A Systematic Investigation of The RL-Jailbreaker in LLMs

• URL: http://arxiv.org/abs/2605.07032v1
• Date: Thu, 07 May 2026 23:22:07 GMT
• Title: A Systematic Investigation of The RL-Jailbreaker in LLMs
• Abstract summary: Adversarial jailbreaking is the strategic manipulation of models to elicit harmful output.<n>We present the first systematic decomposition of Reinforcement Learning jailbreaking.<n>This work provides a tool for improving RL-jailbreaker efficiency and, ultimately, harden generative models resistant to RL-based attacks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• RL-MTJail: Reinforcement Learning for Automated Black-Box Multi-Turn Jailbreaking of Large Language Models [60.201244463046784]
  Large language models are vulnerable to jailbreak attacks.<n>This paper studies black-box multi-turn jailbreaks, aiming to train attacker LLMs to elicit harmful content from black-box models.
  arXiv  Detail & Related papers  (2025-12-08T17:42:59Z)
• Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective [85.06838178922791]
  Reinforcement Learning (RL) has proven highly effective for autoregressive language models.<n>But adapting these methods to diffusion large language models (dLLMs) presents fundamental challenges.<n>We propose a principled RL framework that treats entire sequence generation as a single action and uses the ELBO as a tractable sequence-level likelihood proxy.
  arXiv  Detail & Related papers  (2025-12-03T13:05:32Z)
• AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models [75.214287449744]
  We introduce a framework for post-training policy refinement built around an Impartial World Model.<n>Our primary contribution is to teach this model to be honest about danger.<n>We demonstrate through extensive experiments, that our model significantly outperforms baselines in predicting failures.
  arXiv  Detail & Related papers  (2025-11-25T13:57:24Z)
• bi-GRPO: Bidirectional Optimization for Jailbreak Backdoor Injection on LLMs [33.470999703070866]
  Existing approaches to embedding jailbreak triggers suffer from limitations including poor generalization, compromised stealthiness, or reduced contextual usability.<n>We propose bi-GRPO, a novel RL-based framework tailored explicitly for jailbreak backdoor injection.
  arXiv  Detail & Related papers  (2025-09-24T05:56:41Z)
• Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning [25.19587561988995]
  reinforcement learning enables adversaries to more effectively break safety alignment and facilitate advanced harmful task assistance.<n>We propose TokenBuncher, the first effective defense specifically targeting RL-based harmful fine-tuning.<n>We show that TokenBuncher robustly mitigates harmful RL fine-tuning while preserving benign task utility and finetunability.
  arXiv  Detail & Related papers  (2025-08-28T12:07:11Z)
• ARMOR: Aligning Secure and Safe Large Language Models via Meticulous Reasoning [64.32925552574115]
  ARMOR is a large language model that analyzes jailbreak strategies and extracts the core intent.<n> ARMOR achieves state-of-the-art safety performance, with an average harmful rate of 0.002 and an attack success rate of 0.06 against advanced optimization-based jailbreaks.
  arXiv  Detail & Related papers  (2025-07-14T09:05:54Z)
• Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs [83.11815479874447]
  We propose a novel jailbreak attack framework, inspired by cognitive decomposition and biases in human cognition.<n>We employ cognitive decomposition to reduce the complexity of malicious prompts and relevance bias to reorganize prompts.<n>We also introduce a ranking-based harmfulness evaluation metric that surpasses the traditional binary success-or-failure paradigm.
  arXiv  Detail & Related papers  (2025-05-03T05:28:11Z)
• STShield: Single-Token Sentinel for Real-Time Jailbreak Detection in Large Language Models [31.35788474507371]
  Large Language Models (LLMs) have become increasingly vulnerable to jailbreak attacks.<n>We present STShield, a lightweight framework for real-time jailbroken judgement.
  arXiv  Detail & Related papers  (2025-03-23T04:23:07Z)
• When LLM Meets DRL: Advancing Jailbreaking Efficiency via DRL-guided Search [12.76161683514808]
  We propose RLbreaker, a black-box jailbreaking attack driven by deep reinforcement learning (DRL)<n>We show that RLbreaker is much more effective than existing jailbreaking attacks against six state-of-the-art (SOTA) LLMs.
  arXiv  Detail & Related papers  (2024-06-13T00:04:15Z)
• AutoJailbreak: Exploring Jailbreak Attacks and Defenses through a Dependency Lens [83.08119913279488]
  We present a systematic analysis of the dependency relationships in jailbreak attack and defense techniques. We propose three comprehensive, automated, and logical frameworks. We show that the proposed ensemble jailbreak attack and defense framework significantly outperforms existing research.
  arXiv  Detail & Related papers  (2024-06-06T07:24:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.