CovRL: Fuzzing JavaScript Engines with Coverage-Guided Reinforcement Learning for LLM-based Mutation

arXiv:2402.12222v1 Announce Type: cross
Abstract: Fuzzing is an effective bug-finding technique but it struggles with complex systems like JavaScript engines that demand precise grammatical input. Recently, researchers have adopted language models for context-aware mutation in fuzzing to address this problem. However, existing techniques are limited in utilizing coverage guidance for fuzzing, which is rather performed in a black-box manner. This paper presents a novel technique called CovRL (Coverage-guided Reinforcement Learning) that combines Large Language Models (LLMs) with reinforcement learning from …

abstract arxiv complex systems context coverage cs.cl cs.cr cs.lg cs.se demand fuzzing guidance javascript language language models llm mutation reinforcement reinforcement learning researchers systems type