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ISSTA2026顶会

SyzDiversity: Diversity-Guided Linux Kernel Fuzzing

Kun Hu, Jiaji Qin, Chaofeng Sha, Bihuan Chen, Shuoran Bai, Qicai Chen, Chenglin Wang, Xin Peng, Wenyun Zhao

2026年份

摘要

While coverage-guided kernel fuzzers have been proposed to uncover Linux kernel vulnerabilities, their code coverage and bug-finding capability are limited due to the lack of seed diversity, which is caused by the compounding effect of initial seed generation, seed scheduling, and seed mutation. To address this limitation, we propose a diversity-guided kernel fuzzer SyzDiversity. Specifically, to mitigate overvaluation of early seeds, it leverages proof-of-concept (PoC) seeds derived from real-world vulnerabilities as initial seeds, and further partitions these seeds into multiple communities. To improve diversity guidance in seed scheduling, it leverages a novel metric, community popularity rate (CPR), to model community diversity, and introduces a CPR-aware hierarchical Multi-Armed Bandit (MAB) algorithm that integrates CPR and code coverage as reward signals to prioritize the scheduling of diverse seed communities and seeds. Further, to efficiently populate sparse communities or break through community boundaries, it adopts a CPR-guided seed mutation strategy that adaptively allocates higher mutation frequencies to communities that are more conducive to the diversity evolution of the seeds. Our extensive experiments on Linux kernel versions v5.15 and v6.14 have demonstrated that SyzDiversity improves code coverage and bug-finding capability by 17.2% and 6.4×, respectively, compared to the state-of-the-art kernel fuzzers. It has discovered 32 unique new vulnerabilities, with 12 of them confirmed.

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