Toward Scalable Automated Repository-Level Datasets for Software Vulnerability Detection
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918395946991616 |
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| author | Lbath, Amine |
| author_facet | Lbath, Amine |
| contents | Software vulnerabilities continue to grow in volume and remain difficult to detect in practice. Although learning-based vulnerability detection has progressed, existing benchmarks are largely function-centric and fail to capture realistic, executable, interprocedural settings. Recent repo-level security benchmarks demonstrate the importance of realistic environments, but their manual curation limits scale. This doctoral research proposes an automated benchmark generator that injects realistic vulnerabilities into real-world repositories and synthesizes reproducible proof-of-vulnerability (PoV) exploits, enabling precisely labeled datasets for training and evaluating repo-level vulnerability detection agents. We further investigate an adversarial co-evolution loop between injection and detection agents to improve robustness under realistic constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_17974 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Toward Scalable Automated Repository-Level Datasets for Software Vulnerability Detection Lbath, Amine Software Engineering Artificial Intelligence Software vulnerabilities continue to grow in volume and remain difficult to detect in practice. Although learning-based vulnerability detection has progressed, existing benchmarks are largely function-centric and fail to capture realistic, executable, interprocedural settings. Recent repo-level security benchmarks demonstrate the importance of realistic environments, but their manual curation limits scale. This doctoral research proposes an automated benchmark generator that injects realistic vulnerabilities into real-world repositories and synthesizes reproducible proof-of-vulnerability (PoV) exploits, enabling precisely labeled datasets for training and evaluating repo-level vulnerability detection agents. We further investigate an adversarial co-evolution loop between injection and detection agents to improve robustness under realistic constraints. |
| title | Toward Scalable Automated Repository-Level Datasets for Software Vulnerability Detection |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2603.17974 |