Despite remarkable success in various applications, large language models (LLMs) are vulnerable to adversarial jailbreaks that make the safety guardrails void. However, previous studies for jailbreaks usually resort to brute-force optimization or extrapolations of a high computation cost, which might not be practical or effective. In this paper, inspired by the Milgram experiment w.r.t. the authority power for inciting harmfulness, we disclose a lightweight method, termed as DeepInception, which can hypnotize an LLM to be a jailbreaker. Specifically, DeepInception leverages the personification ability of LLM to construct a virtual, nested scene to jailbreak, which realizes an adaptive way to escape the usage control in a normal scenario. Empirically, DeepInception can achieve competitive jailbreak success rates with previous counterparts and realize a continuous jailbreak in subsequent interactions, which reveals the critical weakness of self-losing on both open-source and closed-source LLMs like Falcon, Vicuna-v1.5, Llama-2, GPT-3.5, and GPT-4. The code is publicly available at: https://github.com/tmlr-group/DeepInception.

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