VulnAgent-X: A Layered Agentic Framework for Repository-Level Vulnerability Detection
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910052573511680 |
|---|---|
| author | Meng, Renwei Wu, Haoyi Wang, Jingming Bai, Haoyan |
| author_facet | Meng, Renwei Wu, Haoyi Wang, Jingming Bai, Haoyan |
| contents | Software vulnerability detection is critical in software en- gineering as security flaws arise from complex interactions across code structure, repository context, and runtime conditions. Existing meth- ods are limited by local code views, one-shot prediction, and insuffi- cient validation, reducing reliability in realistic repository-level settings. This study proposes VulnAgentX, a layered agentic framework integrat- ing lightweight risk screening, bounded context expansion, specialised analysis agents, selective dynamic verification, and evidence fusion into a unified pipeline. Experiments on function-level and just-in-time vul- nerability benchmarks show VulnAgent-X outperforms static baselines, encoder-based models, and simpler agentic variants, with better local- isation and balanced performance-cost trade-offs. Treating vulnerabil- ity detection as a staged, evidence-driven auditing process improves de- tection quality, reduces false positives, and produces interpretable re- sults for repository-level software security analysis. Code is available at https://github.com/xiaolu-666113/Vlun-Agent-X. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13384 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VulnAgent-X: A Layered Agentic Framework for Repository-Level Vulnerability Detection Meng, Renwei Wu, Haoyi Wang, Jingming Bai, Haoyan Software Engineering Artificial Intelligence Software vulnerability detection is critical in software en- gineering as security flaws arise from complex interactions across code structure, repository context, and runtime conditions. Existing meth- ods are limited by local code views, one-shot prediction, and insuffi- cient validation, reducing reliability in realistic repository-level settings. This study proposes VulnAgentX, a layered agentic framework integrat- ing lightweight risk screening, bounded context expansion, specialised analysis agents, selective dynamic verification, and evidence fusion into a unified pipeline. Experiments on function-level and just-in-time vul- nerability benchmarks show VulnAgent-X outperforms static baselines, encoder-based models, and simpler agentic variants, with better local- isation and balanced performance-cost trade-offs. Treating vulnerabil- ity detection as a staged, evidence-driven auditing process improves de- tection quality, reduces false positives, and produces interpretable re- sults for repository-level software security analysis. Code is available at https://github.com/xiaolu-666113/Vlun-Agent-X. |
| title | VulnAgent-X: A Layered Agentic Framework for Repository-Level Vulnerability Detection |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2603.13384 |