As large language models (LLMs) permeate more and more applications, an
assessment of their associated security risks becomes increasingly necessary.
The potential for exploitation by malicious actors, ranging from disinformation
to data breaches and reputation damage, is substantial. This paper addresses a
gap in current research by focusing on the security risks posed by LLMs, which
extends beyond the widely covered ethical and societal implications. Our work
proposes a taxonomy of security risks along the user-model communication
pipeline, explicitly focusing on prompt-based attacks on LLMs. We categorize
the attacks by target and attack type within a prompt-based interaction scheme.
The taxonomy is reinforced with specific attack examples to showcase the
real-world impact of these risks. Through this taxonomy, we aim to inform the
development of robust and secure LLM applications, enhancing their safety and
trustworthiness.

通过针对大型语言模型（LLM）存在的安全风险进行评估，本研究填补了当前研究的空白，并提出了一种基于提示的攻击风险分类方法，以强调 LLM 在用户 - 模型通信路径上的安全风险。该分类方法通过具体的攻击示例加以支持，并旨在为安全性强、值得信赖的 LLM 应用程序的开发提供指导。