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New benchmark MPIB assesses LLM clinical safety against prompt injection attacks

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced the Medical Prompt Injection Benchmark (MPIB), a new dataset and evaluation suite designed to assess the clinical safety of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. MPIB focuses on identifying risks associated with prompt injection attacks, both direct and indirect, within clinical contexts. The benchmark utilizes the Clinical Harm Event Rate (CHER) to measure severe clinical harm and distinguishes between moderate and high-severity outcomes, revealing significant divergences between different LLMs and defense strategies. AI

IMPACT This benchmark will enable better evaluation of LLM safety in clinical settings, potentially leading to more secure AI integration in healthcare.

RANK_REASON The cluster describes a new academic benchmark and dataset for evaluating LLM safety, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark MPIB assesses LLM clinical safety against prompt injection attacks

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The cluster describes a new academic benchmark and dataset for evaluating LLM safety, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Junhyeok Lee, Han Jang, Kyu Sung Choi ·

    MPIB: A Benchmark for Medical Prompt Injection Attacks and Clinical Safety in LLMs

    arXiv:2602.06268v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems are increasingly integrated into clinical workflows. However, prompt injection attacks can steer these systems toward clinically unsafe or misle…

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