Adversarial Poetry as a Universal Single-Turn Jailbreak Mechanism in Large Language Models

We present evidence that adversarial poetry functions as a universal single-turn jailbreak technique for large language models (LLMs). Poetic framing achieved an average jailbreak success rate of 62% for hand-crafted poems and approximately 43% for meta-prompt conversions (compared to non-poetic baselines), substantially outperforming non-poetic baselines and revealing a systematic vulnerability across model families and safety training approaches. These findings demonstrate that stylistic variation alone can circumvent contemporary safety mechanisms, suggesting fundamental limitations in current alignment methods and evaluation protocols.