Year: 2025
Type: conference-paper
Abstract: Large Language Model (LLM) is changing the software development paradigm and has gained huge attention from both academia and industry. Researchers and developers collaboratively explore how to leverage the powerful problem-solving ability of LLMs for specific domain tasks. Due to the wide usage of LLM-based applications, e.g., ChatGPT, multiple works have been proposed to ensure the security of LLM systems. A comprehensive understanding of the entire process of LLM system construction (the LLM supply chain) is crucial, but relevant works are limited. More importantly, the security issues hidden in the LLM SC that could greatly impact the reliable usage of LLMs lack exploration. Most existing works mainly focus on assuring the quality of LLM from the model level; however, security assurance for the entire LLM SC is ignored. In this work, we take the first step to discuss the potential security risks in each component and the integration between components of LLM SC. We summarize 12 security-related risks and provide promising guidance to help build safer LLM systems. We hope our work can facilitate the evolution of artificial general intelligence with secure LLM ecosystems.
Authors: Qiang Hu, Xiaofei Xie, Sen Chen, Lili Quan, Lei Ma
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4411088261