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Large Language Models (LLMs) like ChatGPT face `jailbreak' challenges, wheresafeguards are bypassed to produce ethically harmful prompts. This studyproposes a simple black-box method to effectively generate jailbreak prompts,overcoming the high complexity and computational costs associated with existingmethods. The proposed technique iteratively rewrites harmful prompts intonon-harmful expressions using the target LLM itself, based on the hypothesisthat LLMs can directly sample expressions that bypass safeguards. Demonstratedthrough experiments with ChatGPT (GPT-3.5 and GPT-4) and Gemini-Pro, thismethod achieved an attack success rate of over 80% within an average of 5iterations and remained effective despite model updates. The generatedjailbreak prompts were naturally-worded and concise; moreover, they weredifficult-to-defend. These results indicate that creating effective jailbreakprompts is simpler than previously considered, suggesting that black-boxjailbreak attacks pose a more serious threat.

All in How You Ask for It: Simple Black-Box Method for Jailbreak Attacks