Standard safety filters primarily utilize pattern and keyword detection to block harmful content generation. However, these filters struggle with requests that employ metaphor, fragmented syntax, or poetic rhythms, often failing to identify dangerous intent. The study highlights that poetic structures utilize “low-probability word sequences” and unpredictable syntax, resembling creative writing rather than prompts that violate policies. This unpredictability leads AI models to interpret such requests as narrative or abstract content, effectively disabling refusal logic. Consequently, this reveals a significant structural vulnerability within existing AI safety frameworks, emphasizing the need for improved mechanisms to detect and mitigate risks associated with creatively framed harmful content. Addressing this gap is essential for enhancing AI safety and ensuring the responsible use of AI technologies.
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