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This presentation explores LAMP, a novel approach to multimodal language models (MLLMs), addressing challenges in adversarial robustness. It highlights key contributions, including innovative techniques for universal perturbations, validated by experiments and in-depth analysis. The session concludes with a summary of advancements, broader impacts, and future research pathways, emphasizing its significance for the MLLM field.