AI RESEARCH
OutSafe-Bench: A Benchmark for Multimodal Offensive Content Detection in Large Language Models
arXiv CS.LG
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ArXi:2511.10287v4 Announce Type: replace Since Multimodal Large Language Models (MLLMs) are increasingly being integrated into everyday tools and intelligent agents, growing concerns have arisen regarding their possible output of unsafe contents, ranging from toxic language and biased imagery to privacy violations and harmful misinformation. Current safety benchmarks remain highly limited in both modality coverage and performance evaluations, often neglecting the extensive landscape of content safety. In this work, we.