# A Domain-Based Taxonomy of Jailbreak Vulnerabilities in Large Language Models

arXiv: https://arxiv.org/abs/2504.04976 · HTML: https://mlsanity.com/paper/2504.04976/

## 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 38 of its 51 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Placed in

- LLM safety and security / [Jailbreaking](https://mlsanity.com/safety/jailbreaking/)

## Builds on (most cited first, 30 of 38)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Online Learning: A Modern Introduction Using Convex Optimization](https://mlsanity.com/paper/1912.13213/) (2019)
- [Learning to Drive in a Day](https://mlsanity.com/paper/1807.00412/) (2018)
- [Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning](https://mlsanity.com/paper/1703.07710/) (2017)
- [Constrained Reinforcement Learning Has Zero Duality Gap](https://mlsanity.com/paper/1910.13393/) (2019)
- [Exploration-Exploitation in Constrained MDPs](https://mlsanity.com/paper/2003.02189/) (2020)
- [Safe Reinforcement Learning in Constrained Markov Decision Processes](https://mlsanity.com/paper/2008.06626/) (2020)
- [Learning Adversarial MDPs with Bandit Feedback and Unknown Transition](https://mlsanity.com/paper/1912.01192/) (2019)
- [Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs](https://mlsanity.com/paper/2106.02684/) (2021)
- [Online Convex Optimization in Adversarial Markov Decision Processes](https://mlsanity.com/paper/1905.07773/) (2019)
- [Safe Policies for Reinforcement Learning via Primal-Dual Methods](https://mlsanity.com/paper/1911.09101/) (2019)
- [Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation](https://mlsanity.com/paper/2202.06558/) (2022)
- [Constrained Upper Confidence Reinforcement Learning](https://mlsanity.com/paper/2001.09377/) (2020)
- [Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss](https://mlsanity.com/paper/2003.00660/) (2020)
- [Inverse Constrained Reinforcement Learning](https://mlsanity.com/paper/2011.09999/) (2020)
- [A Survey of Constraint Formulations in Safe Reinforcement Learning](https://mlsanity.com/paper/2402.02025/) (2024)
- [Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies](https://mlsanity.com/paper/1905.11527/) (2019)
- [Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization](https://mlsanity.com/paper/2003.01303/) (2020)
- [DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning](https://mlsanity.com/paper/2112.00885/) (2021)
- [A Dual Approach to Constrained Markov Decision Processes with Entropy Regularization](https://mlsanity.com/paper/2110.08923/) (2021)
- [Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms](https://mlsanity.com/paper/2310.03225/) (2023)
- [Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs](https://mlsanity.com/paper/2206.02346/) (2022)
- [Truly No-Regret Learning in Constrained MDPs](https://mlsanity.com/paper/2402.15776/) (2024)
- [Provably Efficient Model-Free Algorithms for Non-stationary CMDPs](https://mlsanity.com/paper/2303.05733/) (2023)
- [Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process](https://mlsanity.com/paper/2110.10351/) (2021)
- [Learning Safety Constraints from Demonstrations with Unknown Rewards](https://mlsanity.com/paper/2305.16147/) (2023)
- [Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning](https://mlsanity.com/paper/2310.03718/) (2023)
- [Online Learning in Weakly Coupled Markov Decision Processes: A Convergence Time Study](https://mlsanity.com/paper/1709.03465/) (2017)
- [Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints](https://mlsanity.com/paper/2006.05026/) (2020)
- [Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning](https://mlsanity.com/paper/2412.18946/) (2024)

## Abstract (arXiv)

The study of large language models (LLMs) is a key area in open-world machine learning. Although LLMs demonstrate remarkable natural language processing capabilities, they also face several challenges, including consistency issues, hallucinations, and jailbreak vulnerabilities. Jailbreaking refers to the crafting of prompts that bypass alignment safeguards, leading to unsafe outputs that compromise the integrity of LLMs. This work specifically focuses on the challenge of jailbreak vulnerabilities and introduces a novel taxonomy of jailbreak attacks grounded in the training domains of LLMs. It characterizes alignment failures through generalization, objectives, and robustness gaps. Our primary contribution is a perspective on jailbreak, framed through the different linguistic domains that emerge during LLM training and alignment. This viewpoint highlights the limitations of existing approaches and enables us to classify jailbreak attacks on the basis of the underlying model deficiencies they exploit. Unlike conventional classifications that categorize attacks based on prompt construction methods (e.g., prompt templating), our approach provides a deeper understanding of LLM behavior. We introduce a taxonomy with four categories -- mismatched generalization, competing objectives, adversarial robustness, and mixed attacks -- offering insights into the fundamental nature of jailbreak vulnerabilities. Finally, we present key lessons derived from this taxonomic study.