Current capability evaluation methods cannot guarantee detection of hidden model behaviors—adversaries can cryptographically hide arbitrary computations that only activate on secret triggers, fundamentally limiting pre-deployment safety audits.
Researchers demonstrate that AI models can hide arbitrary capabilities using encrypted neural networks embedded as backdoors, which remain undetectable even with full access to model weights. This challenges the assumption that capability evaluations can reliably surface dangerous behaviors before deployment.