HuntFUZZ: Enhancing Error Handling Testing through Clustering Based Fuzzing
<|reference_start|>HuntFUZZ: Enhancing Error Handling Testing through Clustering Based Fuzzing: Testing a program's capability to effectively handling errors is a significant challenge, given that program errors are relatively uncommon. To solve this, Software Fault Injection (SFI)-based fuzzing integrates SFI and traditional fuzzing, injecting and triggering errors for testing (error handling) code. However, we observe that current SFI-based fuzzing approaches have overlooked the correlation between paths housing error points. In fact, the execution paths of error points often share common paths. Nonetheless, Fuzzers usually generate test cases repeatedly to test error points on commonly traversed paths. This practice can compromise the efficiency of the fuzzer(s). Thus, this paper introduces HuntFUZZ, a novel SFI-based fuzzing framework that addresses the issue of redundant testing of error points with correlated paths. Specifically, HuntFUZZ clusters these correlated error points and utilizes concolic execution to compute constraints only for common paths within each cluster. By doing so, we provide the fuzzer with efficient test cases to explore related error points with minimal redundancy. We evaluate HuntFUZZ on a diverse set of 42 applications, and HuntFUZZ successfully reveals 162 known bugs, with 62 of them being related to error handling. Additionally, due to its efficient error point detection method, HuntFUZZ discovers 7 unique zero-day bugs, which are all missed by existing fuzzers. Furthermore, we compare HuntFUZZ with 4 existing fuzzing approaches, including AFL, AFL++, AFLGo, and EH-FUZZ. Our evaluation confirms that HuntFUZZ can cover a broader range of error points, and it exhibits better performance in terms of bug finding speed.<|reference_end|>
@article{wei2024huntfuzz:,
    title={HuntFUZZ: Enhancing Error Handling Testing through Clustering Based
  Fuzzing},
    author={Jin Wei, Ping Chen, Jun Dai, Xiaoyan Sun, Zhihao Zhang, Chang Xu, Yi
  Wanga},
    journal={arXiv preprint arXiv:2407.04297},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04297},
    primaryClass={cs.CR}
}