Jailbreak Defense

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5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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SoK: Robustness in Large Language Models against Jailbreak Attacks
May 6, 2026Feiyue Xu, Hongsheng Hu, Chaoxiang He +9Language Model Safety Evaluation LLM Security
Shanghai Jiao Tong University, China · Microsoft Corporation
Catching the Infection Before It Spreads: Foresight-Guided Defense in Multi-Agent Systems
May 3, 2026Yue Ma, Ziyuan Yang, Yi ZhangMulti-Agent LLM Systems Jailbreak Defense
Sichuan University · Nanyang Technological University
Tracing the Dynamics of Refusal: Exploiting Latent Refusal Trajectories for Robust Jailbreak Detection
May 2, 2026Xulin Hu, Che Wang, Wei Yang Bryan Lim +2Adversarial Attacks on LLMs LLM Refusal Behavior
1Peking University · 2Nanyang Technological University · 3Beijing Jiaotong University
TwinGate: Stateful Defense against Decompositional Jailbreaks in Untraceable Traffic via Asymmetric Contrastive Learning
Apr 30, 2026Bowen Sun, Chaozhuo Li, Yaodong Yang +2Jailbreak Attacks LLM Security
Johns Hopkins University · Microsoft Research Asia · Peking University +1
Cross-Lingual Jailbreak Detection via Semantic Codebooks
Apr 28, 2026Shirin Alanova, Bogdan Minko, Sabrina Sadiekh +1Multilingual Language Model Evaluation LLM Security
AI Talent Hub, ITMO University · HiveTraceLab
One Jailbreak, Many Tongues: Learning Language-Insensitive Intention Representations for Multilingual Jailbreak Detection
Apr 22, 2026Shuyu Jiang, Kaiyu Xu, Xingshu Chen +5Multilingual Language Models LLM Jailbreak Attacks
School of Cyber Science and Engineering, Sichuan University, Chengdu, 610065, China · School of Computer Science and Engineering, Nanyang Technological University · School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
Towards Understanding the Robustness of Sparse Autoencoders
Apr 20, 2026Ahson Saiyed, Sabrina Sadiekh, Chirag AgarwalAdversarial Attacks on LLMs Jailbreak Robustness
University of Virginia · Independent Researcher
SafeDream: Safety World Model for Proactive Early Jailbreak Detection
Apr 18, 2026Bo Yan, Weikai Lin, Yada Zhu +1Language Model Safety Evaluation World Models
University of Central Florida · University of Rochester · IBM Research
Sparse Autoencoders are Capable LLM Jailbreak Mitigators
Feb 12, 2026Yannick Assogba, Jacopo Cortellazzi, Javier Abad +3Sparse Autoencoders LLM Jailbreak Attacks
1Apple · 2ETH Zurich
Response-Based Knowledge Distillation for Multilingual Jailbreak Prevention Unwittingly Compromises Safety
Dec 8, 2025Max Zhang, Derek Liu, Kai Zhang +2Adversarial Attacks on LLMs LLM Safety Alignment
Attention Misses Visual Risk: Risk-Adaptive Steering for Multimodal Safety Alignment
Oct 15, 2025Jonghyun Park, Minhyuk Seo, Chaewon Yeo +1Multimodal Robustness LLM Safety Alignment
Seoul National University · KU Leuven
Decoding One Safety Trigger Token for Balancing Safety and Usability in Large Language Models
May 12, 2025Haoran Gu, Handing Wang, Yi Mei +2LLM Safety Alignment LLM Safety
Xidian University · Victoria University of Wellington · Westlake University
Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge
Apr 8, 2024Weikai Lu, Ziqian Zeng, Jianwei Wang +5LLM Safety Alignment LLM Jailbreak Attacks
South China University of Technology, China · Pazhou Laboratory, China

May 6, 2026, cs.CRJ/K move · Enter open · S save

SoK: Robustness in Large Language Models against Jailbreak Attacks

Feiyue Xu, Hongsheng Hu, Chaoxiang He, Sheng Hang+8

Shanghai Jiao Tong University, China · Microsoft Corporation

Language Model Safety Evaluation LLM Security LLM Jailbreak Attacks Adversarial Robustness of Language Models Jailbreak Defense

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Large Language Models (LLMs) have achieved remarkable success but remain highly susceptible to jailbreak attacks, in which adversarial prompts coerce models into generating harmful, unethical, or policy-violating outputs. Such attacks pose real-world risks, eroding safety, trust, and regulatory compliance in high-stakes applications. Although a variety of attack and defense methods have been proposed, existing evaluation practices are inadequate, often relying on narrow metrics like attack success rate that fail to capture the multidimensional nature of LLM security. In this paper, we present a systematic taxonomy of jailbreak attacks and defenses and introduce Security Cube, a unified, multi-dimensional framework for comprehensive evaluation of these techniques. We provide detailed comparison tables of existing attacks and defenses, highlighting key insights and open challenges across the literature. Leveraging Security Cube, we conduct benchmark studies on 13 representative attacks and 5 defenses, establishing a clear view of the current landscape encompassing jailbreak attacks, defenses, automated judges, and LLM vulnerabilities. Based on these evaluations, we distill critical findings, identify unresolved problems, and outline promising research directions for enhancing LLM robustness against jailbreak attacks. Our analysis aims to pave the way towards more robust, interpretable, and trustworthy LLM systems. Our code is available at Code.

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