S <scp>nowplow</scp> : Effective Kernel Fuzzing with a Learned White-box Test Mutator
Kernel fuzzers rely heavily on program mutation to automatically generate new test programs based on existing ones. In particular, program mutation can alter the test's control and data flow inside the kernel by inserting new system calls, changing the values of call arguments, or performing other program mutations. However, due to the complexity of the kernel code and its user-space interface, finding the effective mutation that can lead to the desired outcome such as increasing the coverage and reaching a target code location is extremely difficult, even with the widespread use of manually-crafted heuristics.
Authors
- Sishuai Gong (ORCID: https://orcid.org/0000-0002-2784-2617)
- Pedro Fonseca (ORCID: https://orcid.org/0000-0003-2480-4487)
- Deniz Altınbüken (ORCID: https://orcid.org/0000-0002-4558-2847)
- Petros Maniatis (ORCID: https://orcid.org/0000-0003-3777-5291)
- Wang Rui
Institutions
- Google (United States) (US)
- Purdue University West Lafayette (US)
Publication Details
- Published
- 2025-03-27
- DOI
- https://doi.org/10.1145/3676641.3716019
- Citations
- 2
- Primary Topic
- Software Testing and Debugging Techniques
- Type
- article
- Field-Weighted Citation Impact
- 4.62