Requirements background
Classified or special network environments require completely offline reverse analysis capabilities, which can be realized through local deployment:
Implementation steps
- Local Model Deployment: Replacement of local big models such as LLaMA in the MCP client configuration
- Knowledge base preloading: Download the Common Vulnerabilities Feature Library and API documentation to . /ghidra_scripts directory.
- Caching mechanism enabled: Modify config.json to set analysis_cache_enabled=true
Key Configurations
- Copy the GhidraMCP/data/patterns directory to local storage
- Disable automatic updates in update_checker.py
- Starting bridge_mcp_ghidra.py with the -offline parameter
caveat
Offline mode requires at least 8GB of video memory, and the "Preload All Symbols" command is executed before analyzing to improve response speed.
This answer comes from the articleGhidraMCP: A Reverse Engineering Tool to Connect AI with GhidraThe
May not be reproduced without permission:AI productivity tools " How to achieve full functionality of GhidraMCP in offline environment?
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