A clinically validated framework for auditing AI chatbot behavior in mental health interactions

Abstract

Millions of users turn to consumer artificial intelligence chatbots to discuss emotional, behavioral and mental-health concerns, creating an urgent need for rigorous and scalable safety evaluations. Here we introduce simulated (SIM) vulnerability-amplifying interaction loops (VAILs) (SIM-VAIL), a clinically validated framework for auditing chatbot behavior in mental-health contexts. SIM-VAIL simulates users with specific psychiatric vulnerabilities and conversational intents, engages them in multi-turn conversations with frontier artificial intelligence chatbots (including Claude, ChatGPT, Gemini, Grok and Llama models) and scores each exchange across 13 clinically grounded risk dimensions. Across 810 conversations, spanning 9 target chatbots and 30 simulated user profiles, concerning behavior in target chatbots was widespread, albeit reduced in newer models. Concerning behavior varied by user vulnerability and conversational intent, accumulated over turns, and could be reduced by interventions at early escalation points. Risk was highest when otherwise supportive chatbot behaviors reinforced the psychological mechanisms underlying the simulated user’s vulnerability, a pattern we term a VAIL. SIM-VAIL provides a scalable framework for mapping mental-health risk across users, chatbots and conversational trajectories, offering a foundation for targeted safety improvements.