When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Life (2026.findings-acl)
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Xinyue Lou, Xu Jinan, Jingyi Yin, Xiaolong Wang, Zhaolu Kang, null Liaoyouwei, Yixuan Wang, Xiangyu Shi, Fengran Mo, SU Yao, Kaiyu Huang
| Challenge: | Safety impact of Multimodal Large Language Models (MLLMs) on human behavior is evaluated in this study. |
| Approach: | They propose a safety-warning-based evaluation framework that encourages models to provide clear and informative safety warnings, rather than generic refusals. |
| Outcome: | The proposed safety-warning-based evaluation framework encourages models to provide clear and informative safety warnings, rather than generic refusals. |
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