A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models

Understand

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 specifically focusing on security risks posed by LLMs within the prompt-based interaction scheme, which extends beyond the widely covered ethical and societal implications.
• Our work proposes a taxonomy of security risks along the user-model communication pipeline and categorizes the attacks by target and attack type alongside the commonly used confidentiality, integrity, and availability (CIA) triad.