Large Language Models for Infrastructure as Code Vulnerability Remediation
In Plain Terms
Infrastructure as Code lets teams set up cloud systems automatically, but the scripts often contain insecure settings that scanners can flag yet not fix. This paper fine-tunes large language models to automatically rewrite vulnerable Terraform code into secure versions, training on thousands of real scripts and their detected misconfigurations. The results show that adapting LLMs to this specific task improves their ability to remediate cloud security flaws.
Artifacts
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Cite this
APA Reyes, R., Ampel, B. M., & Chen, H. (2025). Large Language Models for Infrastructure as Code Vulnerability Remediation. In 20th Annual Pre-ICIS Workshop on Information Security and Privacy (WISP).
BibTeX
@inproceedings{reyes2025large,
title={Large Language Models for Infrastructure as Code Vulnerability Remediation},
author={Raul Reyes and Benjamin M. Ampel and Hsinchun Chen},
booktitle={20th Annual Pre-ICIS Workshop on Information Security and Privacy (WISP)},
year={2025}
}
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.