Large language models (LLMs) have demonstrated significant potential in the realm of natural language understanding and programming code processing tasks. Their capacity to comprehend and generate human-like code has spurred research into harnessing LLMs for code analysis purposes. However, the existing body of literature falls short in delivering a systematic evaluation and assessment of LLMs' effectiveness in code analysis, particularly in the context of obfuscated code. This paper seeks to bridge this gap by offering a comprehensive evaluation of LLMs' capabilities in performing code analysis tasks. Additionally, it presents real-world case studies that employ LLMs for the analysis of malicious code. Our findings indicate that LLMs can indeed serve as valuable tools for automating code analysis, albeit with certain limitations. Through meticulous exploration, this research contributes to a deeper understanding of the potential and constraints associated with utilizing LLMs in code analysis, paving the way for enhanced applications in this critical domain.

翻译：大语言模型（LLM）在自然语言理解与编程代码处理任务中展现出显著潜力。其理解并生成类人代码的能力激发了利用LLM进行代码分析的研究热潮。然而，现有文献尚未系统性评估LLM在代码分析任务中的有效性，特别是在混淆代码场景下的表现。本文通过全面评估LLM执行代码分析任务的能力填补了这一空白，并展示了利用LLM分析恶意代码的真实案例研究。研究表明，尽管存在一定局限性，LLM确实可作为自动化代码分析的有效工具。通过细致探索，本研究深化了对LLM在代码分析中应用潜力与约束的理解，为该关键领域的应用优化奠定了基础。