MLingualFC: Evaluating Jailbreak Vulnerabilities in Multilingual Vision-Language Models 事件

MLingualFC: Evaluating Jailbreak Vulnerabilities in Multilingual Vision-Language Models arXiv:2606.07706v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated strong performance across multimodal tasks, yet their safety robustness remains an open challenge. While prior work has shown that structured visual prompts such as flowcharts can effectively jailbreak VLMs, existing studies are largely limited to English-centric settings. In this paper, we introduce MLingualFC,