Foundation models have come to underpin many critical applications, such as healthcare, public safety, and education. Ensuring their trustworthiness is, therefore, more important than ever. However, recent research has revealed that foundation models are prone to memorizing details or even entire samples from their training data. This issue can lead to privacy violations, intellectual property infringement, and societal harm when sensitive information is leaked. While unintended memorization risks the integrity of models, a certain degree of it is essential for solving novel and complex tasks, highlighting the importance of balancing performance with data leakage. Currently, isolated solutions are being developed across various research fields and data modalities, often without integration or coordination. This fragmentation can lead to duplicated efforts despite shared goals. The lack of interaction and exchange between research fields hinders progress in understanding and mitigating undesired memorization. In this workshop, we explore the causes and consequences of memorization from both theoretical and practical perspectives. We aim to connect insights from different research fields, including data privacy, ethics, and security in machine learning, to assess their impact on models and society and to explore innovative methods for mitigating associated risks. By bringing together researchers and practitioners from diverse fields, we seek to bridge the gap between research and real-world applications, fostering the development of trustworthy foundation models that benefit society without compromising sensitive data, intellectual property, or individual privacy.