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Zeror: Speed Up Fuzzing with coverage-sensitive tracing and Scheduling 35

Zeror: Speed Up Fuzzing with Coverage-sensitive Tracing and ...

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35th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Zhou, Chijin Wang, Mingzhe Liang, Jie Liu, Zhe Jiang, Yu Tsinghua Univ Sch Software KLISS BNRist Beijing Peoples R China NUAA Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781450367684

coverage-guided fuzzing is one of the most popular software testing techniques for vulnerability detection. While effective, current fuzzing methods suffer from significant performance penalty due to instrumentation overhead, which limits its practical use. Existing solutions improve the fuzzing speed by decreasing instrumentation overheads but sacrificing coverage accuracy, which results in unstable performance of vulnerability detection. In this paper, we propose a coverage-sensitive tracing and scheduling framework Zeror that can improve the performance of existing fuzzers, especially in their speed and vulnerability detection. The Zeror is mainly made up of two parts: (1) a self-modifying tracing mechanism to provide a zero-overhead instrumentation for more effective coverage collection, and (2) a real-time scheduling mechanism to support adaptive switch between the zero-overhead instrumented binary and the fully instrumented binary for better vulnerability detection. In this way, Zeror is able to decrease collection overhead and preserve fine-grained coverage for guidance. For evaluation, we implement a prototype of Zeror and evaluate it on Google fuzzer-test-suite, which consists of 24 widely-used applications. The results show that Zeror performs better than existing fuzzing speed-up frameworks such as Untracer and INSTRIM, improves the execution speed of the state-of-the-art fuzzers such as AFL and MOPT by 159.80%, helps them achieve better coverage (averagely 10.14% for AFL, 6.91% for MOPT) and detect vulnerabilities faster (averagely 29.00% for AFL, 46.99% for MOPT).

关键词： coverage-guided Fuzzing coverage-sensitive tracing Scheduling

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Zeror: speed up fuzzing with coverage-sensitive tracing and scheduling 20

Zeror: speed up fuzzing with coverage-sensitive tracing and ...

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Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

作者： Chijin Zhou Mingzhe Wang Jie Liang Zhe Liu Yu Jiang Tsinghua University Beijing China NUAA Nanjing China

ISBN: (纸本)9781450367684

coverage-guided fuzzing is one of the most popular software testing techniques for vulnerability detection. While effective, current fuzzing methods suffer from significant performance penalty due to instrumentation overhead, which limits its practical use. Existing solutions improve the fuzzing speed by decreasing instrumentation overheads but sacrificing coverage accuracy, which results in unstable performance of vulnerability *** this paper, we propose a coverage-sensitive tracing and scheduling framework Zeror that can improve the performance of existing fuzzers, especially in their speed and vulnerability detection. The Zeror is mainly made up of two parts: (1) a self-modifying tracing mechanism to provide a zero-overhead instrumentation for more effective coverage collection, and (2) a real-time scheduling mechanism to support adaptive switch between the zero-overhead instrumented binary and the fully instrumented binary for better vulnerability detection. In this way, Zeror is able to decrease collection overhead and preserve fine-grained coverage for *** evaluation, we implement a prototype of Zeror and evaluate it on Google fuzzer-test-suite, which consists of 24 widely-used applications. The results show that Zeror performs better than existing fuzzing speed-up frameworks such as Untracer and INSTRIM, improves the execution speed of the state-of-the-art fuzzers such as AFL and MOPT by 159.80%, helps them achieve better coverage (averagely 10.14% for AFL, 6.91% for MOPT) and detect vulnerabilities faster (averagely 29.00% for AFL, 46.99% for MOPT).

关键词： coverage-sensitive tracing scheduling coverage-guided fuzzing

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