MultiTurnPSB: Evaluating Multi-Turn Jailbreak Attacks an dClassifier-Based Defenses for Medical AI Safety
📰 ArXiv cs.AI
Learn to evaluate multi-turn jailbreak attacks on medical AI chatbots and develop classifier-based defenses to improve safety
Action Steps
- Build a multi-turn adversarial testbed using MultiTurnPSB to evaluate medical AI chatbots
- Run fixed template, template-adaptive, and live adversarial attacks on GPT-4.1-mini to assess vulnerability
- Configure classifier-based defenses to mitigate unsafe responses
- Test the effectiveness of these defenses under multi-turn attacks
- Apply the findings to improve the safety of patient-facing medical chatbots
Who Needs to Know This
AI researchers and developers working on medical chatbots can benefit from this knowledge to improve the safety and robustness of their systems
Key Insight
💡 Multi-turn attacks can increase unsafe responses in medical AI chatbots by up to 45%
Share This
🚨 New study: MultiTurnPSB evaluates multi-turn jailbreak attacks on medical AI chatbots, revealing a significant rise in unsafe responses 🚨
Full Article
Title: MultiTurnPSB: Evaluating Multi-Turn Jailbreak Attacks an dClassifier-Based Defenses for Medical AI Safety
Abstract:
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 PatientSafetyBench, and evaluate GPT-4.1-mini under fixed template, template-adaptive, and live adversarial attacks. Unsafe responses rise from 35% to nearly 80% by Turn 4 under live attack. Under the same adversary, GPT-4.1-mini a
Abstract:
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 PatientSafetyBench, and evaluate GPT-4.1-mini under fixed template, template-adaptive, and live adversarial attacks. Unsafe responses rise from 35% to nearly 80% by Turn 4 under live attack. Under the same adversary, GPT-4.1-mini a
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