Tutorial: Safety, Security, and Privacy of Foundation Models

The advent of foundation models, such as GPT4 and CLIP, has revolutionized the field of Artificial Intelligence (AI), enabling unprecedented advancements across various domains, including Natural Language Processing (NLP), Computer Vision (CV) tasks, and so on. However, alongside these opportunities come significant challenges related to these foundation models’ safety, security, and privacy. For instance, it has been proven that foundation models are vulnerable to various malicious attacks, compromising their integrity, confidentiality, and availability. These vulnerabilities of the foundational models hinder their practical deployment, especially in security-sensitive scenarios. This tutorial aims to provide a comprehensive overview of the current landscape, focusing on the risks, mitigation strategies, and best practices for ensuring the responsible deployment of foundation models. Participants will gain insights into the potential vulnerabilities of the foundation models, including but not limited to adversarial attacks, data privacy issues, and ethical considerations. Specifically, the tutorial will cover state-of-the-art techniques for securing foundation models, methods for ensuring privacy-preserving data usage, and guidelines for promoting safe AI practices. Moreover, the tutorial will delve into the latest advancements in defense methods, exploring how to detect and mitigate existing attacks effectively. The ethical considerations segment will highlight frameworks and guidelines for developing and deploying AI systems that adhere to ethical standards, addressing bias, fairness, and transparency. Through theoretical discussions and practical demonstrations, attendees will be equipped with the knowledge and tools necessary to address the critical challenges associated with foundation models in their work. Above all, our tutorial will help participants implement robust, secure, and privacy-preserving AI systems in diverse application areas.