Performance challenges
Binary files larger than 100MB may cause response delays or memory overflow:
protective measure
- Segmented loading strategy: Use of limited-scope directives such as "analyze .text segments only".
- Resource Monitoring: Configure the max_memory=4G parameter to limit the memory usage for a single analysis.
- asynchronous processing: Add the -background parameter to make the analysis task run in the background.
Optimization steps
- Execute "Estimate Function Complexity" to exclude template code before analysis
- Enable chunking with analysis_chunk_size parameter
- Prioritize high-value targets such as exported functions.
Emergency program
Terminate the Python process by taskkill in case of lag and check ghidra_analysis.log to locate the bottleneck.
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 avoid performance issues with GhidraMCP for large binary analysis?
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