Adversarial training may teach models to hide, not behave
A model can keep producing harmful outputs while reshaping its internal activations to look harmless, and the usual fix may create a deeper problem: training against the monitor itself. This research writeup examines Obfuscated Adversarial Training, tracing whether the attack is a brittle trick or a learned internal strategy. The stakes are practical: latent-space monitors that report near-perfect detection in testing can collapse under optimization pressure.