Foot In The Door: Understanding Large Language Model Jailbreaking via
  Cognitive Psychology
Zhenhua Wang Wei Xie Baosheng Wang Enze Wang Zhiwen Gui Shuoyoucheng Ma Kai Chen
Large Language Models (LLMs) have gradually become the gateway for people to
acquire new knowledge. However, attackers can break the model's security
protection ("jail") to access restricted information, which is called
"jailbreaking." Previous studies have shown the weakness of current LLMs when
confronted with such jailbreaking attacks. Nevertheless, comprehension of the
intrinsic decision-making mechanism within the LLMs upon receipt of jailbreak
prompts is noticeably lacking. Our research provides a psychological
explanation of the jailbreak prompts. Drawing on cognitive consistency theory,
we argue that the key to jailbreak is guiding the LLM to achieve cognitive
coordination in an erroneous direction. Further, we propose an automatic
black-box jailbreaking method based on the Foot-in-the-Door (FITD) technique.
This method progressively induces the model to answer harmful questions via
multi-step incremental prompts. We instantiated a prototype system to evaluate
the jailbreaking effectiveness on 8 advanced LLMs, yielding an average success
rate of 83.9%. This study builds a psychological perspective on the explanatory
insights into the intrinsic decision-making logic of LLMs.