VulValidate: Auditing Function-Level Vulnerability Labels with Executable Evidence
Reliable learning-based vulnerability detection requires high-quality labels, yet datasets built from vulnerability-fixing commits may label functions as vulnerable simply because they were changed by a security patch. We present VulValidate, a framework that uses LLM agents to coordinate dynamic analysis tools and construct vulnerability-triggering experiments from runtime feedback. Given a labeled function and its fixing patch, VulValidate reconstructs vulnerable and fixed revisions, selects suitable tools and execution paths, refines triggering inputs, and compares runtime behavior to assess function-level attribution. We audit all 35,849 instances originally labeled vulnerable in BigV...
| Authors | Leizhen Zhang, Sheng Chen |
|---|---|
| Primary category | cs.CR |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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