Toward Scalable Automated Repository-Level Datasets for Software Vulnerability Detection

• URL: http://arxiv.org/abs/2603.17974v1
• Date: Wed, 18 Mar 2026 17:38:35 GMT
• Title: Toward Scalable Automated Repository-Level Datasets for Software Vulnerability Detection
• Abstract summary: This research proposes an automated benchmark generator that injects realistic vulnerabilities into real-world repositories.<n>It synthesizes reproducible proof-of-vulnerability exploits, enabling precisely labeled datasets for training and evaluating repo-level vulnerability detection agents.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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