Kgent: Kernel Extensions Large Language Model Agent
Yusheng Zheng, Yiwei Yang, Maolin Chen, Andrew Quinn · 2024
The extended Berkeley Packet Filters (eBPF) ecosystem allows for the extension of Linux and Windows kernels, but writing eBPF programs is challenging due to the required knowledge of OS internals and programming limitations enforced by the eBPF verifier. These limitations ensure that only expert kernel developers can extend their kernels, making it difficult for junior sys admins, patch makers, and DevOps personnel to maintain extensions. This paper presents Kgent, an alternative framework that alleviates the difficulty of writing an eBPF program by allowing Kernel Extensions to be written in Natural language. Kgent uses recent advances in large language models (LLMs) to synthesize an eBPF program given a user's English language prompt. To ensure that LLM's output is semantically equivalent to the user's prompt, Kgent employs a combination of LLM-empowered program comprehension, symbolic execution, and a series of feedback loops. Kgent's key novelty is the combination of these techniques. In particular, the system uses symbolic execution in a novel structure that allows it to combine the results of program synthesis and program comprehension and build on the recent success that LLMs have shown for each of these tasks individually.