  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 code analysis. 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.
