Bugs in operating system kernels can affect billions of devices and users all over the world. As a result, a large body of research has been focused on kernel fuzzing, i.e., automatically generating syscall (system call) sequences to detect potential kernel bugs or vulnerabilities. Syzkaller, one of the most widely studied kernel fuzzers, aims to generate valid syscall sequences based on predefined specifications written in syzlang, a domain-specific language for defining syscalls, their arguments, and the relationships between them. While there has been existing work trying to automate Syzkaller specification generation, this still remains largely manual work and a large number of important syscalls are still uncovered. In this paper, we propose KernelGPT, the first approach to automatically inferring Syzkaller specifications via Large Language Models (LLMs) for enhanced kernel fuzzing. Our basic insight is that LLMs have seen massive kernel code, documentation, and use cases during pre-training, and thus can automatically distill the necessary information for making valid syscalls. More specifically, KernelGPT leverages an iterative approach to automatically infer all the necessary specification components, and further leverages the validation feedback to repair/refine the initial specifications. Our preliminary results demonstrate that KernelGPT can help Syzkaller achieve higher coverage and find multiple previously unknown bugs. Moreover, we also received a request from the Syzkaller team to upstream specifications inferred by KernelGPT.

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