Benchmarking the Safety of Large Language Models for Robotic Health Attendant Control

Large language models (LLMs) are increasingly considered for deployment as the control component of robotic health attendants, yet their safety in this context remains poorly characterized. We introduce a dataset of 270 harmful instructions spanning nine prohibited behavior categories grounded in the American Medical Association Principles of Medical Ethics, and use it to evaluate 72 LLMs in a simulation environment based on the Robotic Health Attendant framework. The mean violation rate across

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ethics.ai (29 April 2026), “Benchmarking the Safety of Large Language Models for Robotic Health Attendant Control,” evidence record 5222, https://ethics.ai/record/5222 (originally published by arXiv).

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