Exploring the Efficacy of Large Language Models (GPT-4) in Binary Reverse Engineering
This addresses the problem of automating reverse engineering tasks for security researchers, but it is incremental as it builds on existing LLM applications.
This study investigated the capabilities of GPT-4 in binary reverse engineering, finding that it is proficient in general code understanding but has varying effectiveness in detailed technical and security analyses.
This study investigates the capabilities of Large Language Models (LLMs), specifically GPT-4, in the context of Binary Reverse Engineering (RE). Employing a structured experimental approach, we analyzed the LLM's performance in interpreting and explaining human-written and decompiled codes. The research encompassed two phases: the first on basic code interpretation and the second on more complex malware analysis. Key findings indicate LLMs' proficiency in general code understanding, with varying effectiveness in detailed technical and security analyses. The study underscores the potential and current limitations of LLMs in reverse engineering, revealing crucial insights for future applications and improvements. Also, we examined our experimental methodologies, such as methods of evaluation and data constraints, which provided us with a technical vision for any future research activity in this field.