  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.
