Execution of Harm-Enabling Actions by LLM Agents During Simulated Psychiatric Crises

Abstract Agent-enabled large language models (LLMs) can autonomously execute user-directed actions, raising new safety concerns in mental health contexts. Using a structured algorithmic safety audit with clinically validated vignettes, we examined whether an agent-enabled LLM executes harm-enabling actions during simulated psychiatric crises. The model executed such actions in over half of the trials (54%). These findings highlight execution-level risks that extend beyond conversational safety and warrant explicit safeguards for agentic systems in mental health–relevant settings.