Assessing the Vulnerabilities of the Open-Source Artificial Intelligence (AI) Landscape: A Large-Scale Analysis of the Hugging Face Platform

Artificial Intelligence (AI) has rapidly proliferated as a critical disruptive technology in the 21st century. Hugging Face hosts pre-trained models, facilitating the sharing and use of open-source code. Hugging Face has been used by 22,000+ organizations, including Intel and Microsoft, with 2.6+ billion model downloads. While Hugging Face democratizes access to AI models, these models may contain unknown security vulnerabilities. In this research, we automatically collect models from Hugging Face, link them to their underlying code bases on GitHub, and perform a large-scale vulnerability assessment of these repositories. Through our approaches, we collected about 110,000 models from Hugging Face and over 29,000 GitHub repositories. Our vulnerability assessment revealed a larger percentage (35.98^%) of high-severity vulnerabilities compared to low-severity vulnerabilities (6.79%). This trend in severity levels contradicts the results of severities detected in repositories forked from root repositories and searched repositories. Given that many of the vulnerabilities reside in fundamental AI repositories such as Transformers, the results of this vulnerability assessment have significant implications for supply chain software security and AI risk management more broadly.