Continually updated models leak when data was used: TMIA reads confidence trajectories to time sample entry and exit, with timestamp F1 above 83%
Continually updated models keep a sequence of queryable snapshots, so an attacker can infer not only whether a sample was used in training but when it entered and left. TMIA treats a sample's confidence across snapshots as a time series, labels entry and exit points with a sliding-window bidirectional LSTM, and pairs them chronologically into valid membership intervals. Across three language models and three image classifiers, TMIA reaches timestamp-level F1 above 83% in every language-model setting and improves interval-level F1 over both baselines by at least 21.82%.