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This paper introduces <jats:sc>SyzAgent<\/jats:sc>, a framework integrating 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.\n<\/jats:p>","DOI":"10.1007\/978-3-031-90900-9_2","type":"book-chapter","created":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T04:44:23Z","timestamp":1745988263000},"page":"33-42","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Towards Large Language Model Guided Kernel Direct Fuzzing"],"prefix":"10.1007","author":[{"given":"Xie","family":"Li","sequence":"first","affiliation":[]},{"given":"Zhaoyue","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Zhenduo","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Youcheng","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Lijun","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,1]]},"reference":[{"key":"2_CR1","unstructured":"Chatgpt (gpt-4o), https:\/\/www.openai.com\/chatgpt"},{"key":"2_CR2","unstructured":"Kcov, https:\/\/github.com\/SimonKagstrom\/kcov"},{"key":"2_CR3","unstructured":"Syzkaller. https:\/\/github.com\/google\/syzkaller\/"},{"key":"2_CR4","unstructured":"Syzlang. https:\/\/github.com\/google\/syzkaller\/blob\/master\/docs\/syscall_descriptions_syntax.md"},{"key":"2_CR5","unstructured":"Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et\u00a0al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"2_CR6","doi-asserted-by":"publisher","unstructured":"Corina, J., Machiry, A., Salls, C., Shoshitaishvili, Y., Hao, S., Kruegel, C., Vigna, G.: DIFUZE: interface aware fuzzing for kernel drivers. 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In: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, CCS 2023, Copenhagen, Denmark, November 26-30, 2023. pp. 1630\u20131644 (2023). https:\/\/doi.org\/10.1145\/3576915.3623146","DOI":"10.1145\/3576915.3623146"},{"key":"2_CR11","unstructured":"Wang, D., Zhang, Z., Zhang, H., Qian, Z., Krishnamurthy, S.V., Abu-Ghazaleh, N.B.: Syzvegas: Beating kernel fuzzing odds with reinforcement learning. In: 30th USENIX Security Symposium, USENIX Security 2021, August 11-13, 2021. pp. 2741\u20132758 (2021)"},{"key":"2_CR12","doi-asserted-by":"publisher","unstructured":"Xia, C.S., Paltenghi, M., Tian, J.L., Pradel, M., Zhang, L.: Fuzz4all: Universal fuzzing with large language models. In: Proceedings of the 46th IEEE\/ACM International Conference on Software Engineering, ICSE 2024, Lisbon, Portugal, April 14-20, 2024. pp. 126:1\u2013126:13 (2024). https:\/\/doi.org\/10.1145\/3597503.3639121","DOI":"10.1145\/3597503.3639121"},{"key":"2_CR13","doi-asserted-by":"publisher","unstructured":"Yang, C., Zhao, Z., Zhang, L.: Kernelgpt: Enhanced kernel fuzzing via large language models. CoRR abs\/2401.00563 (2024). https:\/\/doi.org\/10.48550\/ARXIV.2401.00563","DOI":"10.48550\/ARXIV.2401.00563"},{"key":"2_CR14","unstructured":"Zhao, B., Li, Z., Qin, S., Ma, Z., Yuan, M., Zhu, W., Tian, Z., Zhang, C.: Statefuzz: System call-based state-aware linux driver fuzzing. 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