Detecting Hidden Conformal Failures under Feature Shift

Split conformal prediction provides marginal coverage under exchangeability, yet deployment-time feature shift can silently invalidate this guarantee even when point accuracy barely changes. We study label-free detection of such hidden coverage failures. Soft Conformal Boundary Sensitivity (SCBS) profiles how source-side feature interventions move examples across the conformal boundary and combines this vulnerability with null-calibrated unlabeled target drift. Across 57,600 controlled scenarios, SCBS detects five-point hidden coverage losses with dataset-macro AUROC 0.846. Direction-aware source simulation and an output-based monitor expose complementary mechanisms, so we select between them using a source-only router. Frozen on an ordered Bank Marketing audit, the router obtains AUROC/AUPRC $0.974\pm 0.037$/$0.997\pm 0.003$; across three natural hidden-failure streams it obtains $0.965\pm 0.025$/$0.894\pm 0.035$ with FPR 0.146. Strong generic alternatives do not isolate harmful drift: classifier two-sample testing and linear-time MMD obtain AUROC 0.582 and 0.746 and alarm on nearly every benign shifted window. An impossibility result formalizes why unlabeled covariates cannot universally certify target coverage, and a finite-label extension restores design-based identification. The result is a mechanism-aware deployment warning, not an unconditional coverage guarantee.