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.
Empirical evaluation shows that code generated by all seven tested LLMs contains vulnerabilities, the majority of critical or high severity.
citing papers explorer
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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.
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Security of LLM-generated Code: A Comparative Analysis
Empirical evaluation shows that code generated by all seven tested LLMs contains vulnerabilities, the majority of critical or high severity.