ParmeSan: Sanitizer-guided Greybox Fuzzing
Sebastian Österlund, Kaveh Razavi, Herbert Bos, Cristiano Giuffrida
摘要
One of the key questions when fuzzing is where to look for vulnerabilities. Coverage-guided fuzzers indiscriminately optimize for covering as much code as possible given that bug coverage often correlates with code coverage. Since code coverage overapproximates bug coverage, this approach is less than ideal and may lead to non-trivial timeto-exposure (TTE) of bugs. Directed fuzzers try to address this problem by directing the fuzzer to a basic block with a potential vulnerability. This approach can greatly reduce the TTE for a specific bug, but such special-purpose fuzzers can then greatly underapproximate overall bug coverage. In this paper, we present sanitizer-guided fuzzing, a new design point in this space that specifically optimizes for bug coverage. For this purpose, we make the key observation that while the instrumentation performed by existing software sanitizers are regularly used for detecting fuzzer-induced error conditions, they can further serve as a generic and effective mechanism to identify interesting basic blocks for guiding fuzzers. We present the design and implementation of ParmeSan, a new sanitizer-guided fuzzer that builds on this observation. We show that ParmeSan greatly reduces the TTE of real-world bugs, and finds bugs 37% faster than existing state-of-the-art coverage-based fuzzers (Angora) and 288% faster than directed fuzzers (AFLGo), while still covering the same set of bugs.
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引用它的顶会 Paper64
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它引用的顶会 Paper14
- Coverage-based Greybox Fuzzing as Markov ChainMarcel Böhme, Van-Thuan Pham, Abhik RoychoudhuryCCS 2016 · 被引用 1,026 次
- Directed Greybox FuzzingMarcel Böhme, Van-Thuan Pham, Manh-Dung Nguyen, Abhik RoychoudhuryCCS 2017 · 被引用 836 次
- Angora: Efficient Fuzzing by Principled SearchPeng Chen, Hao ChenS&P 2018 · 被引用 616 次
- QSYM : A Practical Concolic Execution Engine Tailored for Hybrid FuzzingInsu Yun, Sangho Lee, Meng Xu, Yeongjin Jang 等USENIX Security 2018 · 被引用 537 次
- CollAFL: Path Sensitive FuzzingShuitao Gan, Chao Zhang, Xiaojun Qin, Xuwen Tu 等S&P 2018 · 被引用 426 次
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