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State-of-the-art image-to-video models are alarmingly vulnerable to visual prompt attacks, achieving up to 100% success in triggering harmful outputs.
Advanced RS MLLMs struggle with negation, but a novel learning method can dramatically enhance their understanding using minimal unlabeled data.
