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, a multilingual multimodal benchmark designed to evaluate jailbreak vulnerabilities of VLMs across diverse languages using structured flowchart representations. MLingualFC encodes harmful instructions into flowchart images across five languages (Hindi, Punjabi, Spanish, Romanian, and German). We evaluate state-of-the-art multilingual VLMs, including Qwen2.5-VL, Gemma-4, and Pangea, under a black-box threat model. Our results reveal significant multilingual safety gaps. Flowchart-based attacks achieve high attack success rates (ASR) in case of Latin script languages, demonstrating that visual encoding of harmful content effectively bypasses safety alignment across languages. In contrast, non-Latin script languages such as Punjabi exhibit substantially lower ASR, suggesting potential limitations in visual text recognition rather than stronger safety alignment. These findings highlight that current VLM safety mechanisms fail to generalize across languages and modalities. Resources are available at https://github.com/Rishabhpm23/MLingualFC

翻译：视觉语言模型（VLM）在多模态任务中展现了强大的性能，但其安全鲁棒性仍是一个开放挑战。尽管先前研究表明，流程图等结构化视觉提示能有效越狱VLM，但现有研究大多局限于英语中心场景。本文提出MLingualFC——一个多语言多模态基准，旨在通过结构化流程图表示评估VLM在不同语言中的越狱脆弱性。MLingualFC将有害指令编码为五种语言（印地语、旁遮普语、西班牙语、罗马尼亚语和德语）的流程图图像。我们在黑盒威胁模型下评估了包括Qwen2.5-VL、Gemma-4和Pangea在内的最先进多语言VLM。结果揭示了显著的多语言安全差距：基于流程图的攻击在拉丁字母语言中实现了高攻击成功率（ASR），表明有害内容的视觉编码有效绕过了跨语言的安全对齐；相反，旁遮普语等非拉丁字母语言的ASR显著较低，这暗示其限制可能源于视觉文字识别能力不足，而非更强的安全对齐。这些发现表明，当前VLM的安全机制无法跨语言和模态泛化。相关资源见https://github.com/Rishabhpm23/MLingualFC