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 (ASRs), they demand considerable human effort and time. Our proposed Multi-turn-to-Single-turn (M2S) methods—Hyphenize, Numberize, and Pythonize—systematically reformat multi-turn dialogues into structured single-turn prompts. Despite eliminating iterative back-and-forth interactions, these reformatted prompts preserve and often enhance adversarial potency: in extensive evaluations on the Multi-turn Human Jailbreak (MHJ) dataset, M2S methods yield ASRs ranging from 70.6 % to 95.9 % across various state-of-the-art LLMs. Remarkably, our single-turn prompts outperform the original multi-turn attacks by up to 17.5 % in absolute ASR, while reducing token usage by more than half on average. Further analyses reveal that embedding malicious requests in enumerated or code-like structures exploits “contextual blindness,” undermining both native guardrails and external input-output safeguards. By consolidating multi-turn conversations into efficient single-turn prompts, our M2S framework provides a powerful tool for large-scale red-teaming and exposes critical vulnerabilities in contemporary LLM defenses. All code, data, and conversion prompts are available for reproducibility and further investigations: https://github.com/Junuha/M2S_DATA