Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs)
arxiv(2024)
摘要
Creating secure and resilient applications with large language models (LLM)
requires anticipating, adjusting to, and countering unforeseen threats.
Red-teaming has emerged as a critical technique for identifying vulnerabilities
in real-world LLM implementations. This paper presents a detailed threat model
and provides a systematization of knowledge (SoK) of red-teaming attacks on
LLMs. We develop a taxonomy of attacks based on the stages of the LLM
development and deployment process and extract various insights from previous
research. In addition, we compile methods for defense and practical red-teaming
strategies for practitioners. By delineating prominent attack motifs and
shedding light on various entry points, this paper provides a framework for
improving the security and robustness of LLM-based systems.
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