Insu Yun
Georgia Institute of Technology
Distinguished Paper Award Winner!
Abstract:
Recently, hybrid fuzzing has been proposed to address the limitations of fuzzing and concolic execution by combining both approaches. The hybrid approach has shown its effectiveness in various synthetic benchmarks such as DARPA Cyber Grand Challenge (CGC) binaries, but it still suffers from scaling to find bugs in complex, real-world software. We observed that the performance bottleneck of the existing concolic executor is the main limiting factor for its adoption beyond a small-scale study.
To overcome this problem, we design a fast concolic execution engine, called QSYM, to support hybrid fuzzing. The key idea is to tightly integrate the symbolic emulation with the native execution using dynamic binary translation, making it possible to implement more fine-grained, so faster, instruction-level symbolic emulation. Additionally, QSYM loosens the strict soundness requirements of conventional concolic executors for better performance, yet takes advantage of a faster fuzzer for validation, providing unprecedented opportunities for performance optimizations, e.g., optimistically solving constraints and pruning uninteresting basic blocks.
Our evaluation shows that QSYM does not just outperform state-of-the-art fuzzers (i.e., found 14× more bugs than VUzzer in the LAVA-M dataset, and outperformed Driller in 104 binaries out of 126), but also found 13 previously unknown security bugs in eight real-world programs like Dropbox Lepton, ffmpeg, and OpenJPEG, which have already been intensively tested by the state-of-the-art fuzzers, AFL and OSS-Fuzz.
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