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AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs
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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.
Forward citations
Cited by 14 Pith papers
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QDRT combines behavior-conditioned RL, multiple specialized attackers, and a MAP-Elites replay buffer to generate LLM attacks that are more toxic and cover more risk-category/style combinations.
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Jailbreak-R1: Exploring the Jailbreak Capabilities of LLMs via Reinforcement Learning
A three-stage RL framework (cold start, diversity warm-up, curriculum jailbreak) trains a 7B red-team model that reports SOTA jailbreak ASR and diversity on HarmBench, though the evaluation is compromised by training-...
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A co-evolutionary attacker-defender loop, warmed up by strategies extracted from jailbreak papers, reduces jailbreak success rate on a robust model from 73% to 7% in two iterations.
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SATORI statically infers REST API test oracles from OpenAPI specs via LLMs, reporting F1 74.3%, above AGORA+'s 69.3%, with 18 confirmed bugs; the supplied full text, however, is a different paper.
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VERA frames black-box jailbreaking as variational inference, training a LoRA-tuned attacker that samples diverse fluent prompts; reported ASRs are high but several evaluation choices weaken the SOTA claims.
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