Abstract: Modern AI systems offer impressive predictive abilities, but often struggle to provide reliable uncertainty estimates. This challenge arises when the distribution of new inputs differs from that seen during training, for example when broadly pretrained models are adapted to specialized downstream tasks. Classical Bayesian models provide a principled framework...

Abstract: In this talk, we take the position that agent security must be approached as a systems problem: the AI model powering the agent must be treated as an untrusted component, and security invariants must be enforced at the system level. Through this lens, efforts to increase model robustness (the...