LLM-Guided Semantic Mutation for Lua Interpreter Fuzzing: A Coverage-Driven Approach

Abstract

Here we have the software artifacts for the paper 'LLM-Guided Semantic Mutation for Lua Interpreter Fuzzing: A Coverage-Driven Approach'. The work proposes a methodology that uses Large Language Models (LLMs) to generate semantically rich mutations for fuzzing Lua scripts. Our approach involves developing a fuzzer prototype that leverages an LLM's in-context learning capabilities to create syntactically and semantically plausible code variations.