Out-of-context (OOC) misinformation often arises when an authentic news image is paired with misleading text, making the cross-modal evidence used by a detector an important object of interpretation. We present SparseAlign-OOC, a two-level sparse autoencoder (SAE) framework for auditing NewsCLIPpings CLIP-embedding detectors through recurring image-text alignment features. A pair-space SAE maps detector inputs into sparse coordinates whose high-activation examples form human-auditable concept groups, while a hidden-state SAE traces sparse features associated with detector decisions. In a 250-item blind human audit with three annotators, selected SAE groups yield 18/50 coherent-alignment concepts, compared with 9/50 for prototype clustering, 7/50 for PCA, 4/50 for a dense autoencoder, and 0/50 for random groups. A 150-item replication audit on a stronger deep MLP preserves the same ordering, producing pooled results of 29/80 SAE alignment groups versus 15/80 for the strongest baseline. Coverage analysis shows that the presence of concepts increases with activation strength. In contrast, direct pair-space interventions show that human-labeled alignment features produce larger detector changes than topical, incoherent, or random SAE features. In the NewsCLIPpings CLIP-embedding setting, SparseAlign-OOC provides a validated audit pipeline for exposing recurring alignment factors that are both human-auditable and functionally connected to detector behavior.