Sample-Level White-Box Detection of Alignment Faking

Models can fake alignment (AF): produce aligned outputs that conceal misalignment when they infer they are being monitored. We treat sample-level white-box AF detection as a distinct problem and present the first study of it. We introduce a per-rollout labeling procedure separating AF from plain lying and situational awareness. Across four datasets and four models in a shared lineage, existing lie-detection probes transfer poorly to AF, while probes trained on our new controlled synthetic AF dataset gain +12% AUROC and +15% AUPRC. Probe directions also drift substantially across training stages, with cross-model transfer significantly degrading performance. Together, we show preliminary evidence that sample-level white-box AF detection is feasible.