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LLM-powered binary analysis for detecting memory corruption vulnerabilities in stripped code

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Academic research framework from Columbia University and King's College London that detects memory corruption vulnerabilities in stripped binaries. Combines static analysis with LLM-based semantic understanding to identify security issues through runtime feasibility analysis and interprocedural data flow propagation. Targets security researchers and reverse engineers working with compiled code where source is unavailable.

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Integrations
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open-sourceautonomouscoding-agentevaluationpythontool-use