Data Flows in You: Benchmarking and Improving Static Data-flow Analysis on Binary Executables

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Abstract
Data-flow analysis is a critical component of security research. Theoretically, accurate data-flow analysis in binary executables is an undecidable problem, due to complexities of binary code. Practically, many binary analysis engines offer some data-flow analysis capability, but we lack understanding of the accuracy of these analyses, and their limitations. We address this problem by introducing a labeled benchmark data set, including 215, 072 microbenchmark test cases, mapping to 277, 072 binary executables, created specifically to evaluate data-flow analysis implementations. Additionally, we augment our benchmark set with dynamically-discovered data flows from 6 real-world executables. Using our benchmark data set, we evaluate three state of the art data-flow analysis implementations, in angr, Ghidra and Miasm and discuss their very low accuracy and reasons behind it. We further propose three model extensions to static data-flow analysis that significantly improve accuracy, achieving almost perfect recall (0.99) and increasing precision from 0.13 to 0.32 for the GCC compiler, and achieving a recall of 0.86 with a precision increase from 0.12 to 0.22 for Clang. Finally, we show that leveraging these model extensions in a vulnerability-discovery context leads to a tangible improvement in vulnerable instruction identification.
Funding Information
  • United States National Science Foundation (2146518)

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