AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to
  Jailbreak LLMs
Authors: name: Xiaogeng Liu, name: Peiran Li, name: Edward Suh, name: Yevgeniy Vorobeychik, name: Zhuoqing Mao, name: Somesh Jha, name: Patrick McDaniel, name: Huan Sun, name: Bo Li, name: Chaowei Xiao

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.