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* top_k = GetInput(context, node, kInputTopK);
// INT32 number of top results is supported.
TF_LITE_ENSURE_TYPES_EQ(context, top_k->type, kTfLiteInt32);
// Check that the tensor contains only one value.
TF_LITE_ENSURE_EQ(context, NumElements(top_k), 1);
const int32 k = *GetTensorData<int32_t>(top_k);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const int num_dimensions = NumDimensions(input);
// Check that input has one or more dimensions.
TF_LITE_ENSURE_MSG(context, input->dims->size >= 1,
"TopK k input must have 1 or more dimensions.");
// Check that k is less or equal the internal dimension.
TF_LITE_ENSURE_MSG(context, k <= input->dims->data[num_dimensions - 1],
"TopK k is higher than the internal dimension.");
TfLiteIntArray* output_indexes_shape = TfLiteIntArrayCreate(num_dimensions);
TfLiteIntArray* output_values_shape = TfLiteIntArrayCreate(num_dimensions);
for (int i = 0; i < num_dimensions - 1; ++i) {
output_indexes_shape->data[i] = input->dims->data[i];
output_values_shape->data[i] = input->dims->data[i];
}
output_indexes_shape->data[num_dimensions - 1] = k;
output_values_shape->data[num_dimensions - 1] = k;
TfLiteTensor* output_indexes = GetOutput(context, node, kOutputIndexes);
TfLiteTensor* output_values = GetOutput(context, node, kOutputValues);
// Force output types.
output_indexes->type = kTfLiteInt32;
output_values->type = input->type;
auto resize_tensor = [context](TfLiteTensor* tensor, TfLiteIntArray* new_size,
TfLiteIntArray* delete_on_error) {
TfLiteStatus status = context->ResizeTensor(context, tensor, new_size);
if (status != kTfLiteOk) {
if (delete_on_error != nullptr) {
TfLiteIntArrayFree(delete_on_error);
}
}
return status;
};
TF_LITE_ENSURE_OK(context, resize_tensor(output_indexes, output_indexes_shape,
output_values_shape));
TF_LITE_ENSURE_OK(context,
resize_tensor(output_values, output_values_shape, nullptr));
return kTfLiteOk;
} | 341 | 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::topk_v2::ResizeOutput | tflite::ops::builtin::topk_v2::ResizeOutput( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* top_k = GetInput(context, node, kInputTopK);
// INT32 number of top results is supported.
TF_LITE_ENSURE_TYPES_EQ(context, top_k->type, kTfLiteInt32);
// Check that the tensor contains only one value.
TF_LITE_ENSURE_EQ(context, NumElements(top_k), 1);
const int32 k = *GetTensorData<int32_t>(top_k);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const int num_dimensions = NumDimensions(input);
// Check that input has one or more dimensions.
TF_LITE_ENSURE_MSG(context, input->dims->size >= 1,
"TopK k input must have 1 or more dimensions.");
// Check that k is less or equal the internal dimension.
TF_LITE_ENSURE_MSG(context, k <= input->dims->data[num_dimensions - 1],
"TopK k is higher than the internal dimension.");
TfLiteIntArray* output_indexes_shape = TfLiteIntArrayCreate(num_dimensions);
TfLiteIntArray* output_values_shape = TfLiteIntArrayCreate(num_dimensions);
for (int i = 0; i < num_dimensions - 1; ++i) {
output_indexes_shape->data[i] = input->dims->data[i];
output_values_shape->data[i] = input->dims->data[i];
}
output_indexes_shape->data[num_dimensions - 1] = k;
output_values_shape->data[num_dimensions - 1] = k;
TfLiteTensor* output_indexes = GetOutput(context, node, kOutputIndexes);
TfLiteTensor* output_values = GetOutput(context, node, kOutputValues);
// Force output types.
output_indexes->type = kTfLiteInt32;
output_values->type = input->type;
auto resize_tensor = [context](TfLiteTensor* tensor, TfLiteIntArray* new_size,
TfLiteIntArray* delete_on_error) {
TfLiteStatus status = context->ResizeTensor(context, tensor, new_size);
if (status != kTfLiteOk) {
if (delete_on_error != nullptr) {
TfLiteIntArrayFree(delete_on_error);
}
}
return status;
};
TF_LITE_ENSURE_OK(context, resize_tensor(output_indexes, output_indexes_shape,
output_values_shape));
TF_LITE_ENSURE_OK(context,
resize_tensor(output_values, output_values_shape, nullptr));
return kTfLiteOk;
} | 341 | 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::unidirectional_sequence_lstm::CheckInputTensorDimensions | tflite::ops::builtin::unidirectional_sequence_lstm::CheckInputTensorDimensions( TfLiteContext * context , TfLiteNode * node , int n_input , int n_output , int n_cell , bool is_layer_norm_lstm) | ['context', 'node', 'n_input', 'n_output', 'n_cell', 'is_layer_norm_lstm'] | TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
TfLiteNode* node, int n_input,
int n_output, int n_cell,
bool is_layer_norm_lstm) {
const auto* params = reinterpret_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_input_weights = GetOptionalInputTensor(
context, node, lstm::full::kInputToInputWeightsTensor);
if (input_to_input_weights != nullptr) {
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);
}
const TfLiteTensor* input_to_forget_weights =
GetInput(context, node, lstm::full::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);
const TfLiteTensor* input_to_cell_weights =
GetInput(context, node, lstm::full::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);
const TfLiteTensor* recurrent_to_input_weights = GetOptionalInputTensor(
context, node, lstm::full::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);
}
const TfLiteTensor* recurrent_to_forget_weights =
GetInput(context, node, lstm::full::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);
const TfLiteTensor* recurrent_to_cell_weights =
GetInput(context, node, lstm::full::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);
// 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, lstm::full::kCellToInputWeightsTensor);
if (cell_to_input_weights != nullptr) {
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);
}
const TfLiteTensor* cell_to_forget_weights = GetOptionalInputTensor(
context, node, lstm::full::kCellToForgetWeightsTensor);
if (cell_to_forget_weights != nullptr) {
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);
}
const TfLiteTensor* cell_to_output_weights = GetOptionalInputTensor(
context, node, lstm::full::kCellToOutputWeightsTensor);
if (cell_to_output_weights != nullptr) {
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);
}
// Making sure the peephole weights are there all or none.
const bool use_cifg = (input_to_input_weights == nullptr);
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, lstm::full::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);
}
const TfLiteTensor* forget_gate_bias =
GetInput(context, node, lstm::full::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);
const TfLiteTensor* cell_gate_bias =
GetInput(context, node, lstm::full::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);
const TfLiteTensor* output_gate_bias =
GetInput(context, node, lstm::full::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);
const TfLiteTensor* projection_weights = GetOptionalInputTensor(
context, node, lstm::full::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);
}
const TfLiteTensor* projection_bias =
GetOptionalInputTensor(context, node, lstm::full::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);
}
// 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 projecton_tensors_consistent =
((projection_weights != nullptr) || (projection_bias == nullptr));
TF_LITE_ENSURE(context, projecton_tensors_consistent == true);
if (is_layer_norm_lstm) {
const TfLiteTensor* input_layer_norm_coefficients = GetOptionalInputTensor(
context, node, lstm::full::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);
TF_LITE_ENSURE_TYPES_EQ(context, input_layer_norm_coefficients->type,
kTfLiteFloat32);
}
const TfLiteTensor* forget_layer_norm_coefficients =
GetInput(context, node, lstm::full::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);
TF_LITE_ENSURE_TYPES_EQ(context, forget_layer_norm_coefficients->type,
kTfLiteFloat32);
const TfLiteTensor* cell_layer_norm_coefficients =
GetInput(context, node, lstm::full::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);
TF_LITE_ENSURE_TYPES_EQ(context, cell_layer_norm_coefficients->type,
kTfLiteFloat32);
const TfLiteTensor* output_layer_norm_coefficients =
GetInput(context, node, lstm::full::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);
TF_LITE_ENSURE_TYPES_EQ(context, output_layer_norm_coefficients->type,
kTfLiteFloat32);
}
return kTfLiteOk;
} | 1381 | 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::unidirectional_sequence_lstm::CheckInputTensorDimensions | tflite::ops::builtin::unidirectional_sequence_lstm::CheckInputTensorDimensions( TfLiteContext * context , TfLiteNode * node , int n_input , int n_output , int n_cell , bool is_layer_norm_lstm) | ['context', 'node', 'n_input', 'n_output', 'n_cell', 'is_layer_norm_lstm'] | TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
TfLiteNode* node, int n_input,
int n_output, int n_cell,
bool is_layer_norm_lstm) {
const auto* params = reinterpret_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_input_weights = GetOptionalInputTensor(
context, node, lstm::full::kInputToInputWeightsTensor);
if (input_to_input_weights != nullptr) {
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);
}
const TfLiteTensor* input_to_forget_weights =
GetInput(context, node, lstm::full::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);
const TfLiteTensor* input_to_cell_weights =
GetInput(context, node, lstm::full::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);
const TfLiteTensor* recurrent_to_input_weights = GetOptionalInputTensor(
context, node, lstm::full::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);
}
const TfLiteTensor* recurrent_to_forget_weights =
GetInput(context, node, lstm::full::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);
const TfLiteTensor* recurrent_to_cell_weights =
GetInput(context, node, lstm::full::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);
// 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, lstm::full::kCellToInputWeightsTensor);
if (cell_to_input_weights != nullptr) {
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);
}
const TfLiteTensor* cell_to_forget_weights = GetOptionalInputTensor(
context, node, lstm::full::kCellToForgetWeightsTensor);
if (cell_to_forget_weights != nullptr) {
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);
}
const TfLiteTensor* cell_to_output_weights = GetOptionalInputTensor(
context, node, lstm::full::kCellToOutputWeightsTensor);
if (cell_to_output_weights != nullptr) {
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);
}
// Making sure the peephole weights are there all or none.
const bool use_cifg = (input_to_input_weights == nullptr);
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, lstm::full::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);
}
const TfLiteTensor* forget_gate_bias =
GetInput(context, node, lstm::full::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);
const TfLiteTensor* cell_gate_bias =
GetInput(context, node, lstm::full::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);
const TfLiteTensor* output_gate_bias =
GetInput(context, node, lstm::full::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);
const TfLiteTensor* projection_weights = GetOptionalInputTensor(
context, node, lstm::full::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);
}
const TfLiteTensor* projection_bias =
GetOptionalInputTensor(context, node, lstm::full::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);
}
// 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 projecton_tensors_consistent =
((projection_weights != nullptr) || (projection_bias == nullptr));
TF_LITE_ENSURE(context, projecton_tensors_consistent == true);
if (is_layer_norm_lstm) {
const TfLiteTensor* input_layer_norm_coefficients = GetOptionalInputTensor(
context, node, lstm::full::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);
TF_LITE_ENSURE_TYPES_EQ(context, input_layer_norm_coefficients->type,
kTfLiteFloat32);
}
const TfLiteTensor* forget_layer_norm_coefficients =
GetInput(context, node, lstm::full::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);
TF_LITE_ENSURE_TYPES_EQ(context, forget_layer_norm_coefficients->type,
kTfLiteFloat32);
const TfLiteTensor* cell_layer_norm_coefficients =
GetInput(context, node, lstm::full::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);
TF_LITE_ENSURE_TYPES_EQ(context, cell_layer_norm_coefficients->type,
kTfLiteFloat32);
const TfLiteTensor* output_layer_norm_coefficients =
GetInput(context, node, lstm::full::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);
TF_LITE_ENSURE_TYPES_EQ(context, output_layer_norm_coefficients->type,
kTfLiteFloat32);
}
return kTfLiteOk;
} | 1381 | 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::unidirectional_sequence_lstm::Eval | tflite::ops::builtin::unidirectional_sequence_lstm::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const auto* params =
reinterpret_cast<TfLiteUnidirectionalSequenceLSTMParams*>(
node->builtin_data);
const OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
const bool is_layer_norm_lstm = op_data->is_layer_norm_lstm;
const bool time_major = params->time_major;
const TfLiteTensor* input = GetInput(context, node, lstm::full::kInputTensor);
const TfLiteTensor* input_to_input_weights = GetOptionalInputTensor(
context, node, lstm::full::kInputToInputWeightsTensor);
const TfLiteTensor* input_to_forget_weights =
GetInput(context, node, lstm::full::kInputToForgetWeightsTensor);
const TfLiteTensor* input_to_cell_weights =
GetInput(context, node, lstm::full::kInputToCellWeightsTensor);
const TfLiteTensor* input_to_output_weights =
GetInput(context, node, lstm::full::kInputToOutputWeightsTensor);
const TfLiteTensor* recurrent_to_input_weights = GetOptionalInputTensor(
context, node, lstm::full::kRecurrentToInputWeightsTensor);
const TfLiteTensor* recurrent_to_forget_weights =
GetInput(context, node, lstm::full::kRecurrentToForgetWeightsTensor);
const TfLiteTensor* recurrent_to_cell_weights =
GetInput(context, node, lstm::full::kRecurrentToCellWeightsTensor);
const TfLiteTensor* recurrent_to_output_weights =
GetInput(context, node, lstm::full::kRecurrentToOutputWeightsTensor);
const TfLiteTensor* cell_to_input_weights = GetOptionalInputTensor(
context, node, lstm::full::kCellToInputWeightsTensor);
const TfLiteTensor* cell_to_forget_weights = GetOptionalInputTensor(
context, node, lstm::full::kCellToForgetWeightsTensor);
const TfLiteTensor* cell_to_output_weights = GetOptionalInputTensor(
context, node, lstm::full::kCellToOutputWeightsTensor);
const TfLiteTensor* input_gate_bias =
GetOptionalInputTensor(context, node, lstm::full::kInputGateBiasTensor);
const TfLiteTensor* forget_gate_bias =
GetInput(context, node, lstm::full::kForgetGateBiasTensor);
const TfLiteTensor* cell_gate_bias =
GetInput(context, node, lstm::full::kCellGateBiasTensor);
const TfLiteTensor* output_gate_bias =
GetInput(context, node, lstm::full::kOutputGateBiasTensor);
const TfLiteTensor* projection_weights = GetOptionalInputTensor(
context, node, lstm::full::kProjectionWeightsTensor);
const TfLiteTensor* projection_bias =
GetOptionalInputTensor(context, node, lstm::full::kProjectionBiasTensor);
// Index the scratch buffers pointers to the global scratch buffer.
TfLiteTensor* scratch_buffer = GetTemporary(context, node, kScratchBuffer);
TfLiteTensor* output_state =
GetVariableInput(context, node, lstm::full::kOutputStateTensor);
TF_LITE_ENSURE(context, output_state != nullptr);
TfLiteTensor* cell_state =
GetVariableInput(context, node, lstm::full::kCellStateTensor);
TF_LITE_ENSURE(context, cell_state != nullptr);
const TfLiteTensor* input_layer_norm_coefficients =
is_layer_norm_lstm
? GetOptionalInputTensor(
context, node, lstm::full::kInputLayerNormCoefficientsTensor)
: nullptr;
const TfLiteTensor* forget_layer_norm_coefficients =
is_layer_norm_lstm
? GetInput(context, node,
lstm::full::kForgetLayerNormCoefficientsTensor)
: nullptr;
const TfLiteTensor* cell_layer_norm_coefficients =
is_layer_norm_lstm
? GetInput(context, node,
lstm::full::kCellLayerNormCoefficientsTensor)
: nullptr;
const TfLiteTensor* output_layer_norm_coefficients =
is_layer_norm_lstm
? GetInput(context, node,
lstm::full::kOutputLayerNormCoefficientsTensor)
: nullptr;
TfLiteTensor* output = GetOutput(context, node, lstm::full::kOutputTensor);
// Copy out the LSTM specific params so they can be passed in the function.
TfLiteLSTMParams lstm_params;
lstm_params.activation = params->activation;
lstm_params.cell_clip = params->cell_clip;
lstm_params.proj_clip = params->proj_clip;
lstm_params.asymmetric_quantize_inputs = params->asymmetric_quantize_inputs;
switch (input_to_output_weights->type) {
case kTfLiteFloat32: {
return lstm_eval::EvalFloat(
input, input_to_input_weights, input_to_forget_weights,
input_to_cell_weights, input_to_output_weights,
recurrent_to_input_weights, recurrent_to_forget_weights,
recurrent_to_cell_weights, recurrent_to_output_weights,
cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights,
input_layer_norm_coefficients, forget_layer_norm_coefficients,
cell_layer_norm_coefficients, output_layer_norm_coefficients,
/*aux_input=*/nullptr,
/*aux_input_to_input_weights=*/nullptr,
/*aux_input_to_forget_weights=*/nullptr,
/*aux_input_to_cell_weights=*/nullptr,
/*aux_input_to_output_weights=*/nullptr, input_gate_bias,
forget_gate_bias, cell_gate_bias, output_gate_bias,
projection_weights, projection_bias, &lstm_params,
/*forward_sequence=*/true, time_major,
/*output_offset=*/0, scratch_buffer, output_state, cell_state,
output);
}
case kTfLiteUInt8:
case kTfLiteInt8: {
OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
TfLiteTensor* row_sums = GetTemporary(context, node, kRowSums);
const int row_sums_size = row_sums->dims->data[0];
return lstm_eval::EvalHybrid(
input, input_to_input_weights,
/*input_to_input_weights_ledger*/ nullptr, input_to_forget_weights,
/*input_to_forget_weights_ledger*/ nullptr, input_to_cell_weights,
/*input_to_cell_weights_ledger*/ nullptr, input_to_output_weights,
/*input_to_output_weights_ledger*/ nullptr,
recurrent_to_input_weights,
/*recurrent_to_input_weights_ledger*/ nullptr,
recurrent_to_forget_weights,
/*recurrent_to_forget_weights_ledger*/ nullptr,
recurrent_to_cell_weights,
/*recurrent_to_cell_weights_ledger*/ nullptr,
recurrent_to_output_weights,
/*recurrent_to_output_weights_ledger*/ nullptr, cell_to_input_weights,
cell_to_forget_weights, cell_to_output_weights,
input_layer_norm_coefficients, forget_layer_norm_coefficients,
cell_layer_norm_coefficients, output_layer_norm_coefficients,
/*aux_input=*/nullptr,
/*aux_input_to_input_weights=*/nullptr,
/*aux_input_to_forget_weights=*/nullptr,
/*aux_input_to_cell_weights=*/nullptr,
/*aux_input_to_output_weights=*/nullptr, input_gate_bias,
forget_gate_bias, cell_gate_bias, output_gate_bias,
projection_weights, /*projection_weights_ledger*/ nullptr,
projection_bias, &lstm_params,
/*forward_sequence=*/true, time_major,
/*output_offset=*/0, scratch_buffer,
GetTemporary(context, node, kInputScalingFactors),
/*aux_input_sf=*/nullptr,
GetTemporary(context, node, kOutputStateScalingFactors),
GetTemporary(context, node, kProductScalingFactors),
GetTemporary(context, node, kRecoveredCellWeights),
GetTemporary(context, node, kInputQuantized),
/*aux_input_quantized=*/nullptr,
GetTemporary(context, node, kOutputStateQuantized),
GetTemporary(context, node, kCellStateQuantized), output_state,
cell_state, GetTemporary(context, node, kAccumScratch), output,
GetTemporary(context, node, kInputZeroPoints),
/*aux_input_zp=*/nullptr,
GetTemporary(context, node, kOutputStateZeroPoints), row_sums,
row_sums_size, &op_data->compute_row_sums,
CpuBackendContext::GetFromContext(context));
}
default:
TF_LITE_KERNEL_LOG(context, "Type %s is not currently supported.",
TfLiteTypeGetName(input_to_output_weights->type));
return kTfLiteError;
}
return kTfLiteOk;
} | 957 | 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::unidirectional_sequence_lstm::Eval | tflite::ops::builtin::unidirectional_sequence_lstm::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const auto* params =
reinterpret_cast<TfLiteUnidirectionalSequenceLSTMParams*>(
node->builtin_data);
const OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
const bool is_layer_norm_lstm = op_data->is_layer_norm_lstm;
const bool time_major = params->time_major;
const TfLiteTensor* input = GetInput(context, node, lstm::full::kInputTensor);
const TfLiteTensor* input_to_input_weights = GetOptionalInputTensor(
context, node, lstm::full::kInputToInputWeightsTensor);
const TfLiteTensor* input_to_forget_weights =
GetInput(context, node, lstm::full::kInputToForgetWeightsTensor);
const TfLiteTensor* input_to_cell_weights =
GetInput(context, node, lstm::full::kInputToCellWeightsTensor);
const TfLiteTensor* input_to_output_weights =
GetInput(context, node, lstm::full::kInputToOutputWeightsTensor);
const TfLiteTensor* recurrent_to_input_weights = GetOptionalInputTensor(
context, node, lstm::full::kRecurrentToInputWeightsTensor);
const TfLiteTensor* recurrent_to_forget_weights =
GetInput(context, node, lstm::full::kRecurrentToForgetWeightsTensor);
const TfLiteTensor* recurrent_to_cell_weights =
GetInput(context, node, lstm::full::kRecurrentToCellWeightsTensor);
const TfLiteTensor* recurrent_to_output_weights =
GetInput(context, node, lstm::full::kRecurrentToOutputWeightsTensor);
const TfLiteTensor* cell_to_input_weights = GetOptionalInputTensor(
context, node, lstm::full::kCellToInputWeightsTensor);
const TfLiteTensor* cell_to_forget_weights = GetOptionalInputTensor(
context, node, lstm::full::kCellToForgetWeightsTensor);
const TfLiteTensor* cell_to_output_weights = GetOptionalInputTensor(
context, node, lstm::full::kCellToOutputWeightsTensor);
const TfLiteTensor* input_gate_bias =
GetOptionalInputTensor(context, node, lstm::full::kInputGateBiasTensor);
const TfLiteTensor* forget_gate_bias =
GetInput(context, node, lstm::full::kForgetGateBiasTensor);
const TfLiteTensor* cell_gate_bias =
GetInput(context, node, lstm::full::kCellGateBiasTensor);
const TfLiteTensor* output_gate_bias =
GetInput(context, node, lstm::full::kOutputGateBiasTensor);
const TfLiteTensor* projection_weights = GetOptionalInputTensor(
context, node, lstm::full::kProjectionWeightsTensor);
const TfLiteTensor* projection_bias =
GetOptionalInputTensor(context, node, lstm::full::kProjectionBiasTensor);
// Index the scratch buffers pointers to the global scratch buffer.
TfLiteTensor* scratch_buffer = GetTemporary(context, node, kScratchBuffer);
TfLiteTensor* output_state =
GetVariableInput(context, node, lstm::full::kOutputStateTensor);
TF_LITE_ENSURE(context, output_state != nullptr);
TfLiteTensor* cell_state =
GetVariableInput(context, node, lstm::full::kCellStateTensor);
TF_LITE_ENSURE(context, cell_state != nullptr);
const TfLiteTensor* input_layer_norm_coefficients =
is_layer_norm_lstm
? GetOptionalInputTensor(
context, node, lstm::full::kInputLayerNormCoefficientsTensor)
: nullptr;
const TfLiteTensor* forget_layer_norm_coefficients =
is_layer_norm_lstm
? GetInput(context, node,
lstm::full::kForgetLayerNormCoefficientsTensor)
: nullptr;
const TfLiteTensor* cell_layer_norm_coefficients =
is_layer_norm_lstm
? GetInput(context, node,
lstm::full::kCellLayerNormCoefficientsTensor)
: nullptr;
const TfLiteTensor* output_layer_norm_coefficients =
is_layer_norm_lstm
? GetInput(context, node,
lstm::full::kOutputLayerNormCoefficientsTensor)
: nullptr;
TfLiteTensor* output = GetOutput(context, node, lstm::full::kOutputTensor);
// Copy out the LSTM specific params so they can be passed in the function.
TfLiteLSTMParams lstm_params;
lstm_params.activation = params->activation;
lstm_params.cell_clip = params->cell_clip;
lstm_params.proj_clip = params->proj_clip;
lstm_params.asymmetric_quantize_inputs = params->asymmetric_quantize_inputs;
switch (input_to_output_weights->type) {
case kTfLiteFloat32: {
return lstm_eval::EvalFloat(
input, input_to_input_weights, input_to_forget_weights,
input_to_cell_weights, input_to_output_weights,
recurrent_to_input_weights, recurrent_to_forget_weights,
recurrent_to_cell_weights, recurrent_to_output_weights,
cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights,
input_layer_norm_coefficients, forget_layer_norm_coefficients,
cell_layer_norm_coefficients, output_layer_norm_coefficients,
/*aux_input=*/nullptr,
/*aux_input_to_input_weights=*/nullptr,
/*aux_input_to_forget_weights=*/nullptr,
/*aux_input_to_cell_weights=*/nullptr,
/*aux_input_to_output_weights=*/nullptr, input_gate_bias,
forget_gate_bias, cell_gate_bias, output_gate_bias,
projection_weights, projection_bias, &lstm_params,
/*forward_sequence=*/true, time_major,
/*output_offset=*/0, scratch_buffer, output_state, cell_state,
output);
}
case kTfLiteUInt8:
case kTfLiteInt8: {
OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
TfLiteTensor* row_sums = GetTemporary(context, node, kRowSums);
const int row_sums_size = row_sums->dims->data[0];
return lstm_eval::EvalHybrid(
input, input_to_input_weights,
/*input_to_input_weights_ledger*/ nullptr, input_to_forget_weights,
/*input_to_forget_weights_ledger*/ nullptr, input_to_cell_weights,
/*input_to_cell_weights_ledger*/ nullptr, input_to_output_weights,
/*input_to_output_weights_ledger*/ nullptr,
recurrent_to_input_weights,
/*recurrent_to_input_weights_ledger*/ nullptr,
recurrent_to_forget_weights,
/*recurrent_to_forget_weights_ledger*/ nullptr,
recurrent_to_cell_weights,
/*recurrent_to_cell_weights_ledger*/ nullptr,
recurrent_to_output_weights,
/*recurrent_to_output_weights_ledger*/ nullptr, cell_to_input_weights,
cell_to_forget_weights, cell_to_output_weights,
input_layer_norm_coefficients, forget_layer_norm_coefficients,
cell_layer_norm_coefficients, output_layer_norm_coefficients,
/*aux_input=*/nullptr,
/*aux_input_to_input_weights=*/nullptr,
/*aux_input_to_forget_weights=*/nullptr,
/*aux_input_to_cell_weights=*/nullptr,
/*aux_input_to_output_weights=*/nullptr, input_gate_bias,
forget_gate_bias, cell_gate_bias, output_gate_bias,
projection_weights, /*projection_weights_ledger*/ nullptr,
projection_bias, &lstm_params,
/*forward_sequence=*/true, time_major,
/*output_offset=*/0, scratch_buffer,
GetTemporary(context, node, kInputScalingFactors),
/*aux_input_sf=*/nullptr,
GetTemporary(context, node, kOutputStateScalingFactors),
GetTemporary(context, node, kProductScalingFactors),
GetTemporary(context, node, kRecoveredCellWeights),
GetTemporary(context, node, kInputQuantized),
/*aux_input_quantized=*/nullptr,
GetTemporary(context, node, kOutputStateQuantized),
GetTemporary(context, node, kCellStateQuantized), output_state,
cell_state, GetTemporary(context, node, kAccumScratch), output,
GetTemporary(context, node, kInputZeroPoints),
/*aux_input_zp=*/nullptr,
GetTemporary(context, node, kOutputStateZeroPoints), row_sums,
row_sums_size, &op_data->compute_row_sums,
CpuBackendContext::GetFromContext(context));
}
default:
TF_LITE_KERNEL_LOG(context, "Type %s is not currently supported.",
TfLiteTypeGetName(input_to_output_weights->type));
return kTfLiteError;
}
return kTfLiteOk;
} | 957 | 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::unidirectional_sequence_lstm::Prepare | tflite::ops::builtin::unidirectional_sequence_lstm::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
const int scratch_tensor_index = op_data->scratch_tensor_index;
// Check we have all the inputs and outputs we need.
bool is_layer_norm_lstm = false;
if (node->inputs->size == 24) {
const TfLiteTensor* forget_layer_norm_coefficients = GetOptionalInputTensor(
context, node, lstm::full::kForgetLayerNormCoefficientsTensor);
if (forget_layer_norm_coefficients == nullptr) {
is_layer_norm_lstm = false;
} else {
is_layer_norm_lstm = true;
}
} else if (node->inputs->size == 20) {
// This is deprecated and is only kept here for backward compatibility.
is_layer_norm_lstm = false;
} else {
context->ReportError(
context, "The LSTM Full kernel expects 20 or 24 inputs. Got %d inputs",
node->inputs->size);
return kTfLiteError;
}
TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
op_data->is_layer_norm_lstm = is_layer_norm_lstm;
// Inferring batch size, number of outputs and sequence length and
// number of cells from the input tensors.
const TfLiteTensor* input = GetInput(context, node, lstm::full::kInputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32);
TF_LITE_ENSURE(context, input->dims->size > 1);
const auto* params =
reinterpret_cast<TfLiteUnidirectionalSequenceLSTMParams*>(
node->builtin_data);
const bool time_major = params->time_major;
const int n_batch = time_major ? input->dims->data[1] : input->dims->data[0];
const int n_input = input->dims->data[2];
const TfLiteTensor* input_to_output_weights =
GetInput(context, node, lstm::full::kInputToOutputWeightsTensor);
const int n_cell = input_to_output_weights->dims->data[0];
TF_LITE_ENSURE_EQ(context, input_to_output_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, input_to_output_weights->dims->data[1], n_input);
const TfLiteTensor* recurrent_to_output_weights =
GetInput(context, node, lstm::full::kRecurrentToOutputWeightsTensor);
TF_LITE_ENSURE_EQ(context, recurrent_to_output_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, recurrent_to_output_weights->dims->data[0],
n_cell);
const int n_output = recurrent_to_output_weights->dims->data[1];
// Check that input tensor dimensions matches with each other.
TF_LITE_ENSURE_OK(context,
CheckInputTensorDimensions(context, node, n_input, n_output,
n_cell, is_layer_norm_lstm));
// Get the pointer to output, output_state and cell_state buffer tensors.
TfLiteTensor* output = GetOutput(context, node, lstm::full::kOutputTensor);
TfLiteTensor* output_state =
GetVariableInput(context, node, lstm::full::kOutputStateTensor);
TF_LITE_ENSURE(context, output_state != nullptr);
TfLiteTensor* cell_state =
GetVariableInput(context, node, lstm::full::kCellStateTensor);
TF_LITE_ENSURE(context, cell_state != nullptr);
// Check the shape of input state tensors.
// These tensor may be 1D or 2D. It's fine as long as the total size is
// correct.
TF_LITE_ENSURE_EQ(context, NumElements(output_state), n_batch * n_output);
TF_LITE_ENSURE_EQ(context, NumElements(cell_state), n_batch * n_cell);
// Resize the output tensors.
TfLiteIntArray* output_size = TfLiteIntArrayCopy(input->dims);
output_size->data[input->dims->size - 1] = n_output;
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, output, output_size));
TfLiteIntArrayFree(node->temporaries);
if (IsHybridOp(input, input_to_output_weights)) {
node->temporaries = TfLiteIntArrayCreate(kNumTemporaryTensors);
} else {
node->temporaries = TfLiteIntArrayCreate(1);
}
node->temporaries->data[kScratchBuffer] =
scratch_tensor_index + kScratchBuffer;
// Create a scratch buffer tensor.
TfLiteTensor* scratch_buffer = GetTemporary(context, node, kScratchBuffer);
scratch_buffer->type = input->type;
scratch_buffer->allocation_type = kTfLiteArenaRw;
const TfLiteTensor* input_to_input_weights = GetOptionalInputTensor(
context, node, lstm::full::kInputToInputWeightsTensor);
const bool use_cifg = (input_to_input_weights == nullptr);
TfLiteIntArray* scratch_buffer_size = TfLiteIntArrayCreate(2);
scratch_buffer_size->data[0] = n_batch;
if (use_cifg) {
// Reserving space for Cell, Forget, Output gates
scratch_buffer_size->data[1] = n_cell * 3;
} else {
// Reserving space for Input, Cell, Forget, Output gates
scratch_buffer_size->data[1] = n_cell * 4;
}
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_buffer,
scratch_buffer_size));
if (IsHybridOp(input, input_to_output_weights)) {
op_data->compute_row_sums = true;
// Allocate temporary tensors to store quantized values of input,
// output_state and cell_state tensors.
node->temporaries->data[kInputQuantized] =
scratch_tensor_index + kInputQuantized;
TfLiteTensor* input_quantized =
GetTemporary(context, node, kInputQuantized);
input_quantized->type = input_to_output_weights->type;
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[kOutputStateQuantized] =
scratch_tensor_index + kOutputStateQuantized;
TfLiteTensor* output_state_quantized =
GetTemporary(context, node, kOutputStateQuantized);
output_state_quantized->type = input_to_output_weights->type;
output_state_quantized->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqual(output_state_quantized->dims,
output_state->dims)) {
TfLiteIntArray* output_state_quantized_size =
TfLiteIntArrayCopy(output_state->dims);
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, output_state_quantized,
output_state_quantized_size));
}
node->temporaries->data[kCellStateQuantized] =
scratch_tensor_index + kCellStateQuantized;
TfLiteTensor* cell_state_quantized =
GetTemporary(context, node, kCellStateQuantized);
cell_state_quantized->type = input_to_output_weights->type;
cell_state_quantized->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqual(cell_state_quantized->dims, cell_state->dims)) {
TfLiteIntArray* cell_state_quantized_size =
TfLiteIntArrayCopy(cell_state->dims);
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, cell_state_quantized,
cell_state_quantized_size));
}
// Allocate temporary tensors to store scaling factors and product scaling
// factors. The latter is a convenience storage which allows to quantize
// a vector once (which produces the scaling factors) and multiply it with
// different matrices (which requires multiplying the scaling factors with
// the scaling factor of the matrix).
node->temporaries->data[kInputScalingFactors] =
op_data->scratch_tensor_index + kInputScalingFactors;
TfLiteTensor* input_sf = GetTemporary(context, node, kInputScalingFactors);
input_sf->type = kTfLiteFloat32;
input_sf->allocation_type = kTfLiteArenaRw;
int scaling_dims[1] = {n_batch};
if (!TfLiteIntArrayEqualsArray(input_sf->dims, 1, scaling_dims)) {
TfLiteIntArray* input_sf_size = TfLiteIntArrayCreate(1);
input_sf_size->data[0] = n_batch;
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, input_sf, input_sf_size));
}
node->temporaries->data[kOutputStateScalingFactors] =
op_data->scratch_tensor_index + kOutputStateScalingFactors;
TfLiteTensor* output_state_sf =
GetTemporary(context, node, kOutputStateScalingFactors);
output_state_sf->type = kTfLiteFloat32;
output_state_sf->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqualsArray(output_state_sf->dims, 1, scaling_dims)) {
TfLiteIntArray* output_state_sf_size = TfLiteIntArrayCreate(1);
output_state_sf_size->data[0] = n_batch;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, output_state_sf,
output_state_sf_size));
}
node->temporaries->data[kProductScalingFactors] =
scratch_tensor_index + kProductScalingFactors;
TfLiteTensor* prod_scaling_factors =
GetTemporary(context, node, kProductScalingFactors);
prod_scaling_factors->type = kTfLiteFloat32;
prod_scaling_factors->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqualsArray(prod_scaling_factors->dims, 1,
scaling_dims)) {
TfLiteIntArray* prod_scaling_factors_size = TfLiteIntArrayCreate(1);
prod_scaling_factors_size->data[0] = n_batch;
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, prod_scaling_factors,
prod_scaling_factors_size));
}
// Allocate a temporary tensor to store the recovered cell weights. Since
// this is used for diagonal matrices, only need to store n_cell values.
node->temporaries->data[kRecoveredCellWeights] =
scratch_tensor_index + kRecoveredCellWeights;
TfLiteTensor* recovered_cell_weights =
GetTemporary(context, node, kRecoveredCellWeights);
recovered_cell_weights->type = kTfLiteFloat32;
recovered_cell_weights->allocation_type = kTfLiteArenaRw;
int recovered_cell_dims[1] = {n_cell};
if (!TfLiteIntArrayEqualsArray(recovered_cell_weights->dims, 1,
recovered_cell_dims)) {
TfLiteIntArray* recovered_cell_weights_size = TfLiteIntArrayCreate(1);
recovered_cell_weights_size->data[0] = n_cell;
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, recovered_cell_weights,
recovered_cell_weights_size));
}
// Allocate a temporary tensor to store the accumulated int32 values.
node->temporaries->data[kAccumScratch] =
scratch_tensor_index + kAccumScratch;
TfLiteTensor* accum_scratch = GetTemporary(context, node, kAccumScratch);
accum_scratch->type = kTfLiteInt32;
accum_scratch->allocation_type = kTfLiteArenaRw;
int accum_scratch_dims[2] = {n_cell, n_batch};
if (!TfLiteIntArrayEqualsArray(accum_scratch->dims, 2,
accum_scratch_dims)) {
TfLiteIntArray* accum_size = TfLiteIntArrayCreate(2);
accum_size->data[0] = n_cell;
accum_size->data[1] = n_batch;
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, accum_scratch, accum_size));
}
node->temporaries->data[kInputZeroPoints] =
op_data->scratch_tensor_index + kInputZeroPoints;
TfLiteTensor* input_zp = GetTemporary(context, node, kInputZeroPoints);
input_zp->type = kTfLiteFloat32;
input_zp->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqualsArray(input_zp->dims, 1, scaling_dims)) {
TfLiteIntArray* input_zp_size = TfLiteIntArrayCreate(1);
input_zp_size->data[0] = n_batch;
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, input_zp, input_zp_size));
}
node->temporaries->data[kOutputStateZeroPoints] =
op_data->scratch_tensor_index + kOutputStateZeroPoints;
TfLiteTensor* output_state_zp =
GetTemporary(context, node, kOutputStateZeroPoints);
output_state_zp->type = kTfLiteFloat32;
output_state_zp->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqualsArray(output_state_zp->dims, 1, scaling_dims)) {
TfLiteIntArray* output_state_zp_size = TfLiteIntArrayCreate(1);
output_state_zp_size->data[0] = n_batch;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, output_state_zp,
output_state_zp_size));
}
node->temporaries->data[kRowSums] = scratch_tensor_index + kRowSums;
TfLiteTensor* row_sums = GetTemporary(context, node, kRowSums);
row_sums->type = kTfLiteInt32;
row_sums->allocation_type = kTfLiteArenaRwPersistent;
int row_sums_rows = use_cifg ? 6 : 8;
const TfLiteTensor* projection_weights = GetOptionalInputTensor(
context, node, lstm::full::kProjectionWeightsTensor);
if (projection_weights != nullptr) {
row_sums_rows += ceil(static_cast<float>(n_output) / n_cell);
}
int row_sums_dims[2] = {row_sums_rows, n_cell};
if (!TfLiteIntArrayEqualsArray(row_sums->dims, 2, row_sums_dims)) {
TfLiteIntArray* row_sums_size = TfLiteIntArrayCreate(2);
row_sums_size->data[0] = row_sums_dims[0];
row_sums_size->data[1] = row_sums_dims[1];
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, row_sums, row_sums_size));
}
}
return kTfLiteOk;
} | 1799 | 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::unidirectional_sequence_lstm::Prepare | tflite::ops::builtin::unidirectional_sequence_lstm::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
const int scratch_tensor_index = op_data->scratch_tensor_index;
// Check we have all the inputs and outputs we need.
bool is_layer_norm_lstm = false;
if (node->inputs->size == 24) {
const TfLiteTensor* forget_layer_norm_coefficients = GetOptionalInputTensor(
context, node, lstm::full::kForgetLayerNormCoefficientsTensor);
if (forget_layer_norm_coefficients == nullptr) {
is_layer_norm_lstm = false;
} else {
is_layer_norm_lstm = true;
}
} else if (node->inputs->size == 20) {
// This is deprecated and is only kept here for backward compatibility.
is_layer_norm_lstm = false;
} else {
context->ReportError(
context, "The LSTM Full kernel expects 20 or 24 inputs. Got %d inputs",
node->inputs->size);
return kTfLiteError;
}
TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
op_data->is_layer_norm_lstm = is_layer_norm_lstm;
// Inferring batch size, number of outputs and sequence length and
// number of cells from the input tensors.
const TfLiteTensor* input = GetInput(context, node, lstm::full::kInputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32);
TF_LITE_ENSURE(context, input->dims->size > 1);
const auto* params =
reinterpret_cast<TfLiteUnidirectionalSequenceLSTMParams*>(
node->builtin_data);
const bool time_major = params->time_major;
const int n_batch = time_major ? input->dims->data[1] : input->dims->data[0];
const int n_input = input->dims->data[2];
const TfLiteTensor* input_to_output_weights =
GetInput(context, node, lstm::full::kInputToOutputWeightsTensor);
const int n_cell = input_to_output_weights->dims->data[0];
TF_LITE_ENSURE_EQ(context, input_to_output_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, input_to_output_weights->dims->data[1], n_input);
const TfLiteTensor* recurrent_to_output_weights =
GetInput(context, node, lstm::full::kRecurrentToOutputWeightsTensor);
TF_LITE_ENSURE_EQ(context, recurrent_to_output_weights->dims->size, 2);
TF_LITE_ENSURE_EQ(context, recurrent_to_output_weights->dims->data[0],
n_cell);
const int n_output = recurrent_to_output_weights->dims->data[1];
// Check that input tensor dimensions matches with each other.
TF_LITE_ENSURE_OK(context,
CheckInputTensorDimensions(context, node, n_input, n_output,
n_cell, is_layer_norm_lstm));
// Get the pointer to output, output_state and cell_state buffer tensors.
TfLiteTensor* output = GetOutput(context, node, lstm::full::kOutputTensor);
TfLiteTensor* output_state =
GetVariableInput(context, node, lstm::full::kOutputStateTensor);
TF_LITE_ENSURE(context, output_state != nullptr);
TfLiteTensor* cell_state =
GetVariableInput(context, node, lstm::full::kCellStateTensor);
TF_LITE_ENSURE(context, cell_state != nullptr);
// Check the shape of input state tensors.
// These tensor may be 1D or 2D. It's fine as long as the total size is
// correct.
TF_LITE_ENSURE_EQ(context, NumElements(output_state), n_batch * n_output);
TF_LITE_ENSURE_EQ(context, NumElements(cell_state), n_batch * n_cell);
// Resize the output tensors.
TfLiteIntArray* output_size = TfLiteIntArrayCopy(input->dims);
output_size->data[input->dims->size - 1] = n_output;
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, output, output_size));
TfLiteIntArrayFree(node->temporaries);
if (IsHybridOp(input, input_to_output_weights)) {
node->temporaries = TfLiteIntArrayCreate(kNumTemporaryTensors);
} else {
node->temporaries = TfLiteIntArrayCreate(1);
}
node->temporaries->data[kScratchBuffer] =
scratch_tensor_index + kScratchBuffer;
// Create a scratch buffer tensor.
TfLiteTensor* scratch_buffer = GetTemporary(context, node, kScratchBuffer);
scratch_buffer->type = input->type;
scratch_buffer->allocation_type = kTfLiteArenaRw;
const TfLiteTensor* input_to_input_weights = GetOptionalInputTensor(
context, node, lstm::full::kInputToInputWeightsTensor);
const bool use_cifg = (input_to_input_weights == nullptr);
TfLiteIntArray* scratch_buffer_size = TfLiteIntArrayCreate(2);
scratch_buffer_size->data[0] = n_batch;
if (use_cifg) {
// Reserving space for Cell, Forget, Output gates
scratch_buffer_size->data[1] = n_cell * 3;
} else {
// Reserving space for Input, Cell, Forget, Output gates
scratch_buffer_size->data[1] = n_cell * 4;
}
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_buffer,
scratch_buffer_size));
if (IsHybridOp(input, input_to_output_weights)) {
op_data->compute_row_sums = true;
// Allocate temporary tensors to store quantized values of input,
// output_state and cell_state tensors.
node->temporaries->data[kInputQuantized] =
scratch_tensor_index + kInputQuantized;
TfLiteTensor* input_quantized =
GetTemporary(context, node, kInputQuantized);
input_quantized->type = input_to_output_weights->type;
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[kOutputStateQuantized] =
scratch_tensor_index + kOutputStateQuantized;
TfLiteTensor* output_state_quantized =
GetTemporary(context, node, kOutputStateQuantized);
output_state_quantized->type = input_to_output_weights->type;
output_state_quantized->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqual(output_state_quantized->dims,
output_state->dims)) {
TfLiteIntArray* output_state_quantized_size =
TfLiteIntArrayCopy(output_state->dims);
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, output_state_quantized,
output_state_quantized_size));
}
node->temporaries->data[kCellStateQuantized] =
scratch_tensor_index + kCellStateQuantized;
TfLiteTensor* cell_state_quantized =
GetTemporary(context, node, kCellStateQuantized);
cell_state_quantized->type = input_to_output_weights->type;
cell_state_quantized->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqual(cell_state_quantized->dims, cell_state->dims)) {
TfLiteIntArray* cell_state_quantized_size =
TfLiteIntArrayCopy(cell_state->dims);
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, cell_state_quantized,
cell_state_quantized_size));
}
// Allocate temporary tensors to store scaling factors and product scaling
// factors. The latter is a convenience storage which allows to quantize
// a vector once (which produces the scaling factors) and multiply it with
// different matrices (which requires multiplying the scaling factors with
// the scaling factor of the matrix).
node->temporaries->data[kInputScalingFactors] =
op_data->scratch_tensor_index + kInputScalingFactors;
TfLiteTensor* input_sf = GetTemporary(context, node, kInputScalingFactors);
input_sf->type = kTfLiteFloat32;
input_sf->allocation_type = kTfLiteArenaRw;
int scaling_dims[1] = {n_batch};
if (!TfLiteIntArrayEqualsArray(input_sf->dims, 1, scaling_dims)) {
TfLiteIntArray* input_sf_size = TfLiteIntArrayCreate(1);
input_sf_size->data[0] = n_batch;
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, input_sf, input_sf_size));
}
node->temporaries->data[kOutputStateScalingFactors] =
op_data->scratch_tensor_index + kOutputStateScalingFactors;
TfLiteTensor* output_state_sf =
GetTemporary(context, node, kOutputStateScalingFactors);
output_state_sf->type = kTfLiteFloat32;
output_state_sf->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqualsArray(output_state_sf->dims, 1, scaling_dims)) {
TfLiteIntArray* output_state_sf_size = TfLiteIntArrayCreate(1);
output_state_sf_size->data[0] = n_batch;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, output_state_sf,
output_state_sf_size));
}
node->temporaries->data[kProductScalingFactors] =
scratch_tensor_index + kProductScalingFactors;
TfLiteTensor* prod_scaling_factors =
GetTemporary(context, node, kProductScalingFactors);
prod_scaling_factors->type = kTfLiteFloat32;
prod_scaling_factors->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqualsArray(prod_scaling_factors->dims, 1,
scaling_dims)) {
TfLiteIntArray* prod_scaling_factors_size = TfLiteIntArrayCreate(1);
prod_scaling_factors_size->data[0] = n_batch;
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, prod_scaling_factors,
prod_scaling_factors_size));
}
// Allocate a temporary tensor to store the recovered cell weights. Since
// this is used for diagonal matrices, only need to store n_cell values.
node->temporaries->data[kRecoveredCellWeights] =
scratch_tensor_index + kRecoveredCellWeights;
TfLiteTensor* recovered_cell_weights =
GetTemporary(context, node, kRecoveredCellWeights);
recovered_cell_weights->type = kTfLiteFloat32;
recovered_cell_weights->allocation_type = kTfLiteArenaRw;
int recovered_cell_dims[1] = {n_cell};
if (!TfLiteIntArrayEqualsArray(recovered_cell_weights->dims, 1,
recovered_cell_dims)) {
TfLiteIntArray* recovered_cell_weights_size = TfLiteIntArrayCreate(1);
recovered_cell_weights_size->data[0] = n_cell;
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, recovered_cell_weights,
recovered_cell_weights_size));
}
// Allocate a temporary tensor to store the accumulated int32 values.
node->temporaries->data[kAccumScratch] =
scratch_tensor_index + kAccumScratch;
TfLiteTensor* accum_scratch = GetTemporary(context, node, kAccumScratch);
accum_scratch->type = kTfLiteInt32;
accum_scratch->allocation_type = kTfLiteArenaRw;
int accum_scratch_dims[2] = {n_cell, n_batch};
if (!TfLiteIntArrayEqualsArray(accum_scratch->dims, 2,
accum_scratch_dims)) {
TfLiteIntArray* accum_size = TfLiteIntArrayCreate(2);
accum_size->data[0] = n_cell;
accum_size->data[1] = n_batch;
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, accum_scratch, accum_size));
}
node->temporaries->data[kInputZeroPoints] =
op_data->scratch_tensor_index + kInputZeroPoints;
TfLiteTensor* input_zp = GetTemporary(context, node, kInputZeroPoints);
input_zp->type = kTfLiteFloat32;
input_zp->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqualsArray(input_zp->dims, 1, scaling_dims)) {
TfLiteIntArray* input_zp_size = TfLiteIntArrayCreate(1);
input_zp_size->data[0] = n_batch;
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, input_zp, input_zp_size));
}
node->temporaries->data[kOutputStateZeroPoints] =
op_data->scratch_tensor_index + kOutputStateZeroPoints;
TfLiteTensor* output_state_zp =
GetTemporary(context, node, kOutputStateZeroPoints);
output_state_zp->type = kTfLiteFloat32;
output_state_zp->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqualsArray(output_state_zp->dims, 1, scaling_dims)) {
TfLiteIntArray* output_state_zp_size = TfLiteIntArrayCreate(1);
output_state_zp_size->data[0] = n_batch;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, output_state_zp,
output_state_zp_size));
}
node->temporaries->data[kRowSums] = scratch_tensor_index + kRowSums;
TfLiteTensor* row_sums = GetTemporary(context, node, kRowSums);
row_sums->type = kTfLiteInt32;
row_sums->allocation_type = kTfLiteArenaRwPersistent;
int row_sums_rows = use_cifg ? 6 : 8;
const TfLiteTensor* projection_weights = GetOptionalInputTensor(
context, node, lstm::full::kProjectionWeightsTensor);
if (projection_weights != nullptr) {
row_sums_rows += ceil(static_cast<float>(n_output) / n_cell);
}
int row_sums_dims[2] = {row_sums_rows, n_cell};
if (!TfLiteIntArrayEqualsArray(row_sums->dims, 2, row_sums_dims)) {
TfLiteIntArray* row_sums_size = TfLiteIntArrayCreate(2);
row_sums_size->data[0] = row_sums_dims[0];
row_sums_size->data[1] = row_sums_dims[1];
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, row_sums, row_sums_size));
}
}
return kTfLiteOk;
} | 1799 | 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::unidirectional_sequence_rnn::Eval | tflite::ops::builtin::unidirectional_sequence_rnn::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteSequenceRNNParams*>(node->builtin_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
const TfLiteTensor* recurrent_weights =
GetInput(context, node, kRecurrentWeightsTensor);
const TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
// The hidden_state is a variable input tensor that can be modified.
TfLiteTensor* hidden_state =
const_cast<TfLiteTensor*>(GetInput(context, node, kHiddenStateTensor));
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
switch (input_weights->type) {
case kTfLiteFloat32:
return EvalFloat(input, input_weights, recurrent_weights, bias, params,
hidden_state, output);
case kTfLiteUInt8:
case kTfLiteInt8: {
// TODO(mirkov): implement eval with quantized inputs as well.
auto* op_data = reinterpret_cast<OpData*>(node->user_data);
TfLiteTensor* input_quantized = GetTemporary(context, node, 0);
TfLiteTensor* hidden_state_quantized = GetTemporary(context, node, 1);
TfLiteTensor* scaling_factors = GetTemporary(context, node, 2);
TfLiteTensor* accum_scratch = GetTemporary(context, node, 3);
TfLiteTensor* zero_points = GetTemporary(context, node, 4);
TfLiteTensor* row_sums = GetTemporary(context, node, 5);
return EvalHybrid(input, input_weights, recurrent_weights, bias, params,
input_quantized, hidden_state_quantized,
scaling_factors, hidden_state, output, zero_points,
accum_scratch, row_sums, &op_data->compute_row_sums);
}
default:
TF_LITE_KERNEL_LOG(context, "Type %d not currently supported.",
TfLiteTypeGetName(input_weights->type));
return kTfLiteError;
}
return kTfLiteOk;
} | 303 | 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::unidirectional_sequence_rnn::Eval | tflite::ops::builtin::unidirectional_sequence_rnn::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteSequenceRNNParams*>(node->builtin_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
const TfLiteTensor* recurrent_weights =
GetInput(context, node, kRecurrentWeightsTensor);
const TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
// The hidden_state is a variable input tensor that can be modified.
TfLiteTensor* hidden_state =
const_cast<TfLiteTensor*>(GetInput(context, node, kHiddenStateTensor));
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
switch (input_weights->type) {
case kTfLiteFloat32:
return EvalFloat(input, input_weights, recurrent_weights, bias, params,
hidden_state, output);
case kTfLiteUInt8:
case kTfLiteInt8: {
// TODO(mirkov): implement eval with quantized inputs as well.
auto* op_data = reinterpret_cast<OpData*>(node->user_data);
TfLiteTensor* input_quantized = GetTemporary(context, node, 0);
TfLiteTensor* hidden_state_quantized = GetTemporary(context, node, 1);
TfLiteTensor* scaling_factors = GetTemporary(context, node, 2);
TfLiteTensor* accum_scratch = GetTemporary(context, node, 3);
TfLiteTensor* zero_points = GetTemporary(context, node, 4);
TfLiteTensor* row_sums = GetTemporary(context, node, 5);
return EvalHybrid(input, input_weights, recurrent_weights, bias, params,
input_quantized, hidden_state_quantized,
scaling_factors, hidden_state, output, zero_points,
accum_scratch, row_sums, &op_data->compute_row_sums);
}
default:
TF_LITE_KERNEL_LOG(context, "Type %d not currently supported.",
TfLiteTypeGetName(input_weights->type));
return kTfLiteError;
}
return kTfLiteOk;
} | 303 | 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::unidirectional_sequence_rnn::Prepare | tflite::ops::builtin::unidirectional_sequence_rnn::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
// Check we have all the inputs and outputs we need.
TF_LITE_ENSURE_EQ(context, node->inputs->size, 5);
TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
const TfLiteTensor* recurrent_weights =
GetInput(context, node, kRecurrentWeightsTensor);
const TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
const TfLiteTensor* hidden_state =
GetInput(context, node, kHiddenStateTensor);
// Check all the parameters of tensor match within themselves and match the
// input configuration.
auto* params = reinterpret_cast<TfLiteSequenceRNNParams*>(node->builtin_data);
const bool time_major = params->time_major;
const int batch_size =
(time_major) ? input->dims->data[1] : input->dims->data[0];
const int max_time =
(time_major) ? input->dims->data[0] : input->dims->data[1];
const int num_units = input_weights->dims->data[0];
TF_LITE_ENSURE_EQ(context, input->dims->data[2],
input_weights->dims->data[1]);
TF_LITE_ENSURE_EQ(context, input_weights->dims->data[0], bias->dims->data[0]);
TF_LITE_ENSURE_EQ(context, recurrent_weights->dims->data[0],
bias->dims->data[0]);
TF_LITE_ENSURE_EQ(context, recurrent_weights->dims->data[1],
bias->dims->data[0]);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32);
TF_LITE_ENSURE_TYPES_EQ(context, input_weights->type,
recurrent_weights->type);
TF_LITE_ENSURE_EQ(context, NumDimensions(hidden_state), 2);
TF_LITE_ENSURE_EQ(context, hidden_state->dims->data[0], batch_size);
TF_LITE_ENSURE_EQ(context, hidden_state->dims->data[1], num_units);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// Resize output.
TfLiteIntArray* output_size_array = TfLiteIntArrayCreate(3);
output_size_array->data[0] = (time_major) ? max_time : batch_size;
output_size_array->data[1] = (time_major) ? batch_size : max_time;
output_size_array->data[2] = num_units;
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, output, output_size_array));
const bool is_hybrid = IsHybridOp(input, input_weights);
// Allocate temporary tensors to store quantized values of input and
// hidden_state tensors.
if (is_hybrid) {
auto* op_data = reinterpret_cast<OpData*>(node->user_data);
op_data->compute_row_sums = true;
TfLiteIntArrayFree(node->temporaries);
node->temporaries = TfLiteIntArrayCreate(6);
node->temporaries->data[0] = op_data->scratch_tensor_index;
TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/0);
input_quantized->type = input_weights->type;
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[1] = op_data->scratch_tensor_index + 1;
TfLiteTensor* hidden_state_quantized =
GetTemporary(context, node, /*index=*/1);
hidden_state_quantized->type = input_weights->type;
hidden_state_quantized->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqual(hidden_state_quantized->dims,
hidden_state->dims)) {
TfLiteIntArray* hidden_state_quantized_size =
TfLiteIntArrayCopy(hidden_state->dims);
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, hidden_state_quantized,
hidden_state_quantized_size));
}
node->temporaries->data[2] = op_data->scratch_tensor_index + 2;
TfLiteTensor* scaling_factors = GetTemporary(context, node, /*index=*/2);
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[3] = op_data->scratch_tensor_index + 3;
TfLiteTensor* accum_scratch = GetTemporary(context, node, /*index=*/3);
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_scratch_size = TfLiteIntArrayCreate(2);
accum_scratch_size->data[0] = accum_scratch_dims[0];
accum_scratch_size->data[1] = accum_scratch_dims[1];
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, accum_scratch,
accum_scratch_size));
}
node->temporaries->data[4] = op_data->scratch_tensor_index + 4;
TfLiteTensor* zero_points = GetTemporary(context, node, /*index=*/4);
zero_points->type = kTfLiteInt32;
zero_points->allocation_type = kTfLiteArenaRw;
int zero_points_dims[1] = {batch_size};
if (!TfLiteIntArrayEqualsArray(zero_points->dims, 1, zero_points_dims)) {
TfLiteIntArray* zero_points_size = TfLiteIntArrayCreate(1);
zero_points_size->data[0] = batch_size;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, zero_points,
zero_points_size));
}
node->temporaries->data[5] = op_data->scratch_tensor_index + 5;
TfLiteTensor* row_sums = GetTemporary(context, node, /*index=*/5);
row_sums->type = kTfLiteInt32;
row_sums->allocation_type = kTfLiteArenaRwPersistent;
int row_sums_dims[2] = {2, num_units};
if (!TfLiteIntArrayEqualsArray(row_sums->dims, 2, row_sums_dims)) {
TfLiteIntArray* row_sums_size = TfLiteIntArrayCreate(2);
row_sums_size->data[0] = row_sums_dims[0];
row_sums_size->data[1] = row_sums_dims[1];
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, row_sums, row_sums_size));
}
}
return kTfLiteOk;
} | 1092 | 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::unidirectional_sequence_rnn::Prepare | tflite::ops::builtin::unidirectional_sequence_rnn::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
// Check we have all the inputs and outputs we need.
TF_LITE_ENSURE_EQ(context, node->inputs->size, 5);
TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
const TfLiteTensor* recurrent_weights =
GetInput(context, node, kRecurrentWeightsTensor);
const TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
const TfLiteTensor* hidden_state =
GetInput(context, node, kHiddenStateTensor);
// Check all the parameters of tensor match within themselves and match the
// input configuration.
auto* params = reinterpret_cast<TfLiteSequenceRNNParams*>(node->builtin_data);
const bool time_major = params->time_major;
const int batch_size =
(time_major) ? input->dims->data[1] : input->dims->data[0];
const int max_time =
(time_major) ? input->dims->data[0] : input->dims->data[1];
const int num_units = input_weights->dims->data[0];
TF_LITE_ENSURE_EQ(context, input->dims->data[2],
input_weights->dims->data[1]);
TF_LITE_ENSURE_EQ(context, input_weights->dims->data[0], bias->dims->data[0]);
TF_LITE_ENSURE_EQ(context, recurrent_weights->dims->data[0],
bias->dims->data[0]);
TF_LITE_ENSURE_EQ(context, recurrent_weights->dims->data[1],
bias->dims->data[0]);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32);
TF_LITE_ENSURE_TYPES_EQ(context, input_weights->type,
recurrent_weights->type);
TF_LITE_ENSURE_EQ(context, NumDimensions(hidden_state), 2);
TF_LITE_ENSURE_EQ(context, hidden_state->dims->data[0], batch_size);
TF_LITE_ENSURE_EQ(context, hidden_state->dims->data[1], num_units);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// Resize output.
TfLiteIntArray* output_size_array = TfLiteIntArrayCreate(3);
output_size_array->data[0] = (time_major) ? max_time : batch_size;
output_size_array->data[1] = (time_major) ? batch_size : max_time;
output_size_array->data[2] = num_units;
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, output, output_size_array));
const bool is_hybrid = IsHybridOp(input, input_weights);
// Allocate temporary tensors to store quantized values of input and
// hidden_state tensors.
if (is_hybrid) {
auto* op_data = reinterpret_cast<OpData*>(node->user_data);
op_data->compute_row_sums = true;
TfLiteIntArrayFree(node->temporaries);
node->temporaries = TfLiteIntArrayCreate(6);
node->temporaries->data[0] = op_data->scratch_tensor_index;
TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/0);
input_quantized->type = input_weights->type;
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[1] = op_data->scratch_tensor_index + 1;
TfLiteTensor* hidden_state_quantized =
GetTemporary(context, node, /*index=*/1);
hidden_state_quantized->type = input_weights->type;
hidden_state_quantized->allocation_type = kTfLiteArenaRw;
if (!TfLiteIntArrayEqual(hidden_state_quantized->dims,
hidden_state->dims)) {
TfLiteIntArray* hidden_state_quantized_size =
TfLiteIntArrayCopy(hidden_state->dims);
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, hidden_state_quantized,
hidden_state_quantized_size));
}
node->temporaries->data[2] = op_data->scratch_tensor_index + 2;
TfLiteTensor* scaling_factors = GetTemporary(context, node, /*index=*/2);
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[3] = op_data->scratch_tensor_index + 3;
TfLiteTensor* accum_scratch = GetTemporary(context, node, /*index=*/3);
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_scratch_size = TfLiteIntArrayCreate(2);
accum_scratch_size->data[0] = accum_scratch_dims[0];
accum_scratch_size->data[1] = accum_scratch_dims[1];
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, accum_scratch,
accum_scratch_size));
}
node->temporaries->data[4] = op_data->scratch_tensor_index + 4;
TfLiteTensor* zero_points = GetTemporary(context, node, /*index=*/4);
zero_points->type = kTfLiteInt32;
zero_points->allocation_type = kTfLiteArenaRw;
int zero_points_dims[1] = {batch_size};
if (!TfLiteIntArrayEqualsArray(zero_points->dims, 1, zero_points_dims)) {
TfLiteIntArray* zero_points_size = TfLiteIntArrayCreate(1);
zero_points_size->data[0] = batch_size;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, zero_points,
zero_points_size));
}
node->temporaries->data[5] = op_data->scratch_tensor_index + 5;
TfLiteTensor* row_sums = GetTemporary(context, node, /*index=*/5);
row_sums->type = kTfLiteInt32;
row_sums->allocation_type = kTfLiteArenaRwPersistent;
int row_sums_dims[2] = {2, num_units};
if (!TfLiteIntArrayEqualsArray(row_sums->dims, 2, row_sums_dims)) {
TfLiteIntArray* row_sums_size = TfLiteIntArrayCreate(2);
row_sums_size->data[0] = row_sums_dims[0];
row_sums_size->data[1] = row_sums_dims[1];
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, row_sums, row_sums_size));
}
}
return kTfLiteOk;
} | 1092 | 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::unique::Eval | tflite::ops::builtin::unique::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output_index_tensor = GetOutput(context, node, 1);
TF_LITE_ENSURE_EQ(context, NumElements(output_index_tensor),
NumElements(input));
switch (input->type) {
case kTfLiteInt8:
TF_LITE_ENSURE_STATUS(EvalImpl<int8_t>(context, input, node));
break;
case kTfLiteInt16:
TF_LITE_ENSURE_STATUS(EvalImpl<int16_t>(context, input, node));
break;
case kTfLiteInt32:
TF_LITE_ENSURE_STATUS(EvalImpl<int32_t>(context, input, node));
break;
case kTfLiteInt64:
TF_LITE_ENSURE_STATUS(EvalImpl<int64_t>(context, input, node));
break;
case kTfLiteFloat32:
TF_LITE_ENSURE_STATUS(EvalImpl<float>(context, input, node));
break;
case kTfLiteUInt8:
TF_LITE_ENSURE_STATUS(EvalImpl<uint8_t>(context, input, node));
break;
default:
context->ReportError(context, "Currently Unique doesn't support type: %s",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
return kTfLiteOk;
} | 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::unique::Eval | tflite::ops::builtin::unique::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output_index_tensor = GetOutput(context, node, 1);
TF_LITE_ENSURE_EQ(context, NumElements(output_index_tensor),
NumElements(input));
switch (input->type) {
case kTfLiteInt8:
TF_LITE_ENSURE_STATUS(EvalImpl<int8_t>(context, input, node));
break;
case kTfLiteInt16:
TF_LITE_ENSURE_STATUS(EvalImpl<int16_t>(context, input, node));
break;
case kTfLiteInt32:
TF_LITE_ENSURE_STATUS(EvalImpl<int32_t>(context, input, node));
break;
case kTfLiteInt64:
TF_LITE_ENSURE_STATUS(EvalImpl<int64_t>(context, input, node));
break;
case kTfLiteFloat32:
TF_LITE_ENSURE_STATUS(EvalImpl<float>(context, input, node));
break;
case kTfLiteUInt8:
TF_LITE_ENSURE_STATUS(EvalImpl<uint8_t>(context, input, node));
break;
default:
context->ReportError(context, "Currently Unique doesn't support type: %s",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
return kTfLiteOk;
} | 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::unique::EvalImpl | tflite::ops::builtin::unique::EvalImpl( TfLiteContext * context , const TfLiteTensor * input , TfLiteNode * node) | ['context', 'input', 'node'] | TfLiteStatus EvalImpl(TfLiteContext* context, const TfLiteTensor* input,
TfLiteNode* node) {
// Map from value, to index in the unique elements vector.
// Note that we prefer to use map than unordered_map as it showed less
// increase in the binary size.
std::map<T, int> unique_values;
TfLiteTensor* output_indexes = GetOutput(context, node, 1);
std::vector<T> output_values;
I* indexes = GetTensorData<I>(output_indexes);
const T* data = GetTensorData<T>(input);
const int num_elements = NumElements(input);
for (int i = 0; i < num_elements; ++i) {
const auto element_it = unique_values.find(data[i]);
if (element_it != unique_values.end()) {
indexes[i] = element_it->second;
} else {
const int unique_index = unique_values.size();
unique_values[data[i]] = unique_index;
indexes[i] = unique_index;
output_values.push_back(data[i]);
}
}
// Allocate output tensor.
TfLiteTensor* unique_output = GetOutput(context, node, 0);
std::unique_ptr<TfLiteIntArray, void (*)(TfLiteIntArray*)> shape(
TfLiteIntArrayCreate(NumDimensions(input)), TfLiteIntArrayFree);
shape->data[0] = unique_values.size();
TF_LITE_ENSURE_STATUS(
context->ResizeTensor(context, unique_output, shape.release()));
// Set the values in the output tensor.
T* output_unique_values = GetTensorData<T>(unique_output);
for (int i = 0; i < output_values.size(); ++i) {
output_unique_values[i] = output_values[i];
}
return kTfLiteOk;
} | 290 | 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::unique::EvalImpl | tflite::ops::builtin::unique::EvalImpl( TfLiteContext * context , const TfLiteTensor * input , TfLiteNode * node) | ['context', 'input', 'node'] | TfLiteStatus EvalImpl(TfLiteContext* context, const TfLiteTensor* input,
TfLiteNode* node) {
// Map from value, to index in the unique elements vector.
// Note that we prefer to use map than unordered_map as it showed less
// increase in the binary size.
std::map<T, int> unique_values;
TfLiteTensor* output_indexes = GetOutput(context, node, 1);
std::vector<T> output_values;
I* indexes = GetTensorData<I>(output_indexes);
const T* data = GetTensorData<T>(input);
const int num_elements = NumElements(input);
for (int i = 0; i < num_elements; ++i) {
const auto element_it = unique_values.find(data[i]);
if (element_it != unique_values.end()) {
indexes[i] = element_it->second;
} else {
const int unique_index = unique_values.size();
unique_values[data[i]] = unique_index;
indexes[i] = unique_index;
output_values.push_back(data[i]);
}
}
// Allocate output tensor.
TfLiteTensor* unique_output = GetOutput(context, node, 0);
std::unique_ptr<TfLiteIntArray, void (*)(TfLiteIntArray*)> shape(
TfLiteIntArrayCreate(NumDimensions(input)), TfLiteIntArrayFree);
shape->data[0] = unique_values.size();
TF_LITE_ENSURE_STATUS(
context->ResizeTensor(context, unique_output, shape.release()));
// Set the values in the output tensor.
T* output_unique_values = GetTensorData<T>(unique_output);
for (int i = 0; i < output_values.size(); ++i) {
output_unique_values[i] = output_values[i];
}
return kTfLiteOk;
} | 290 | 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::unique::Prepare | tflite::ops::builtin::unique::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
static const int kOutputUniqueTensor = 0;
static const int kOutputIndexTensor = 1;
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output_unique_tensor =
GetOutput(context, node, kOutputUniqueTensor);
TfLiteTensor* output_index_tensor =
GetOutput(context, node, kOutputIndexTensor);
// The op only supports 1D input.
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 1);
TfLiteIntArray* output_index_shape = TfLiteIntArrayCopy(input->dims);
// The unique values are determined during evaluation, so we don't know yet
// the size of the output tensor.
SetTensorToDynamic(output_unique_tensor);
return context->ResizeTensor(context, output_index_tensor,
output_index_shape);
} | 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::unique::Prepare | tflite::ops::builtin::unique::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
static const int kOutputUniqueTensor = 0;
static const int kOutputIndexTensor = 1;
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output_unique_tensor =
GetOutput(context, node, kOutputUniqueTensor);
TfLiteTensor* output_index_tensor =
GetOutput(context, node, kOutputIndexTensor);
// The op only supports 1D input.
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 1);
TfLiteIntArray* output_index_shape = TfLiteIntArrayCopy(input->dims);
// The unique values are determined during evaluation, so we don't know yet
// the size of the output tensor.
SetTensorToDynamic(output_unique_tensor);
return context->ResizeTensor(context, output_index_tensor,
output_index_shape);
} | 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::unpack::Eval | tflite::ops::builtin::unpack::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteUnpackParams* data =
reinterpret_cast<TfLiteUnpackParams*>(node->builtin_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
switch (input->type) {
case kTfLiteFloat32: {
UnpackImpl<float>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteInt32: {
UnpackImpl<int32_t>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteUInt8: {
UnpackImpl<uint8_t>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteInt8: {
UnpackImpl<int8_t>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteBool: {
UnpackImpl<bool>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteInt16: {
UnpackImpl<int16_t>(context, node, input, data->num, data->axis);
break;
}
default: {
context->ReportError(context, "Type '%s' is not supported by unpack.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
}
return kTfLiteOk;
} | 238 | 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::unpack::Eval | tflite::ops::builtin::unpack::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteUnpackParams* data =
reinterpret_cast<TfLiteUnpackParams*>(node->builtin_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
switch (input->type) {
case kTfLiteFloat32: {
UnpackImpl<float>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteInt32: {
UnpackImpl<int32_t>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteUInt8: {
UnpackImpl<uint8_t>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteInt8: {
UnpackImpl<int8_t>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteBool: {
UnpackImpl<bool>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteInt16: {
UnpackImpl<int16_t>(context, node, input, data->num, data->axis);
break;
}
default: {
context->ReportError(context, "Type '%s' is not supported by unpack.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
}
return kTfLiteOk;
} | 238 | 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::unpack::Prepare | tflite::ops::builtin::unpack::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
const TfLiteUnpackParams* data =
reinterpret_cast<TfLiteUnpackParams*>(node->builtin_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), data->num);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TF_LITE_ENSURE(context, NumElements(input) > 0);
int axis = data->axis;
if (axis < 0) {
axis += NumDimensions(input);
}
TF_LITE_ENSURE(context, 0 <= axis && axis < NumDimensions(input));
if (input->type != kTfLiteInt32 && input->type != kTfLiteFloat32 &&
input->type != kTfLiteUInt8 && input->type != kTfLiteInt8 &&
input->type != kTfLiteInt16 && input->type != kTfLiteBool) {
context->ReportError(context, "Type '%s' is not supported by unpack.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
const TfLiteIntArray* input_shape = input->dims;
// Num should be equal to the shape[axis].
// Resize outputs. rank will be R - 1.
TfLiteIntArray* output_shape = TfLiteIntArrayCreate(NumDimensions(input) - 1);
int o = 0;
for (int index = 0; index < NumDimensions(input); ++index) {
if (index != axis) {
output_shape->data[o++] = input_shape->data[index];
}
}
TF_LITE_ENSURE_EQ(context, data->num, input_shape->data[axis]);
for (int i = 0; i < data->num; ++i) {
TfLiteIntArray* copied_output_shape = TfLiteIntArrayCopy(output_shape);
TfLiteTensor* output = GetOutput(context, node, i);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, input->type);
// Guarantee input/output quantization params match as we do not support
// rescaling of unpacked quantized tensors.
TF_LITE_ENSURE_EQ(context, input->params.zero_point,
output->params.zero_point);
TF_LITE_ENSURE_EQ(context, input->params.scale, output->params.scale);
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, output, copied_output_shape));
}
TfLiteIntArrayFree(output_shape);
return kTfLiteOk;
} | 374 | 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::unpack::Prepare | tflite::ops::builtin::unpack::Prepare( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
const TfLiteUnpackParams* data =
reinterpret_cast<TfLiteUnpackParams*>(node->builtin_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), data->num);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TF_LITE_ENSURE(context, NumElements(input) > 0);
int axis = data->axis;
if (axis < 0) {
axis += NumDimensions(input);
}
TF_LITE_ENSURE(context, 0 <= axis && axis < NumDimensions(input));
if (input->type != kTfLiteInt32 && input->type != kTfLiteFloat32 &&
input->type != kTfLiteUInt8 && input->type != kTfLiteInt8 &&
input->type != kTfLiteInt16 && input->type != kTfLiteBool) {
context->ReportError(context, "Type '%s' is not supported by unpack.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
const TfLiteIntArray* input_shape = input->dims;
// Num should be equal to the shape[axis].
// Resize outputs. rank will be R - 1.
TfLiteIntArray* output_shape = TfLiteIntArrayCreate(NumDimensions(input) - 1);
int o = 0;
for (int index = 0; index < NumDimensions(input); ++index) {
if (index != axis) {
output_shape->data[o++] = input_shape->data[index];
}
}
TF_LITE_ENSURE_EQ(context, data->num, input_shape->data[axis]);
for (int i = 0; i < data->num; ++i) {
TfLiteIntArray* copied_output_shape = TfLiteIntArrayCopy(output_shape);
TfLiteTensor* output = GetOutput(context, node, i);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, input->type);
// Guarantee input/output quantization params match as we do not support
// rescaling of unpacked quantized tensors.
TF_LITE_ENSURE_EQ(context, input->params.zero_point,
output->params.zero_point);
TF_LITE_ENSURE_EQ(context, input->params.scale, output->params.scale);
TF_LITE_ENSURE_OK(
context, context->ResizeTensor(context, output, copied_output_shape));
}
TfLiteIntArrayFree(output_shape);
return kTfLiteOk;
} | 374 | 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::where::Eval | tflite::ops::builtin::where::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* cond_tensor =
GetInput(context, node, kInputConditionTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (IsDynamicTensor(output)) {
TF_LITE_ENSURE_OK(context,
ResizeOutputTensor(context, cond_tensor, output));
}
TfLiteIntArray* dims = cond_tensor->dims;
if (dims->size == 0) {
// Scalar tensors are not supported.
TF_LITE_KERNEL_LOG(context, "Where op requires condition w/ rank > 0");
return kTfLiteError;
}
reference_ops::SelectTrueCoords(GetTensorShape(cond_tensor),
GetTensorData<bool>(cond_tensor),
GetTensorData<int64_t>(output));
return kTfLiteOk;
} | 119 | 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::where::Eval | tflite::ops::builtin::where::Eval( TfLiteContext * context , TfLiteNode * node) | ['context', 'node'] | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* cond_tensor =
GetInput(context, node, kInputConditionTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (IsDynamicTensor(output)) {
TF_LITE_ENSURE_OK(context,
ResizeOutputTensor(context, cond_tensor, output));
}
TfLiteIntArray* dims = cond_tensor->dims;
if (dims->size == 0) {
// Scalar tensors are not supported.
TF_LITE_KERNEL_LOG(context, "Where op requires condition w/ rank > 0");
return kTfLiteError;
}
reference_ops::SelectTrueCoords(GetTensorShape(cond_tensor),
GetTensorData<bool>(cond_tensor),
GetTensorData<int64_t>(output));
return kTfLiteOk;
} | 119 | True | 1 |
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