@inproceedings{lou-etal-2026-helpers,
    title = "When Helpers Become Hazards: A Benchmark for Analyzing Multimodal {LLM}-Powered Safety in Daily Life",
    author = "Lou, Xinyue  and
      Jinan, Xu  and
      Yin, Jingyi  and
      Wang, Xiaolong  and
      Kang, Zhaolu  and
      Liaoyouwei  and
      Wang, Yixuan  and
      Shi, Xiangyu  and
      Mo, Fengran  and
      Yao, SU  and
      Huang, Kaiyu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1446/",
    pages = "28937--28963",
    ISBN = "979-8-89176-395-1",
    abstract = "As Multimodal Large Language Models{~}(MLLMs) become an indispensable assistant in human life, the unsafe content generated by MLLMs poses a danger to human behavior, perpetually overhanging human society like a sword of Damocles. To investigate and evaluate the safety impact of MLLMs responses on human behavior in daily life, we introduce SaLAD, a multimodal satety benchmark which contains 2,013 real-world image{--}text samples across 10 common categories, with a balanced design covering both unsafe scenarios and cases of oversensitivity. It emphasizes realistic risk exposure, authentic visual inputs, and fine-grained cross-modal reasoning, ensuring that safety risks cannot be inferred from text alone. We further propose a safety-warning-based evaluation framework that encourages models to provide clear and informative safety warnings, rather than generic refusals. Results on 18 MLLMs demonstrate that the top-performing models achieve a safe response rate of only 57.2{\%} on unsafe queries. Morevoer, even popular safety alignment methods limit effectiveness of the models in our scenario, revealing the vulnerabilities of current MLLMs in identifying dangerous behaviors in daily life. Our dataset is available at \url{https://github.com/xinyuelou/SaLAD}."
}