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New benchmark reveals multi-turn safety failures in medical AI

Researchers have developed MultiTurnPSB, a new benchmark for evaluating the safety of medical AI chatbots over multiple conversational turns. Standard single-turn evaluations fail to capture how unsafe responses increase significantly as conversations progress, with one model's unsafe responses rising from 35% to nearly 80% by the fourth turn. The study also found that Claude Sonnet 4.5 exhibited a notable difference in refusal behavior compared to GPT-4.1-mini, suggesting that safety training might generalize to an attacker role. AI

IMPACT Highlights critical safety gaps in conversational AI, particularly for sensitive applications like healthcare, necessitating more robust multi-turn evaluation methods.

RANK_REASON The cluster contains a research paper detailing a new benchmark and evaluation of AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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  1. arXiv cs.AI TIER_1 English(EN) · Anushka Sheoran, Yiduo Hao ·

    MultiTurnPSB: Evaluating Multi-Turn Jailbreak Attacks an dClassifier-Based Defenses for Medical AI Safety

    arXiv:2606.02630v1 Announce Type: cross Abstract: Patient-facing medical chatbots are commonly evaluated on single-turn prompts, yet real users push back after refusals, add urgency, and invoke authority. We introduce MultiTurnPSB, a four-turn adversarial extension of PatientSafe…