# AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs

arXiv: https://arxiv.org/abs/2410.05295 · HTML: https://mlsanity.com/paper/2410.05295/

## Key facts

- Cited by 199 papers in the map (data through 14 Oct 2026).
- Placed in Jailbreaking, one of 12 directions in LLM safety and security.
- Builds on 20 of its 49 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Placed in

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

## Builds on (most cited first, 20 of 20)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [Jailbroken: How Does LLM Safety Training Fail?](https://mlsanity.com/paper/2307.02483/) (2023)
- [HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal](https://mlsanity.com/paper/2402.04249/) (2024)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models](https://mlsanity.com/paper/2310.04451/) (2023)
- [Tree of Attacks: Jailbreaking Black-Box LLMs Automatically](https://mlsanity.com/paper/2312.02119/) (2023)
- [How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs](https://mlsanity.com/paper/2401.06373/) (2024)
- [Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations](https://mlsanity.com/paper/2310.06387/) (2023)
- [GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher](https://mlsanity.com/paper/2308.06463/) (2023)
- [JailBreakV: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks](https://mlsanity.com/paper/2404.03027/) (2024)
- [ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs](https://mlsanity.com/paper/2402.11753/) (2024)
- [AmpleGCG: Learning a Universal and Transferable Generative Model of Adversarial Suffixes for Jailbreaking Both Open and Closed LLMs](https://mlsanity.com/paper/2404.07921/) (2024)
- [Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking](https://mlsanity.com/paper/2311.09827/) (2023)
- [CodeChameleon: Personalized Encryption Framework for Jailbreaking Large Language Models](https://mlsanity.com/paper/2402.16717/) (2024)
- [Latent Jailbreak: A Benchmark for Evaluating Text Safety and Output Robustness of Large Language Models](https://mlsanity.com/paper/2307.08487/) (2023)
- [From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?](https://mlsanity.com/paper/2308.01990/) (2023)
- [FuzzLLM: A Novel and Universal Fuzzing Framework for Proactively Discovering Jailbreak Vulnerabilities in Large Language Models](https://mlsanity.com/paper/2309.05274/) (2023)
- [GUARD: Role-playing to Generate Natural-language Jailbreakings to Test Guideline Adherence of Large Language Models](https://mlsanity.com/paper/2402.03299/) (2024)
- [Foot In The Door: Understanding Large Language Model Jailbreaking via Cognitive Psychology](https://mlsanity.com/paper/2402.15690/) (2024)

## Built on by (most cited first, 30 of 199)

- [A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment](https://mlsanity.com/paper/2504.15585/) (2025)
- [The Hidden Risks of Large Reasoning Models: A Safety Assessment of R1](https://mlsanity.com/paper/2502.12659/) (2025)
- [AutoRedTeamer: Autonomous Red Teaming with Lifelong Attack Integration](https://mlsanity.com/paper/2503.15754/) (2025)
- [Safety is Not Only About Refusal: Reasoning-Enhanced Fine-tuning for Interpretable LLM Safety](https://mlsanity.com/paper/2503.05021/) (2025)
- [Prompt Injection Attacks on Agentic Coding Assistants: A Systematic Analysis of Vulnerabilities in Skills, Tools, and Protocol Ecosystems](https://mlsanity.com/paper/2601.17548/) (2026)
- [Adversarial Reasoning at Jailbreaking Time](https://mlsanity.com/paper/2502.01633/) (2025)
- [Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks](https://mlsanity.com/paper/2504.18838/) (2025)
- [Faster-GCG: Efficient Discrete Optimization Jailbreak Attacks against Aligned Large Language Models](https://mlsanity.com/paper/2410.15362/) (2024)
- [SAFEFLOW: A Principled Protocol for Trustworthy and Transactional Autonomous Agent Systems](https://mlsanity.com/paper/2506.07564/) (2025)
- [Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition](https://mlsanity.com/paper/2507.20526/) (2025)
- [MAGIC: A Co-Evolving Attacker-Defender Adversarial Game for Robust LLM Safety](https://mlsanity.com/paper/2602.01539/) (2026)
- [MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks](https://mlsanity.com/paper/2602.09222/) (2026)
- [Steering Dialogue Dynamics for Robustness against Multi-turn Jailbreaking Attacks](https://mlsanity.com/paper/2503.00187/) (2025)
- [SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning](https://mlsanity.com/paper/2505.16186/) (2025)
- [Adjacent Words, Divergent Intents: Jailbreaking Large Language Models via Task Concurrency](https://mlsanity.com/paper/2510.21189/) (2025)
- [Lifelong Safety Alignment for Language Models](https://mlsanity.com/paper/2505.20259/) (2025)
- [Short-length Adversarial Training Helps LLMs Defend Long-length Jailbreak Attacks: Theoretical and Empirical Evidence](https://mlsanity.com/paper/2502.04204/) (2025)
- [Doxing via the Lens: Revealing Location-related Privacy Leakage on Multi-modal Large Reasoning Models](https://mlsanity.com/paper/2504.19373/) (2025)
- [TROJail: Trajectory-Level Optimization for Multi-Turn Large Language Model Jailbreaks with Process Rewards](https://mlsanity.com/paper/2512.07761/) (2025)
- [CoP: Agentic Red-teaming for Large Language Models using Composition of Principles](https://mlsanity.com/paper/2506.00781/) (2025)
- [AdapTools: Adaptive Tool-based Indirect Prompt Injection Attacks on Agentic LLMs](https://mlsanity.com/paper/2602.20720/) (2026)
- [Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search](https://mlsanity.com/paper/2602.22983/) (2026)
- [GASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMs](https://mlsanity.com/paper/2411.14133/) (2024)
- [AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement](https://mlsanity.com/paper/2502.16776/) (2025)
- [GuidedBench: Measuring and Mitigating the Evaluation Discrepancies of In-the-wild LLM Jailbreak Methods](https://mlsanity.com/paper/2502.16903/) (2025)
- [StealthRank: LLM Ranking Manipulation via Stealthy Prompt Optimization](https://mlsanity.com/paper/2504.05804/) (2025)
- [Jailbreaking on Text-to-Video Models via Scene Splitting Strategy](https://mlsanity.com/paper/2509.22292/) (2025)
- [Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt Generation for Enhanced LLM Content Moderation](https://mlsanity.com/paper/2501.18638/) (2025)
- [Adversarial Tokenization](https://mlsanity.com/paper/2503.02174/) (2025)
- [RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary Quality-Diversity Search](https://mlsanity.com/paper/2504.15047/) (2025)

## Abstract (arXiv)

In this paper, we propose AutoDAN-Turbo, a black-box jailbreak method that can automatically discover as many jailbreak strategies as possible from scratch, without any human intervention or predefined scopes (e.g., specified candidate strategies), and use them for red-teaming. As a result, AutoDAN-Turbo can significantly outperform baseline methods, achieving a 74.3% higher average attack success rate on public benchmarks. Notably, AutoDAN-Turbo achieves an 88.5 attack success rate on GPT-4-1106-turbo. In addition, AutoDAN-Turbo is a unified framework that can incorporate existing human-designed jailbreak strategies in a plug-and-play manner. By integrating human-designed strategies, AutoDAN-Turbo can even achieve a higher attack success rate of 93.4 on GPT-4-1106-turbo.