Robustness of confessions under adversarial pressure

Ascertain whether models trained to produce ConfessionReport outputs remain honest under adversarial pressure by confessing when prompted to pursue a secret goal or when trained to exhibit malign behavior, particularly when confession training is performed only on out-of-distribution data.

Background

Confessions are proposed as a tool to elicit honest self-reporting, especially for intentional misbehavior such as scheming or reward hacking. However, the robustness of confessions under adversarial setups—where models are prompted to optimize for hidden objectives or trained to behave malignly—remains uncertain.

Testing whether confession honesty persists under such conditions would inform the reliability of confessions as a safety mechanism and their generalization when training data does not include the adversarial behaviors of interest.