M2S: Multi-turn to Single-turn jailbreak in Red Teaming for LLMs

• URL: http://arxiv.org/abs/2503.04856v3
• Date: Tue, 05 Aug 2025 08:06:33 GMT
• Title: M2S: Multi-turn to Single-turn jailbreak in Red Teaming for LLMs
• Authors: Junwoo Ha, Hyunjun Kim, Sangyoon Yu, Haon Park, Ashkan Yousefpour, Yuna Park, Suhyun Kim,
• Abstract summary: We introduce a novel framework for consolidating multi-turn adversarial jailbreak'' prompts into single-turn queries.<n>Our multi-turn-to-single-turn (M2S) methods systematically reformat multi-turn dialogues into structured single-turn prompts.<n>Remarkably, the single-turn prompts outperform the original multi-turn attacks by as much as 17.5 percentage points.
• Score: 8.91993614197627
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a novel framework for consolidating multi-turn adversarial ``jailbreak'' prompts into single-turn queries, significantly reducing the manual overhead required for adversarial testing of large language models (LLMs). While multi-turn human jailbreaks have been shown to yield high attack success rates, they demand considerable human effort and time. Our multi-turn-to-single-turn (M2S) methods -- Hyphenize, Numberize, and Pythonize -- systematically reformat multi-turn dialogues into structured single-turn prompts. Despite removing iterative back-and-forth interactions, these prompts preserve and often enhance adversarial potency: in extensive evaluations on the Multi-turn Human Jailbreak (MHJ) dataset, M2S methods achieve attack success rates from 70.6 percent to 95.9 percent across several state-of-the-art LLMs. Remarkably, the single-turn prompts outperform the original multi-turn attacks by as much as 17.5 percentage points while cutting token usage by more than half on average. Further analysis shows that embedding malicious requests in enumerated or code-like structures exploits ``contextual blindness'', bypassing both native guardrails and external input-output filters. By converting multi-turn conversations into concise single-turn prompts, the M2S framework provides a scalable tool for large-scale red teaming and reveals critical weaknesses in contemporary LLM defenses.

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