arXiv Artificial Intelligence

Vulnerability Localization Benchmark: Measuring Agentic Security Analysis at Repository Scale

1 hour ago 15.09.2026 07:00 👁 2 views
Vulnerability Localization Benchmark: Measuring Agentic Security Analysis at Repository Scale

Quick summary

arXiv:2609.15939v1 Announce Type: cross Abstract: Language-model agents increasingly operate over complete software repositories, yet cybersecurity evaluations primarily measure whether they can detect, reproduce, or repair vulnerabilities rather than whether they can locate the relevant code. We study vulnerability localization: given a weakness class and an unfamiliar repository, identify the implementation files associated with that weakness. We introduce the Vulnerability Localization Benchmark (VLoc Bench), comprising 500 real world vulnerabilities from 290 repositories across six package

Key takeaways

  • arXiv:2609.15939v1 Announce Type: cross Abstract: Language-model agents increasingly operate over complete software repositories, yet cybersecurity evaluations primarily measure whether they can detect, reproduce, or repair vulnerabilities rather than whether they can locate the relevant code.
  • We study vulnerability localization: given a weakness class and an unfamiliar repository, identify the implementation files associated with that weakness.
  • We introduce the Vulnerability Localization Benchmark (VLoc Bench), comprising 500 real world vulnerabilities from 290 repositories across six package

Why it matters

“Vulnerability Localization Benchmark: Measuring Agentic Security Analysis at Repository Scale” shows why AI risk cannot be reduced to answer accuracy. Access controls, logging, human approval and incident response need to be designed into the workflow from the start.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗