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2018 IEEE Symposium on Security and Privacy (SP) (2018)
San Francisco, CA, US
May 21, 2018 to May 23, 2018
ISSN: 2375-1207
ISBN: 978-1-5386-4353-2
pp: 660-677
Shuitao Gan , State Key Laboratory of Mathematical Engineering and Advanced Computing
Chao Zhang , Tsinghua University
Xiaojun Qin , State Key Laboratory of Mathematical Engineering and Advanced Computing
Xuwen Tu , State Key Laboratory of Mathematical Engineering and Advanced Computing
Kang Li , Cyber Immunity Lab
Zhongyu Pei , Tsinghua University
Zuoning Chen , National Research Center of Parallel Computer Engineering and Technology
ABSTRACT
Coverage-guided fuzzing is a widely used and ef- fective solution to find software vulnerabilities. Tracking code coverage and utilizing it to guide fuzzing are crucial to coverage- guided fuzzers. However, tracking full and accurate path coverage is infeasible in practice due to the high instrumentation overhead. Popular fuzzers (e.g., AFL) often use coarse coverage information, e.g., edge hit counts stored in a compact bitmap, to achieve highly efficient greybox testing. Such inaccuracy and incompleteness in coverage introduce serious limitations to fuzzers. First, it causes path collisions, which prevent fuzzers from discovering potential paths that lead to new crashes. More importantly, it prevents fuzzers from making wise decisions on fuzzing strategies. In this paper, we propose a coverage sensitive fuzzing solution CollAFL. It mitigates path collisions by providing more accurate coverage information, while still preserving low instrumentation overhead. It also utilizes the coverage information to apply three new fuzzing strategies, promoting the speed of discovering new paths and vulnerabilities. We implemented a prototype of CollAFL based on the popular fuzzer AFL and evaluated it on 24 popular applications. The results showed that path collisions are common, i.e., up to 75% of edges could collide with others in some applications, and CollAFL could reduce the edge collision ratio to nearly zero. Moreover, armed with the three fuzzing strategies, CollAFL outperforms AFL in terms of both code coverage and vulnerability discovery. On average, CollAFL covered 20% more program paths, found 320% more unique crashes and 260% more bugs than AFL in 200 hours. In total, CollAFL found 157 new security bugs with 95 new CVEs assigned.
INDEX TERMS
fuzzing, vulnerability-discovery
CITATION

S. Gan et al., "CollAFL: Path Sensitive Fuzzing," 2018 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, US, , pp. 660-677.
doi:10.1109/SP.2018.00040
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