Large Language Models (LLMs) have demonstrated effectiveness across various tasks, yet they remain susceptible to malicious exploitation. Adversaries can circumvent LLMs’ safety constraints (“jail”) through carefully engineered “jailbreaking” prompts. Researchers have developed various jailbreak techniques leveraging optimization, obfuscation, and persuasive tactics to assess LLM security. However, these approaches frame LLMs as passive targets of manipulation, overlooking their capacity for active reasoning. In this work, we introduce Persu-Agent, a novel jailbreak framework grounded in Greenwald’s Cognitive Response Theory. Unlike previous approaches that focus primarily on prompt design, we target the LLM’s internal reasoning process. Persu-Agent prompts the model to generate its justifications for harmful queries, effectively persuading itself. Experiments on advanced open-source and commercial LLMs show that Persu-Agent achieves an average jailbreak success rate of 84%, outperforming existing SOTA methods. Our findings offer new insights into the cognitive tendencies of LLMs and contribute to developing more secure and robust LLMs.
Wang et al. (2025) studied this question.