Yi Li bio photo

Yi Li

Associate Professor

College of Computing and Data Science (CCDS)
Nanyang Technological University (NTU)

Address: Block N4-02b-63
50 Nanyang Avenue, Singapore 639798
Phone: +65 6790 4287

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We recently have a paper [1] accepted at ICSE’20. This work is trying to expand the capability of fuzzing techniques in the hunt for vulnerabilities with more complex exploits, e.g., use-after-free, which are only triggered by a correct sequence of operations. We have built a fuzzing tool, UAFL, which accepts user-defined vulnerability descriptions represented as a typestate property. Guided by the typestate property, UAFL progressively generates test inputs which may eventually lead to the interested operation sequences.

A brief summary of the paper is given below.

Existing coverage-based fuzzers usually use the individual control-flow graph (CFG) edge coverage to guide the fuzzing process, which has shown great potential in finding vulnerabilities. However, CFG edge coverage is not effective in discovering vulnerabilities such as use-after-free (UaF). This is because, to trigger UaF vulnerabilities, one needs not only to cover individual edges, but also to traverse some long sequence of edges in a particular order, which is challenging for existing fuzzers. To this end, we first propose to model UaF vulnerabilities as typestate properties, then develop a typestate-guided fuzzer, named UAFL, for discovering vulnerabilities violating typestate properties. Given a typestate property, we first perform a static typestate analysis to find operation sequences potentially violating the property. Then, the fuzzing process is guided by the operation sequences in order to progressively generate test cases triggering property violations. In addition, we also adopt the information flow analysis to improve the efficiency of the fuzzing process. We performed a thorough evaluation of UAFL on 14 widely-used real-world programs. The experiment results show that UAFL substantially outperforms the state-of-the-art fuzzers, including AFL, AFLFast, FairFuzz, MOpt, Angora and QSYM, in terms of the time taken to discover vulnerabilities. We discovered10 previously unknown vulnerabilities, and received 5 new CVEs.

Detailed experimental results are available at: https://sites.google.com/view/uafl/.


  1. Wang, H., Xie, X., Li, Y., Wen, C., Li, Y., Liu, Y., Qin, S., Chen, H., & Sui, Y. (2020). Typestate-Guided Fuzzer for Discovering Use-after-Free Vulnerabilities. Proceedings of the 42nd International Conference on Software Engineering (ICSE), 999–1010.