How to optimize the accuracy of GhidraMCP for malware analysis?

2025-08-27

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Background

AI-assisted analysis may produce false positives due to model limitations. Judgment accuracy can be significantly improved by the following methods:

Multi-model cross validation: Alternate Claude and 5ire clients to compare the output of different AIs
contextual enhancement: Enter commands such as "Load Windows API Knowledge Base" to add background information before analyzing.
Analyzing Focus Constraints: Narrow the scope of scanning with specific commands such as "check only network communication related functions".

Prioritize Import Table Analysis to Locate Key DLL Calls
Behavioral chain analysis of suspicious functions using the "trace back to higher level caller" directive
Save analysis history for subsequent model training improvement

Professional users can add custom heuristic rules by modifying bridge_mcp_ghidra.py