Decodable but Not Detectable: A Leakage Fingerprint for Near-OOD Benchmarks
While auditing a perturbation-based OOD detector on a document benchmark, we recorded an AUROC of 0.326 -- well below the 0.5 chance level. The cause is a benchmark leak: the designated "OOD" class is one the model was trained on, so its examples sit inside the in-distribution fit set and the detector is penalized for correctly ranking them as familiar. Deleting the class and retraining 35 models across two domains raises the score to 0.911. We distill the contamination into a leak fingerprint -- near-perfect supervised decodability (AUROC approximately 1) coupled with unsupervised detection collapsed below 0.65 -- and validate it on a controlled battery of 52 settings (20 leaked, 32 clea...
| Authors | Vishnu Bindu Balachandran |
|---|---|
| Primary category | cs.LG |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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