When Medical Safety Alignment Fails: A Benchmark for Evaluating LLMs on High-Risk Medical Queries
arXiv:2606.28332v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for medical and health-related questions, yet their safety in high-risk medical scenarios remains poorly understood. We introduce \textsc{MedHarm}\footnote{Code and data will be released upon acceptance. Due to the sensitive nature of high-risk medical queries, data access will be available to qualified researchers upon request.}, a high-risk medical safety benchmark with 1,100 medically grounded queries across 10 safety-critical categories, including toxicology, pharmacology, covert poisoning,
The increasing deployment of LLMs in sensitive domains like healthcare necessitates robust safety evaluations as adoption accelerates.
This benchmark highlights critical safety gaps in current LLM capabilities for high-risk medical scenarios, forcing developers to prioritize rigorous alignment and validation.
The focus shifts towards developing more sophisticated safety protocols and benchmarks for LLMs, especially in regulated and high-stakes applications like medicine.
- · AI safety researchers
- · Healthcare regulatory bodies
- · Patients
- · LLM developers prioritizing safety
- · LLM developers with inadequate safety measures
- · Early adopters of unverified medical LLM applications
Introduction of specific safety benchmarks for medical LLMs will drive focused research into failure modes and mitigation strategies.
Increased scrutiny and potential regulatory frameworks for LLM deployment in healthcare will emerge, impacting market access and development cycles.
The benchmark could become a de-facto standard for medical AI certification, leading to a 'safety race' among LLM providers to achieve compliance.
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Read at arXiv cs.AI