VulWeaver combines repaired static-analysis graphs, holistic code context, and structured LLM reasoning; it reports F1 0.75 on a new Java benchmark and 0.78 on the C/C++ PrimeVul test set.
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Fine-tuned decoder-only LLMs fall into a Semantic Trap on vulnerability detection, achieving high scores on unpaired normal code but failing on paired vulnerable-patched code, semantic perturbations, and gap analysis, while reasoning supervision reduces symptoms at the cost of recall.
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VulWeaver: Weaving Broken Semantics for Grounded Vulnerability Detection
VulWeaver combines repaired static-analysis graphs, holistic code context, and structured LLM reasoning; it reports F1 0.75 on a new Java benchmark and 0.78 on the C/C++ PrimeVul test set.
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Do Fine-Tuned LLMs Understand Vulnerabilities? An Investigation into the Semantic Trap
Fine-tuned decoder-only LLMs fall into a Semantic Trap on vulnerability detection, achieving high scores on unpaired normal code but failing on paired vulnerable-patched code, semantic perturbations, and gap analysis, while reasoning supervision reduces symptoms at the cost of recall.