Evolving Safety Landscape of Multi-modal Large Language Models: A Survey of Emerging Threats and Safeguards
A deep dive into the shifting threat landscape and evolving defense strategies for integrated multimodal AI systems.
Multi-modal large language models (MLLMs) integrate heterogeneous modalities through modality alignment and fusion, enabling stronger understanding and reasoning. However, this architectural shift reshapes the safety landscape of machine learning. Increased model complexity and c...
ArXiv CS.LG
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As multimodal models integrate diverse data streams, they introduce complex attack vectors that traditional, unimodal safety protocols fail to address. This survey establishes a necessary framework for researchers to defend against emerging cross-modal vulnerabilities in sophisticated AI architectures.
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