Reverse Engineering and LLMs for Decompilation

[mam009]

This is a fascinating intersection of traditional reverse engineering and modern AI capabilities! Let me break down how LLMs are being used in decompilation and reverse engineering:
Current Applications1. Code Decompilation Enhancement

• LLMs can help make decompiled code more readable by suggesting better variable names, adding comments, and inferring the original structure
  
• They can translate assembly/bytecode into higher-level pseudocode that's easier to understand
  
• Tools like GPT-4 can analyze decompiled output from tools like IDA Pro, Ghidra, or Binary Ninja and provide insights
  

2. Pattern Recognition

• LLMs excel at identifying common code patterns, algorithms, and library functions in decompiled code
  
• They can recognize obfuscation techniques and suggest de-obfuscation strategies
  
• Helpful for identifying crypto algorithms, compression routines, or standard library calls
  

3. Vulnerability Analysis

• LLMs can scan decompiled code for potential security vulnerabilities
  
• They can suggest what certain suspicious code blocks might be doing
  
• Useful for malware analysis and security research
  

Limitations to Be Aware Of

• Hallucinations: LLMs may confidently provide incorrect interpretations of assembly or decompiled code
  
• Context windows: Large binaries may exceed token limits
  
• Accuracy: Critical reverse engineering work still requires human verification
  
• No execution context: LLMs can't debug or trace actual program execution
  

Practical WorkflowA typical LLM-assisted reverse engineering workflow might look like:

1. Use traditional decompilers (Ghidra, IDA, etc.) to get initial output
   
2. Feed specific functions or code blocks to an LLM for analysis
   
3. Ask for clarification on unclear sections
   
4. Request variable renaming suggestions
   
5. Verify LLM suggestions against actual binary behavior