Programs expecting structured inputs often consist of both a syntactic analysis stage in which raw input is parsed into an internal data structure and a semantic analysis stage which conducts checks on this data structure and executes the core logic of the program. Existing random testing methodologies, like coverage-guided fuzzing (CGF) and generator-based fuzzing, tend to produce inputs that are rejected early in one of these two stages. We propose Zest, a random testing methodology that effectively explores the semantic analysis stages of such programs. Zest combines two key innovations to achieve this. First, we introduce validity fuzzing, which biases CGF towards generating semantically valid inputs. Second, we introduce parametric generators, which convert input from a simple parameter domain, such as a sequence of numbers, into a more structured domain, such as syntactically valid XML. These generators enable parameter-level mutations to map to structural mutations in syntactically valid test inputs. We implement Zest in Java and evaluate it against AFL and QuickCheck, popular CGF and generator-based fuzzing tools, on six real-world benchmarks: Apache Maven, Ant, and BCEL, ScalaChess, the Google Closure compiler, and Mozilla Rhino. We find that Zest achieves the highest coverage of the semantic analysis stage for five of these benchmarks. Further, we find 18 new bugs across the benchmarks, including 7 bugs that are uniquely found by Zest.
@cite_55: Device drivers are an essential part in modern Unix-like systems to handle operations on physical devices, from hard disks and printers to digital cameras and Bluetooth speakers. The surge of new hardware, particularly on mobile devices, introduces an explosive growth of device drivers in system kernels. Many such drivers are provided by third-party developers, which are susceptible to security vulnerabilities and lack proper vetting. Unfortunately, the complex input data structures for device drivers render traditional analysis tools, such as fuzz testing, less effective, and so far, research on kernel driver security is comparatively sparse. In this paper, we present DIFUZE, an interface-aware fuzzing tool to automatically generate valid inputs and trigger the execution of the kernel drivers. We leverage static analysis to compose correctly-structured input in the userspace to explore kernel drivers. DIFUZE is fully automatic, ranging from identifying driver handlers, to mapping to device file names, to constructing complex argument instances. We evaluate our approach on seven modern Android smartphones. The results show that DIFUZE can effectively identify kernel driver bugs, and reports 32 previously unknown vulnerabilities, including flaws that lead to arbitrary code execution.
@cite_9: Fuzzing consists of repeatedly testing an application with modified, or fuzzed, inputs with the goal of finding security vulnerabilities in input-parsing code. In this paper, we show how to automate the generation of an input grammar suitable for input fuzzing using sample inputs and neural-network-based statistical machine-learning techniques. We present a detailed case study with a complex input format, namely PDF, and a large complex security-critical parser for this format, namely, the PDF parser embedded in Microsoft's new Edge browser. We discuss (and measure) the tension between conflicting learning and fuzzing goals: learning wants to capture the structure of well-formed inputs, while fuzzing wants to break that structure in order to cover unexpected code paths and find bugs. We also present a new algorithm for this learn&fuzz challenge which uses a learnt input probability distribution to intelligently guide where to fuzz inputs.
@cite_62: We present an algorithm for synthesizing a context-free grammar encoding the language of valid program inputs from a set of input examples and blackbox access to the program. Our algorithm addresses shortcomings of existing grammar inference algorithms, which both severely overgeneralize and are prohibitively slow. Our implementation, GLADE, leverages the grammar synthesized by our algorithm to fuzz test programs with structured inputs. We show that GLADE substantially increases the incremental coverage on valid inputs compared to two baseline fuzzers.
Recently, there has been interest in generating input grammars from existing inputs, using machine learning @cite_9 and language inference algorithms @cite_62 . Similarly, DIFUZE @cite_55 infers device driver interfaces from a running kernel to boostrap subsequent structured fuzzing. These techniques are complementary to ---the grammars generated by these techniques could be transformed into parametric generators for .