Towards Safe Multi-Modal Learning: Evolving Threats and Safety Solutions

Multi-modal learning has enabled powerful systems that combine text, images, audio, and video for perception, reasoning, and decision-making. At the same time, it has introduced safety challenges that differ fundamentally from those in traditional uni-modal learning. This tutorial presents a structured overview of the evolving safety landscape in multi-modal AI, focusing on emerging threat models and corresponding defense strategies. It examines key risks such as compromised modality integration, modality misalignment, and fused cross-modal vulnerabilities, and reviews recent work on adversarial attacks, jailbreaks, hallucinations, and safety solutions for more reliable multi-modal systems.