Matryoshka: Fuzzing Deeply Nested Branches
Peng Chen, Jianzhong Liu, Hao Chen
摘要
Greybox fuzzing has made impressive progress in recent years, evolving from heuristics-based random mutation to solving individual branch constraints. However, they have difficulty solving path constraints that involve deeply nested conditional statements, which are common in image and video decoders, network packet analyzers, and checksum tools. We propose an approach for addressing this problem. First, we identify all the control flow-dependent conditional statements of the target conditional statement. Next, we select the taint flow-dependent conditional statements. Finally, we use three strategies to find an input that satisfies all conditional statements simultaneously. We implemented this approach in a tool called Matryoshka 1 and compared its effectiveness on 13 open source programs with other state-of-the-art fuzzers. Matryoshka achieved significantly higher cumulative line and branch coverage than AFL, QSYM, and Angora. We manually classified the crashes found by Matryoshka into 41 unique new bugs and obtained 12 CVEs. Our evaluation demonstrates the key technique contributing to Matryoshka's impressive performance: among the nesting constraints of a target conditional statement, Matryoshka collects only those that may cause the target unreachable, which greatly simplifies the path constraint that it has to solve. CCS CONCEPTS • Security and privacy → Software security engineering; • Software and its engineering → Software testing and debugging.
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引用它的顶会 Paper34
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它引用的顶会 Paper14
- SOK: (State of) The Art of War: Offensive Techniques in Binary AnalysisYan Shoshitaishvili, Ruoyu Wang, Christopher Salls, Nick Stephens 等S&P 2016 · 被引用 1,085 次
- Driller: Augmenting Fuzzing Through Selective Symbolic ExecutionNick Stephens, John Grosen, Christopher Salls, Andrew Dutcher 等NDSS 2016 · 被引用 1,021 次
- Directed Greybox FuzzingMarcel Böhme, Van-Thuan Pham, Manh-Dung Nguyen, Abhik RoychoudhuryCCS 2017 · 被引用 836 次
- VUzzer: Application-aware Evolutionary FuzzingSanjay Rawat, Vivek Jain, Ashish Kumar, Lucian Cojocar 等NDSS 2017 · 被引用 700 次
- Angora: Efficient Fuzzing by Principled SearchPeng Chen, Hao ChenS&P 2018 · 被引用 616 次
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