An Empirical Study of Vulnerable Package Dependencies in LLM Repositories

• URL: http://arxiv.org/abs/2508.21417v1
• Date: Fri, 29 Aug 2025 08:38:58 GMT
• Title: An Empirical Study of Vulnerable Package Dependencies in LLM Repositories
• Authors: Shuhan Liu, Xing Hu, Xin Xia, David Lo, Xiaohu Yang,
• Abstract summary: Large language models (LLMs) rely on external code dependencies from package management systems.<n>Vulnerabilities in dependencies can expose LLMs to security risks.<n>Half of the vulnerabilities in the LLM ecosystem remain undisclosed for more than 56.2 months, significantly longer than those in the Python ecosystem.
• Score: 14.817045028745563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have developed rapidly in recent years, revolutionizing various fields. Despite their widespread success, LLMs heavily rely on external code dependencies from package management systems, creating a complex and interconnected LLM dependency supply chain. Vulnerabilities in dependencies can expose LLMs to security risks. While existing research predominantly focuses on model-level security threats, vulnerabilities within the LLM dependency supply chain have been overlooked. To fill this gap, we conducted an empirical analysis of 52 open-source LLMs, examining their third-party dependencies and associated vulnerabilities. We then explored activities within the LLM repositories to understand how maintainers manage third-party vulnerabilities in practice. Finally, we compared third-party dependency vulnerabilities in the LLM ecosystem to those in the Python ecosystem. Our results show that half of the vulnerabilities in the LLM ecosystem remain undisclosed for more than 56.2 months, significantly longer than those in the Python ecosystem. Additionally, 75.8% of LLMs include vulnerable dependencies in their configuration files. This study advances the understanding of LLM supply chain risks, provides insights for practitioners, and highlights potential directions for improving the security of the LLM supply chain.

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