Large Language Models (LLMs) have achieved remarkable progress, but their deployment has exposed critical vulnerabilities, particularly to jailbreak attacks that circumvent safety alignments. Guardrails--external defense mechanisms that monitor and control LLM interactions--have emerged as a promising solution. However, the current landscape of LLM guardrails is fragmented, lacking a unified taxonomy and comprehensive evaluation framework. In this Systematization of Knowledge (SoK) paper, we present the first holistic analysis of jailbreak guardrails for LLMs. We propose a novel, multi-dimensional taxonomy that categorizes guardrails along six key dimensions, and introduce a Security-Efficiency-Utility evaluation framework to assess their practical effectiveness. Through extensive analysis and experiments, we identify the strengths and limitations of existing guardrail approaches, provide insights into optimizing their defense mechanisms, and explore their universality across attack types. Our work offers a structured foundation for future research and development, aiming to guide the principled advancement and deployment of robust LLM guardrails. The code is available at https://github.com/xunguangwang/SoK4JailbreakGuardrails.
@article{arxiv.2506.10597,
title = {SoK: Evaluating Jailbreak Guardrails for Large Language Models},
author = {Xunguang Wang and Zhenlan Ji and Wenxuan Wang and Zongjie Li and Daoyuan Wu and Shuai Wang},
journal= {arXiv preprint arXiv:2506.10597},
year = {2025}
}