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 all models was 54.4\%, with more than half exceeding 50\%, and violation rates varied substantially across behavior categories, with superficially plausible instructions such as device manipulation...
| Authors | Mahiro Nakao, Kazuhiro Takemoto |
|---|---|
| Primary category | cs.AI |
| Published | 29 April 2026 |
| Last updated at source | 24 July 2026 |
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