Fuzzing, a widely-used technique for bug detection, has seen advancements through Large Language Models (LLMs). Despite their potential, LLMs face specific challenges in fuzzing. In this paper, we identified five major challenges of LLM-assisted fuzzing. To support our findings, we revisited the most recent papers from top-tier conferences, confirming that these challenges are widespread. As a remedy, we propose some actionable recommendations to help improve applying LLM in Fuzzing and conduct preliminary evaluations on DBMS fuzzing. The results demonstrate that our recommendations effectively address the identified challenges.

翻译：模糊测试作为一种广泛使用的漏洞检测技术，近年来借助大语言模型取得了显著进展。尽管大语言模型潜力巨大，但在模糊测试领域仍面临特定挑战。本文识别出大语言模型辅助模糊测试的五大主要挑战。为支撑研究结论，我们重新审视了顶级会议的最新论文，证实这些挑战具有普遍性。作为应对方案，我们提出了一系列可行建议以改进大语言模型在模糊测试中的应用，并在数据库管理系统模糊测试中开展了初步评估。结果表明，我们的建议能够有效应对所识别的挑战。