Contrastive Learning | Benjamin M. Ampel

Automated Cross-Repository Vulnerability Variant Retrieval Using Patch-Weighted Contrastive Learning

Mon, 26 Oct 2026 00:00:00 +0000

In Plain Terms

When a security flaw is found in one open-source project, copies and near-copies of that same flawed code often live on in other projects. This paper presents AVA, a system that learns from known vulnerabilities and their fixes to automatically hunt down those unfixed look-alikes across many repositories at once. It shows that real-world patches can teach a model to tell vulnerable code from fixed code, extending remediation beyond the project where a flaw was first reported.

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Citation

Joseph Chen, Benjamin M. Ampel, Steven Ullman, Raul Y. Reyes, & Hsinchun Chen (2026). Automated Cross-Repository Vulnerability Variant Retrieval Using Patch-Weighted Contrastive Learning. In *Proceedings of the 2026 IEEE Cyber Awareness and Research Symposium (CARS)*

Vendor-Conditioned Contrastive Learning for Predicting Organizational Cyber Threat Targets

Mon, 26 Oct 2026 00:00:00 +0000

In Plain Terms

This paper presents TRACE, a model that reads exploit posts from hacker forums and exploit databases to predict which kind of organization an attack targets. By tailoring its learning to the software vendor involved and testing on data from later time periods than it trained on, TRACE stays accurate as threats evolve, reaching a 97% macro F1-score and beating a wide range of traditional and deep learning baselines. It is the substantially expanded successor to the earlier HackER "Predicting Organizational Cybersecurity Risk" preprint.

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Citation

Benjamin M. Ampel (2026). Vendor-Conditioned Contrastive Learning for Predicting Organizational Cyber Threat Targets. In *Proceedings of the 2026 IEEE Cyber Awareness and Research Symposium (CARS)*

Contrastive Learning | Benjamin M. Ampel

Automated Cross-Repository Vulnerability Variant Retrieval Using Patch-Weighted Contrastive Learning

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Related Papers

Citation

Vendor-Conditioned Contrastive Learning for Predicting Organizational Cyber Threat Targets

Key Contributions

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Related Papers

Citation