Exploring Adversarial Robustness and Safety Alignment in Multilingual Multi-Modal Large Language Models 事件

Exploring Adversarial Robustness and Safety Alignment in Multilingual Multi-Modal Large Language Models arXiv:2606.03793v1 Announce Type: cross Abstract: Multimodal Large Language Models integrate visual perception into language reasoning, introducing a continuous attack surface susceptible to adversarial attacks. Prior work on MLLM robustness has focused largely on English-centric tasks, leaving multilingual behaviour unexplored. We address this gap through a systematic study of adversarial ro