A multi-agent LLM system cuts false positives in static application security testing by 88.6% on the OWASP Benchmark while dropping recall by only 3.1%.
Evaluation of ChatGPT model for vulnerability detection
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ML4AVD research remains locked into binary function-level classification of C/C++ vulnerabilities because twelve pain points in the pipeline reinforce each other through feedback loops.
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QASecClaw: A Multi-Agent LLM Approach for False Positive Reduction in Static Application Security Testing
A multi-agent LLM system cuts false positives in static application security testing by 88.6% on the OWASP Benchmark while dropping recall by only 3.1%.
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Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points
ML4AVD research remains locked into binary function-level classification of C/C++ vulnerabilities because twelve pain points in the pipeline reinforce each other through feedback loops.