arXiv Machine Learning
MPIB: A Benchmark for Medical Prompt Injection Attacks and Clinical Safety in LLMs
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HarmReduction: Benchmarking LLMs in Harm Reduction Information Provision to Support People Who Use Drugs
The paper introduces HarmReduction, a benchmark for evaluating large language models (LLMs) on their ability to provide accurate and safe harm reduction information to people who use drugs (PWUD). The benchmark, HR-Basic, contains 2,160 question‑answer‑evidence pairs covering safety boundary checks, quantitative value provision, and polysubstance risk inference. Experiments show that even state‑of‑the‑art LLMs struggle with accuracy and can pose severe safety risks, underscoring the need for a dedicated evaluation framework.
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When Medical Safety Alignment Fails: A Benchmark for Evaluating LLMs on High-Risk Medical Queries
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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming
arXiv:2508. 00923v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to answer health-related questions and support healthcare workflows, yet evidence for their safety still relies heavily on static benchmarks that can rapidly become obsolete or be optimized against.
arXiv AI
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arXiv AI
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Health-ORSC-Bench: A Benchmark for Measuring Over-Refusal and Safety Completion in Health Context
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arXiv AI
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First, do NOHARM: towards clinically safe large language models
arXiv:2512. 01241v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized.