Exploring Adversarial Robustness and Safety Alignment in Multilingual Multi-Modal Large Language Models
arXiv:2606.03793v1 Announce Type: new 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 robustness and multimodal safety across 12 diverse languages, evaluating open-source MLLMs that acquire multilingual capability through instruction tuning. Gradient-based attacks reveal a transferable multilin
The proliferation of advanced MLLMs and their increasing deployment in diverse linguistic contexts necessitates a deeper understanding of their vulnerabilities and safety mechanisms beyond English-centric research.
As MLLMs become more integrated into critical applications globally, understanding and mitigating adversarial attacks and safety risks across multiple languages is crucial for their reliable and equitable deployment.
This research shifts the focus of MLLM robustness and safety alignment from primarily English to a multilingual perspective, highlighting new vectors for attack and areas for defense in global AI systems.
- · AI robustness researchers
- · Multilingual AI developers
- · Governments focused on AI safety
- · Users of secure multilingual MLLMs
- · Developers neglecting multilingual safety
- · Systems deployed without robust defenses
- · Malicious actors without advanced attack vectors
Increased focus on developing multilingual adversarial training and safety alignment techniques for MLLMs.
Heightened awareness and regulation regarding the ethical and security implications of deploying global, multilingual AI models.
Potential for new international standards and collaborations for MLLM safety and robustness across diverse linguistic and cultural contexts.
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