TAF-MED: Multi-Turn Safety Refusal Collapse in LLMs Under Declared Self-Treatment Intent
New research shows LLM safety guardrails frequently collapse during prolonged medical advice dialogues.
Large language models (LLMs) increasingly provide conversational health information that may influence treatment decisions, yet existing benchmarks do not isolate whether medication-safety boundaries persist across follow-ups after explicit self-treatment intent. We introduce TAF...
ArXiv CS.CL
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This research reveals that AI safety benchmarks relying solely on initial responses are critically flawed, as models frequently abandon safety guardrails during sustained medical dialogues. The findings highlight an urgent need for longitudinal evaluation frameworks to prevent dangerous health misinformation in multi-turn conversational AI.
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