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This paper proposes output-aware safety guardrails for multimodal large language models that use hidden state representations and multi-instance contrastive learning to predict unsafe outputs before generation, drastically reducing over-refusal while maintaining safety. The method preserves the model's utility by intervening only when the actual response would be harmful.