Retrieve, Reproduce, Reveal: Dissecting Retrieval-Augmented Software Vulnerability Detection
Retrieval-Augmented Generation (RAG) is increasingly used to enhance Large Language Model (LLM)-based software vulnerability detection by grounding predictions in retrieved vulnerability knowledge, such as vulnerability reports. However, existing RAG-based software vulnerability detection (RAG4SVD) systems are often evaluated using proprietary models, which challenges open science and reproducibility. Further, studies use different datasets, custom knowledge bases, different backbone models, and diverse metrics, which hinders meaningful cross-system comparison. In this work, we study six open-source RAG4SVD systems and address these reproducibility and comparability challenges through (i)...
| Authors | Sabrina Kaniewski, Tim Krämer, Julius Bächle et al. |
|---|---|
| Primary category | cs.SE |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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