首次系统分析大模型供应链安全风险，揭示12类隐患。
Large Language Model Supply Chain: Open Problems From the Security Perspective
- 拆解大模型从构建到应用的全流程，识别各环节安全漏洞
- 提出12类关键安全风险，覆盖数据、训练、部署等阶段
- 适合关注AI安全与可信系统的研究者和开发者
大型语言模型（LLM）正在改变软件开发范式，受到学术界和产业界的广泛关注。研究人员和开发者正协作探索如何利用LLM强大的问题求解能力完成特定领域任务。由于基于LLM的应用（如ChatGPT）广泛应用，已有大量工作致力于保障LLM系统的安全性。然而，对整个LLM系统构建过程（即LLM供应链）的全面理解仍显不足，且隐藏在LLM供应链中的安全问题对可靠使用造成重大影响，尚未得到充分探讨。现有研究主要聚焦于模型层面的质量保证，忽视了对整个LLM供应链的安全保障。本文首次系统性地讨论了LLM供应链中各组件及组件间集成所面临的潜在安全风险，总结出12类安全相关风险，并提供有前景的指导建议，以帮助构建更安全的LLM系统。我们希望本工作能推动具备安全生态的通用人工智能演进。
原文摘要 · Abstract (English)
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. However, a comprehensive understanding of the entire processes 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 which could highly impact the reliable usage of LLMs are lack of exploration. Existing works mainly focus on assuring the quality of LLM from the model level, 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 as well as 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.
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