application scenario
Security research requires systematic detection of potential vulnerabilities in binaries:
Efficient Programs
- Patterned Scanning: Enter standard exploit mode commands such as "check for CWE-121 stack overflow".
- Calling Diagram Analysis:: Visualization of security boundary breach points using "hazard function propagation mapping".
- patch comparison: Locate fixed vulnerabilities by "comparing 1.0/2.0 function differences".
Workflow optimization
- Run the basic "Flag all memory operations" scan first.
- Use "deep audit malloc call chain" for high-risk modules.
- Include AI confidence scores when exporting reports
advanced skill
Modify vulnerability_patterns.json to add custom vulnerability features to support industry-specific standards
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 maximize the use of GhidraMCP in vulnerability mining scenarios?
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