Unique Security And Privacy Threats Of Large Language Model: A Comprehensive Survey

Wang Shang, Zhu Tianqing, Liu Bo, Ding Ming, Guo Xu, Ye Dayong, Zhou Wanlei, Yu Philip S.. Arxiv 2024

[Paper]    
Agentic Applications Attention Mechanism Fine Tuning Model Architecture Pretraining Methods RAG Security Survey Paper Tools Training Techniques

With the rapid development of artificial intelligence, large language models (LLMs) have made remarkable advancements in natural language processing. These models are trained on vast datasets to exhibit powerful language understanding and generation capabilities across various applications, including machine translation, chatbots, and agents. However, LLMs have revealed a variety of privacy and security issues throughout their life cycle, drawing significant academic and industrial attention. Moreover, the risks faced by LLMs differ significantly from those encountered by traditional language models. Given that current surveys lack a clear taxonomy of unique threat models across diverse scenarios, we emphasize the unique privacy and security threats associated with five specific scenarios: pre-training, fine-tuning, retrieval-augmented generation systems, deployment, and LLM-based agents. Addressing the characteristics of each risk, this survey outlines potential threats and countermeasures. Research on attack and defense situations can offer feasible research directions, enabling more areas to benefit from LLMs.

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