Rapid Optimization for Jailbreaking LLMs via Subconscious Exploitation
  and Echopraxia
Guangyu Shen Siyuan Cheng Kaiyuan Zhang Guanhong Tao Shengwei An Lu Yan Zhuo Zhang Shiqing Ma Xiangyu Zhang
Large Language Models (LLMs) have become prevalent across diverse sectors,
transforming human life with their extraordinary reasoning and comprehension
abilities. As they find increased use in sensitive tasks, safety concerns have
gained widespread attention. Extensive efforts have been dedicated to aligning
LLMs with human moral principles to ensure their safe deployment. Despite their
potential, recent research indicates aligned LLMs are prone to specialized
jailbreaking prompts that bypass safety measures to elicit violent and harmful
content. The intrinsic discrete nature and substantial scale of contemporary
LLMs pose significant challenges in automatically generating diverse,
efficient, and potent jailbreaking prompts, representing a continuous obstacle.
In this paper, we introduce RIPPLE (Rapid Optimization via Subconscious
Exploitation and Echopraxia), a novel optimization-based method inspired by two
psychological concepts: subconsciousness and echopraxia, which describe the
processes of the mind that occur without conscious awareness and the
involuntary mimicry of actions, respectively. Evaluations across 6 open-source
LLMs and 4 commercial LLM APIs show RIPPLE achieves an average Attack Success
Rate of 91.5\%, outperforming five current methods by up to 47.0\% with an 8x
reduction in overhead. Furthermore, it displays significant transferability and
stealth, successfully evading established detection mechanisms. The code of our
work is available at
\url{https://github.com/SolidShen/RIPPLE_official/tree/official}