MLingualFC: Evaluating Jailbreak Vulnerabilities in Multilingual Vision-Language Models
Researchers have developed MLingualFC, a new multilingual benchmark to test the safety vulnerabilities of vision-language models (VLMs). This benchmark uses flowchart images encoded with harmful instructions in five languages: Hindi, Punjabi, Spanish, Romanian, and German. Evaluations of models like Qwen2.5-VL, Gemma-4, and Pangea revealed that visual attacks are highly successful in Latin-script languages, indicating current safety measures do not generalize well across languages and modalities. AI
IMPACT Highlights the need for more robust, multilingual safety alignment in advanced AI models.