Advances in machine learning have enabled new applications that border on science fiction. AI-driven text synthesis, autonomous cars, data analytics, and self-aware software systems are now revolutionizing markets by achieving or exceeding human performance. In this talk, I discuss the rapidly evolving use of machine learning—from simple models to massive foundational models—in security-sensitive contexts and explore why many systems are vulnerable to nonobvious and potentially dangerous manipulation. We will examine sensitivity in applications where misuse might lead to harm—for instance, forcing adaptive networks into an unstable state, crashing an autonomous vehicle or bypassing an adult content filter. I explore how currently accepted wisdom about threats and defenses should be viewed (and sometimes refuted) in light of the functional and security challenges of real-world systems. The talk concludes with a discussion of the technological, economic and societal challenges we face as a result of the rise of machine learning as a fundamental construct driving technology.
Patrick McDaniel is the Tsun-Ming Shih Professor of Computer Sciences in the School of Computer, Data & Information Sciences at the University of Wisconsin-Madison. Professor McDaniel is a Fellow of IEEE, ACM and AAAS, a recipient of the SIGOPS Hall of Fame Award and SIGSAC Outstanding Innovation Award, and the director of the NSF Frontier Center for Trustworthy Machine Learning. He also served as the program manager and lead scientist for the Army Research Laboratory's Cyber-Security Collaborative Research Alliance from 2013 to 2018. Patrick's research focuses on a wide range of topics in computer and network security and technical public policy. Prior to joining Wisconsin in 2022, he was the William L. Weiss Professor of Information and Communications Technology and Director of the Institute for Networking and Security Research at Pennsylvania State University.