Li, XieYuan, ZhaoyueZhang, ZhenduoSun, YouchengZhang, Lijun2025-05-292025-05-29202501/05/2025X. Li, Z. Yuan, Z. Zhang, Y. Sun, and L. Zhang, “Towards Large Language Model Guided Kernel Direct Fuzzing,” Lecture Notes in Computer Science, vol. 15693, pp. 33–42, Jan. 2025, doi: 10.1007/978-3-031-90900-9_2.978-303190899-60302-974310.1007/978-3-031-90900-9_2https://hdl.handle.net/20.500.14634/913Direct 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 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. © The Author(s) 2025.EnglishLarge Language Models28th International Conference on Fundamental Approaches to Software Engineering, FASE 2025, which was held as part of the International Joint Conferences on Theory and Practice of Software, ETAPS 20253342Conference15693 LNCSConference proceedingLecture Notes in Computer Science