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arXiv · 2609.20850

MME-Safety: A Fine-grained Benchmark for Safety Evaluation of MLLMs

Yilian Shi·Yueming Lyu·Haoxiang Tan·Linzhuang Zou·Qihao Wang·Guihua Yu·Chenyang Si·Caifeng Shan
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Abstract

While Multimodal Large Language Models (MLLMs) show remarkable advancements, their cross-modal capabilities introduce complex vulnerabilities that easily bypass unimodal filters. Existing benchmarks lack fine-grained intent-related annotations and rely on unidimensional metrics, hindering comprehensive robustness evaluation. To address this, we propose MME-Safety, a rigorously verified benchmark featuring a unique four-dimensional annotation schema that categorizes risk scenarios, harm severity, and modality-specific stealth levels. Furthermore, we introduce a hierarchical evaluation framework to assess fundamental response reliability, actual risk exposure, and the structural integrity of defensive behaviors. Extensive zero-shot evaluations across 17 state-of-the-art MLLMs provide a comprehensive safety profile of current multimodal systems. Our analysis systematically investigates cross-modal input configurations and uncovers safety implications associated with Chain-of-Thought (CoT) reasoning. These multifaceted findings underscore the urgent need for robust, reasoning-aware safety alignment in the multimodal landscape.

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Yilian Shi, Yueming Lyu, Haoxiang Tan, Linzhuang Zou, Qihao Wang, Guihua Yu, Chenyang Si, Caifeng Shan. 2026-09-22. MME-Safety: A Fine-grained Benchmark for Safety Evaluation of MLLMs. https://arxiv.org/abs/2609.20850

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