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SoK: Evaluating Jailbreak Guardrails for Large Language Models

Authors: Xunguang Wang, Zhenlan Ji, Wenxuan Wang, Zongjie Li, Daoyuan Wu, Shuai Wang

Published: 2025-06-12

arXiv ID: 2506.10597v1

Added to Library: 2025-06-13 03:01 UTC

Red Teaming

📄 Abstract

Large Language Models (LLMs) have achieved remarkable progress, but their deployment has exposed critical vulnerabilities, particularly to jailbreak attacks that circumvent safety mechanisms. Guardrails--external defense mechanisms that monitor and control LLM interaction--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, explore their universality across attack types, and provide insights into optimizing defense combinations. 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.

🔍 Key Points

  • Introduces a multi-dimensional taxonomy for categorizing jailbreak guardrails along six key dimensions: intervention stages, technical paradigms, safety granularity, reactivity, applicability, and interpretability.
  • Proposes a comprehensive Security-Efficiency-Utility (SEU) evaluation framework to assess the performance of guardrails, balancing their defense effectiveness against operational efficiency and user experience.
  • Conducts extensive analysis and experimental evaluations of existing guardrail methods, highlighting their strengths and weaknesses and providing insights into optimizing defenses against various jailbreak attack types.
  • Investigates the universality of guardrails across different attack modalities and LLM types, indicating paths for future research in developing more generalizable defenses.
  • Establishes a benchmarking and leaderboard system for guardrail effectiveness, offering a resource for practitioners to select optimal guardrail configurations based on specific deployment scenarios.

💡 Why This Paper Matters

This paper is vital for understanding and improving the security measures in large language models, which are increasingly vulnerable to sophisticated jailbreak attacks. Its structured taxonomy and evaluation framework pave the way for systematic advancements in the development and deployment of guardrails, fostering safer AI applications. By addressing the fragmentation in the landscape of existing defense mechanisms, it promotes a unified approach to combating adversarial threats in LLMs.

🎯 Why It's Interesting for AI Security Researchers

The insights and methodologies presented in this paper are crucial for AI security researchers focusing on the robustness of language models. Given the growing concern over the deployment of LLMs in sensitive applications, this work provides necessary frameworks for evaluating and enhancing their security against jailbreak attacks. Its comprehensive analysis of guardrail strategies contributes to the ongoing discourse on AI safety, making it a vital reference for future research and development efforts aimed at fortifying LLM security.

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