  Jailbreak attacks on Language Model Models (LLMs) entail crafting prompts
aimed at exploiting the models to generate malicious content. Existing
jailbreak attacks can successfully deceive the LLMs, however they cannot
deceive the human. This paper proposes a new type of jailbreak attacks which
can deceive both the LLMs and human (i.e., security analyst). The key insight
of our idea is borrowed from the social psychology - that is human are easily
deceived if the lie is hidden in truth. Based on this insight, we proposed the
logic-chain injection attacks to inject malicious intention into benign truth.
Logic-chain injection attack firstly dissembles its malicious target into a
chain of benign narrations, and then distribute narrations into a related
benign article, with undoubted facts. In this way, newly generate prompt cannot
only deceive the LLMs, but also deceive human.
