Audio deepfake detectors perform unevenly across synthesis systems. Can frozen speech representations expose structure behind this variation? We construct a simple direction between genuine and synthetic WavLM embeddings and characterize each system’s position and spread along it. Across 61 MLAAD text-to-speech system–language groups, projected spread is strongly associated with mean detector difficulty across three runs (𝜌=0.60). It is the only feature surviving correction over 83 recorded tests, and leave-one-system-out ridge prediction gives R²=0.277. The genuine–synthetic directions also differ markedly across corpora, revealing domain-dependent representation geometry. As an auxiliary utterance score, the centroid projection reduces MLAAD detector equal error rate from 27.2% to 16.3%, improving on both component scores. Supervised probe comparisons further identify where adaptation gains originate. Together, these results establish a compact representation-space diagnostic of attack-specific detector behavior; the informative statistic and benefits of score adaptation depend on the corpus and detector.