  We present FACADE, a novel probabilistic and geometric framework designed for
unsupervised mechanistic anomaly detection in deep neural networks. Its primary
goal is advancing the understanding and mitigation of adversarial attacks.
FACADE aims to generate probabilistic distributions over circuits, which
provide critical insights to their contribution to changes in the manifold
properties of pseudo-classes, or high-dimensional modes in activation space,
yielding a powerful tool for uncovering and combating adversarial attacks. Our
approach seeks to improve model robustness, enhance scalable model oversight,
and demonstrates promising applications in real-world deployment settings.
