Abstract
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.
Abstract (translated)
模糊测试（Fuzzing）是一种广泛使用的代码审计技术，它通过大型语言模型（LLMs）取得了进展。尽管LLMs具有巨大的潜力，但它们在模糊测试方面面临一些特定的挑战。在本文中，我们确定了LLM辅助模糊测试的五个主要挑战。为了支持我们的发现，我们回顾了顶级会议中最新的论文，证实了这些挑战是普遍存在的。为了改善在模糊测试中应用LLM，我们提出了一些可行的建议，并对DBMS模糊测试进行了初步评估。结果显示，我们的建议有效地解决了识别出的挑战。
URL
https://arxiv.org/abs/2404.16297
