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Vulnerable Region-Aware Greybox Fuzzing

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Abstract

Fuzzing is known to be one of the most effective techniques to uncover security vulnerabilities of large-scale software systems. During fuzzing, it is crucial to distribute the fuzzing resource appropriately so as to achieve the best fuzzing performance under a limited budget. Existing distribution strategies of American Fuzzy Lop (AFL) based greybox fuzzing focus on increasing coverage blindly without considering the metrics of code regions, thus lacking the insight regarding which region is more likely to be vulnerable and deserves more fuzzing resources. We tackle the above drawback by proposing a vulnerable region-aware greybox fuzzing approach. Specifically, we distribute more fuzzing resources towards regions that are more likely to be vulnerable based on four kinds of code metrics. We implemented the approach as an extension to AFL named RegionFuzz. Large-scale experimental evaluations validate the effectiveness and efficiency of RegionFuzz-11 new bugs including three new CVEs are successfully uncovered by RegionFuzz.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Ling-Yun Situ, Zhi-Qiang Zuo, Lin-Zhang Wang & Xuan-Dong Li

  2. School of Information Management, Nanjing University, Nanjing, 210023, China

    Ling-Yun Situ & Jin Shi

  3. Department of Computer Science, University of Georgia, Athens, GA, 30602, USA

    Le Guan

  4. College of Information Sciences and Technology, Pennsylvania State University, State College, PA, 16802, USA

    Peng Liu

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  1. Ling-Yun Situ
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Correspondence to Zhi-Qiang Zuo or Lin-Zhang Wang.

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Situ, LY., Zuo, ZQ., Guan, L. et al. Vulnerable Region-Aware Greybox Fuzzing. J. Comput. Sci. Technol. 36, 1212–1228 (2021). https://doi.org/10.1007/s11390-021-1196-0

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