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Auteurs principaux: Li, Xie, Yuan, Zhaoyue, Zhang, Zhenduo, Sun, Youcheng, Zhang, Lijun
Format: Preprint
Publié: 2025
Sujets:
Accès en ligne:https://arxiv.org/abs/2503.02301
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author Li, Xie
Yuan, Zhaoyue
Zhang, Zhenduo
Sun, Youcheng
Zhang, Lijun
author_facet Li, Xie
Yuan, Zhaoyue
Zhang, Zhenduo
Sun, Youcheng
Zhang, Lijun
contents Direct kernel fuzzing is a targeted approach that focuses on specific areas of the kernel, effectively addressing the challenges of frequent updates and the inherent complexity of operating systems, which are critical infrastructure. This paper introduces SyzAgent, a framework that integrates LLMs with the state-of-the-art kernel fuzzer Syzkaller, where the LLMs are used to guide the mutation and generation of test cases in real-time. We present preliminary results demonstrating that this method is effective on around 67\% cases in our benchmark during the experiment.
format Preprint
id arxiv_https___arxiv_org_abs_2503_02301
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Towards Large Language Model Guided Kernel Direct Fuzzing
Li, Xie
Yuan, Zhaoyue
Zhang, Zhenduo
Sun, Youcheng
Zhang, Lijun
Software Engineering
Direct kernel fuzzing is a targeted approach that focuses on specific areas of the kernel, effectively addressing the challenges of frequent updates and the inherent complexity of operating systems, which are critical infrastructure. This paper introduces SyzAgent, a framework that integrates LLMs with the state-of-the-art kernel fuzzer Syzkaller, where the LLMs are used to guide the mutation and generation of test cases in real-time. We present preliminary results demonstrating that this method is effective on around 67\% cases in our benchmark during the experiment.
title Towards Large Language Model Guided Kernel Direct Fuzzing
topic Software Engineering
url https://arxiv.org/abs/2503.02301