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There is growing interest in ensuring that large language models (LLMs) alignwith human values. However, the alignment of such models is vulnerable toadversarial jailbreaks, which coax LLMs into overriding their safetyguardrails. The identification of these vulnerabilities is thereforeinstrumental in understanding inherent weaknesses and preventing future misuse.To this end, we propose Prompt Automatic Iterative Refinement (PAIR), analgorithm that generates semantic jailbreaks with only black-box access to anLLM. PAIR -- which is inspired by social engineering attacks -- uses anattacker LLM to automatically generate jailbreaks for a separate targeted LLMwithout human intervention. In this way, the attacker LLM iteratively queriesthe target LLM to update and refine a candidate jailbreak. Empirically, PAIRoften requires fewer than twenty queries to produce a jailbreak, which isorders of magnitude more efficient than existing algorithms. PAIR also achievescompetitive jailbreaking success rates and transferability on open andclosed-source LLMs, including GPT-3.5/4, Vicuna, and PaLM-2.

Jailbreaking Black Box Large Language Models in Twenty Queries