The rapid evolution of multimodal foundation models, capable of processing and generating language, images, video, and audio, has transformed numerous fields, including robotics, healthcare, and AI-driven media. However, these advancements bring forth significant challenges related to reliability, security, and societal impact. Instances of model hallucinations and the inadvertent generation of harmful content by Text-to-Image (T2I) models underscore the need for responsible and sustainable development practices.Our workshop aims to address these critical issues by establishing design principles that prioritize precautionary measures over reactive solutions. We will explore methodologies to enhance the reliability and robustness of multimodal models, focusing on fairness, security, and the mitigation of misinformation. By emphasizing preemptive strategies during dataset curation and model pre-training, we aim to reduce the extensive resource demands traditionally associated with iterative refinement processes.Key topics of discussion will include the identification of reliability concerns stemming from data quality, model architecture, and training strategies. Additionally, we will explore novel design principles that ensure the responsible and sustainable advancement of multimodal generative models. Our goal is to foster a collaborative environment where leading researchers and practitioners can develop actionable frameworks that align with ethical standards and maximize societal benefits.Through keynote talks, panel discussions, and interactive sessions, this workshop will provide a comprehensive platform for the AI community to converge on best practices for building the next generation of multimodal foundation models. We seek to ensure these models are not only technologically advanced but also secure, equitable, and environmentally sustainable.