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

Large Language Models for Code Analysis: Do LLMs Really Do Their Job?