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BEACON: Directed Grey-Box Fuzzing with Provable Path Pruning

  • Heqing Huang
  • , Yiyuan Guo
  • , Qingkai Shi*
  • , Peisen Yao
  • , Rongxin Wu
  • , Charles Zhang
  • *Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Unlike coverage-based fuzzing that gives equal attention to every part of a code, directed fuzzing aims to direct a fuzzer to a specific target in the code, e.g., the code with potential vulnerabilities. Despite much progress, we observe that existing directed fuzzers are still not efficient as they often symbolically or concretely execute a lot of program paths that cannot reach the target code. They thus waste a lot of computational resources. This paper presents BEACON, which can effectively direct a grey-box fuzzer in the sea of paths in a provable manner. That is, assisted by a lightweight static analysis that computes abstracted preconditions for reaching the target, we can prune 82.94% of the executing paths at runtime with negligible analysis overhead (<5h) but with the guarantee that the pruned paths must be spurious with respect to the target. We have implemented our approach, BEACON, and compared it to five state-of-the-art (directed) fuzzers in the application scenario of vulnerability reproduction. The evaluation results demonstrate that BEACON is 11.50x faster on average than existing directed grey-box fuzzers and it can also improve the speed of the conventional coverage-guided fuzzers, AFL, AFL++, and Mopt, to reproduce specific bugs with 6.31x, 11.86x, and 10.92x speedup, respectively. More interestingly, when used to test the vulnerability patches, BEACON found 14 incomplete fixes of existing CVE-identified vulnerabilities and 8 new bugs while 10 of them are exploitable with new CVE ids assigned. © 2022, Heqing Huang. Under license to IEEE.
Original languageEnglish
Title of host publicationProceedings - 43rd IEEE Symposium on Security and Privacy, SP 2022
PublisherIEEE
Pages36-50
ISBN (Electronic)978-1-6654-1316-9
ISBN (Print)978-1-6654-1317-6
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event43rd IEEE Symposium on Security and Privacy (SP 2022) - San Francisco, United States
Duration: 23 May 2022 → 26 May 2022

Publication series

NameProceedings - IEEE Symposium on Security and Privacy
ISSN (Print)1081-6011
ISSN (Electronic)2375-1207

Conference

Conference43rd IEEE Symposium on Security and Privacy (SP 2022)
PlaceUnited States
CitySan Francisco
Period23/05/22 → 26/05/22

Funding

We thank the anonymous reviewers for their insightful comments and opinions for improving this work. Rongxin Wu is supported by the Leading-edge Technology Program of Jiangsu Natural Science Foundation (BK20202001) and NSFC61902329. Other authors are supported by the RGC16206517 and ITS/440/18FP grants from the Hong Kong Research Grant Council, and the donations from Microsoft Donation and Huawei. Qingkai Shi is the corresponding author.

Research Keywords

  • Directed fuzzing
  • precondition inference
  • program transformation

RGC Funding Information

  • RGC-funded

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