From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection
Practical evaluation of deep-learning and LLM-based vulnerability detectors reveals stark performance drops from benchmarks to real-world code — a caution for teams integrating AI into SAST pipelines.
Summary written by editorial AI · Source link below
arXiv:2512.10485v4 Announce Type: replace Abstract: Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiveness remains underexplored. Recent work suggests that graph neural network-based and transformer-based models, including large language models (LLMs), yield promising results when evaluated on curated benchmark datasets. These datasets are typically characterized by similar data distributions and may co
Editorial Analysis
Enterprises embedding AI-powered vulnerability scanners into CI/CD should not rely on benchmark claims; real-world detection rates are substantially lower.
Validate AI-based SAST tool effectiveness against your own codebase before relying on it as a release gate.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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