Ancestral Membership Auditing: Tracing Source Use Through Synthetic Descendants

Synthetic training data can replace original records while retaining information about the sources that generated them. We study ancestral membership auditing: determining whether synthetic descendants of a known source were used for downstream fine-tuning when the source itself is excluded from training and the actual descendants remain hidden. We identify the Sibling Transfer Effect (STE), where independently regenerated examples from member sources exhibit more favorable average changes in answer likelihood than those from nonmember sources. We audit this effect using source-grounded question-answer probes and source-level likelihood readouts with base or reference calibration. Across eight model families on Enron and BillSum, five-reference calibration achieves mean AUCs of 0.9240 and 0.9477 with matched generators and 0.8328 and 0.8504 under generator mismatch. Removing exact matches to training records preserves detectability, and the strongest signal occurs among non-exact siblings sharing source evidence. STE can also appear through relative retention, where member probes deteriorate less even when their absolute likelihood decreases. These results show that synthetic transformation can change training records without necessarily erasing an auditable footprint of source participation.