A Security Risk Taxonomy for Large Language Models
Erik Derner Kristina Batistič Jan Zahálka Robert Babuška
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.