mlsanityData to 14 Oct 2026

A Systematic Investigation of RL-Jailbreaking in LLMs

arXiv 2605.07032 ↗First posted 7 May 2026cs.LG cs.AIcited by 0

On the map: LLM safety and security → Jailbreaking

Abstract (arXiv)

The evolution of generative models from next-token predictors to autonomous engines of complex systems necessitates rigorous safety hardening. Adversarial jailbreaking, the strategic manipulation of models to elicit harmful output, remains a primary threat to safe deployment. While Reinforcement Learning (RL) frames jailbreaking as a multi-step attack through sequential optimization, a mechanistic understanding of why the framework succeeds remains incomplete. To fill this gap, we present the first systematic decomposition of RL jailbreaking. We deconstruct the framework into problem formalization (reward function, action space, episode length), and algorithmic measures (RL algorithm, training data, reward-shaping) to identify the structural determinants of adversarial success. Our results reveal that the RL-jailbreaker successfully compromised all targeted models and safeguards. Through this first-of-its-kind analysis, we demonstrate that environment formalization, specifically dense rewards and extended episode lengths, is the primary driver of jailbreaking success. This work provides a tool for improving RL-jailbreaker efficiency and, ultimately, harden generative models resistant to RL-based attacks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Jailbreaking, one of 12 directions in LLM safety and security. Builds on 57 of its 75 references that are in the map. First posted on arXiv 7 May 2026.

Builds on

57 of 75 references.

Closest ancestors · 15

Same direction, newest first

  1. 2025The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections10 Oct
  2. 2025Formalization Driven LLM Prompt Jailbreaking via Reinforcement Learning28 Sep
  3. 2025Lifelong Safety Alignment for Language Models26 May
  4. 2025Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming31 Jan
  5. 2024Best-of-N Jailbreaking4 Dec
  6. 2024RL-JACK: Reinforcement Learning-powered Black-box Jailbreaking Attack against LLMs13 Jun
  7. 2024When LLM Meets DRL: Advancing Jailbreaking Efficiency via DRL-guided Search13 Jun
  8. 2024A StrongREJECT for Empty Jailbreaks15 Feb
  9. 2024How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs12 Jan
  10. 2023Tree of Attacks: Jailbreaking Black-Box LLMs Automatically4 Dec
  11. 2023DeepInception: Hypnotize Large Language Model to Be Jailbreaker6 Nov
  12. 2023GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher12 Aug
3 more
  1. 2023"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models7 Aug
  2. 2023Universal and Transferable Adversarial Attacks on Aligned Language Models27 Jul
  3. 2023Jailbroken: How Does LLM Safety Training Fail?5 Jul

Other references · 42

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2025Qwen3 Technical Reportcited by 8,820
  5. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  6. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  7. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  8. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  9. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  10. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  11. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
  12. 2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusalcited by 1,429
30 more
  1. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,255
  2. 2022Red Teaming Language Models with Language Modelscited by 1,026
  3. 2019Universal Adversarial Triggers for Attacking and Analyzing NLPcited by 722
  4. 2023The Linear Representation Hypothesis and the Geometry of Large Language Modelscited by 716
  5. 2024WildChat: 1M ChatGPT Interaction Logs in the Wildcited by 629
  6. 2020BERT-ATTACK: Adversarial Attack Against BERT Using BERTcited by 491
  7. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 366
  8. 2022A Holistic Approach to Undesired Content Detection in the Real Worldcited by 356
  9. 2024ShieldGemma: Generative AI Content Moderation Based on Gemmacited by 243
  10. 2021Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Modelscited by 232
  11. 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challengescited by 169
  12. 2024Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversationscited by 129
  13. 2023Explore, Establish, Exploit: Red Teaming Language Models from Scratchcited by 114
  14. 2024Limitations of the LLM-as-a-Judge Approach for Evaluating LLM Outputs in Expert Knowledge Taskscited by 106
  15. 2024Curiosity-driven Red-teaming for Large Language Modelscited by 88
  16. 2024SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model Safetycited by 77
  17. 2024Can You Trust LLM Judgments? Reliability of LLM-as-a-Judgecited by 56
  18. 2025The OpenHands Software Agent SDK: A Composable and Extensible Foundation for Production Agentscited by 45
  19. 2020ANLIzing the Adversarial Natural Language Inference Datasetcited by 36
  20. 2025Jailbreak-R1: Exploring the Jailbreak Capabilities of LLMs via Reinforcement Learningcited by 27
  21. 2026Dolphin-v2: Universal Document Parsing via Scalable Anchor Promptingcited by 22
  22. 2026A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustnesscited by 20
  23. 2023Beyond Classification: Financial Reasoning in State-of-the-Art Language Modelscited by 19
  24. 2026Tiny Aya: Bridging Scale and Multilingual Depthcited by 15
  25. 2025STACK: Adversarial Attacks on LLM Safeguard Pipelinescited by 14
  26. 2023A Survey on Malware Detection with Graph Representation Learningcited by 11
  27. 2025Building Safe GenAI Applications: An End-to-End Overview of Red Teaming for Large Language Modelscited by 11
  28. 2024DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing Constraintscited by 10
  29. 2025Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large Language Modelscited by 9
  30. 2025CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attackscited by 4

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  1. 2024PathSeeker: Exploring LLM Security Vulnerabilities with a Reinforcement Learning-Based Jailbreak Approach
  2. 2026TrailBlazer: History-Guided Reinforcement Learning for Black-Box LLM Jailbreaking
  3. 2025"To Survive, I Must Defect": Jailbreaking LLMs via the Game-Theory Scenarios
  4. 2026Compositional Jailbreaking: An Empirical Analysis of Mutator Chain Interactions in Aligned LLMs
  5. 2026ATTCK-R: Knowledge-Aware Long-Horizon Jailbreaking for Protected LLMs
  6. 2026Metis: Learning to Jailbreak LLMs via Self-Evolving Metacognitive Policy Optimization
  7. 2025MetaCipher: A Time-Persistent and Universal Multi-Agent Framework for Cipher-Based Jailbreak Attacks for LLMs
  8. 2025Advancing Jailbreak Strategies: A Hybrid Approach to Exploiting LLM Vulnerabilities and Bypassing Modern Defenses