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.

翻译：本研究探究了大语言模型（LLMs），特别是GPT-4，在二进制逆向工程（RE）中的能力。通过采用结构化的实验方法，我们分析了该大语言模型在解释和理解人工编写代码与反编译代码方面的表现。研究分为两个阶段：第一阶段聚焦于基础代码解释，第二阶段则涉及更复杂的恶意软件分析。关键发现表明，大语言模型在通用代码理解方面具有较强能力，但在详细的技术分析与安全分析中效果参差不齐。本研究揭示了逆向工程中大语言模型的潜力与当前局限性，为未来应用与改进提供了重要启示。此外，我们还考察了实验方法（如评估方法与数据约束），这为未来在该领域的任何研究活动提供了技术视角。