Foundation models have become central to medical imaging research, yet subject re-identification implications remain unclear. In this work, we study whether optical coherence tomography (OCT)-derived B-scan features extracted using frozen generalist and specialist foundation models allow re-identification of subjects intra- and cross-device. A lightweight binary classifier was trained to predict whether two feature sets originate from the same individual. Results show that specialist models such as RETFound reach 78% re-identification accuracy (Rank-1) on high-resolution OCT data, while generalist models perform only slightly worse. Performance decreased substantially on the lower-resolution data and was near chance across devices. These findings suggest that general foundation models extract subject-related information, potentially entangled with recording device-related information.