Identifying Vulnerable GitHub Repositories and Users in Scientific Cyberinfrastructure: An Unsupervised Graph Embedding Approach

Ben Lazarine, Sagar Samtani, Mark Patton, Hongyi Zhu, Steven Ullman, Benjamin M. Ampel, Hsinchun Chen
Last updated on Jun 9, 2026
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Scientific research teams lean heavily on GitHub to share code, but public repositories can leak secrets and expose insecure code. This study uses social-network analysis and graph machine learning to map relationships among GitHub users and repositories and cluster those with similar vulnerabilities, finding that high-impact genomics projects were prone to leaked secrets and insecure coding.

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APA Lazarine, B., Samtani, S., Patton, M., Zhu, H., Ullman, S., Ampel, B. M., & Chen, H. (2020). Identifying Vulnerable GitHub Repositories and Users in Scientific Cyberinfrastructure: An Unsupervised Graph Embedding Approach. In 2020 IEEE International Conference on Intelligence and Security Informatics (ISI). https://doi.org/10.1109/ISI49825.2020.9280544
BibTeX
@inproceedings{lazarine2020identifying,
  title={Identifying Vulnerable GitHub Repositories and Users in Scientific Cyberinfrastructure: An Unsupervised Graph Embedding Approach },
  author={Ben Lazarine and Sagar Samtani and Mark Patton and Hongyi Zhu and Steven Ullman and Benjamin M. Ampel and Hsinchun Chen},
  booktitle={2020 IEEE International Conference on Intelligence and Security Informatics (ISI)},
  year={2020},
  doi={10.1109/ISI49825.2020.9280544}
}
Vulnerability Scanning Deep Learning Graph Embedding GitHub
Benjamin M. Ampel

Benjamin M. Ampel

Assistant Professor in Computer Information Systems and Director, Center for CyberAI Research (CCAIR)

My research focuses on AI-enabled Cybersecurity, including Cyber Threat Intelligence, Large Language Models, and Phishing Detection.