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.
Vuleval: Towards repository-level evaluation of software vulnerability detec- tion
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ReasonVul deploys three LLM agents with independent analysis and structured debate to achieve 40% PairAcc and 72.52% F1 on PrimeVul, outperforming baselines by 81% in PairAcc.
Reproducibility study of Vul-RAG confirms original findings in a fully local open-weights setting but identifies a persistent performance plateau at approximately 0.30 pairwise accuracy across diverse recent open-weight LLMs.
citing papers explorer
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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.
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Three Heads Are Better Than One: A Multi-perspective Reasoning Framework for Enhanced Vulnerability Detection
ReasonVul deploys three LLM agents with independent analysis and structured debate to achieve 40% PairAcc and 72.52% F1 on PrimeVul, outperforming baselines by 81% in PairAcc.
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Revisiting Vul-RAG: Reproducibility and Replicability of RAG-based Vulnerability Detection with Open-Weight Models
Reproducibility study of Vul-RAG confirms original findings in a fully local open-weights setting but identifies a persistent performance plateau at approximately 0.30 pairwise accuracy across diverse recent open-weight LLMs.