arXiv:2609.28517v2 Announce Type: replace Abstract: Post-hoc malware calibrators can condition confidence on graph structure, but their structural inputs may leave the support represented by validation data under malware-type shift. We study this risk on MalNet-Tiny by holding out each of four malware types across three seeds, freezing a graph isomorphism network, and fitting post-hoc mappings only on known-type data. On known-type tests, generic and combined structural calibrators have negative log likelihood (NLL) of 0.4341 and 0.4628. Their mean held-out-type gap widens to 0.1369, with a four-type 95% interval of [-0.0125, 0.2864] and improvement in one of four types. Eight label-independent nuisance variables produce a smaller mean NLL increase of 0.0415, but the paired combined-versus-nuisance interval includes zero. We then use calibration data to define a community-support guard: predictions outside the 95th percentile of calibration-standardized community displacement revert to the generic calibrator. The guard reduces combined NLL by 0.1133 and high-confidence errors from 43.25 to 33.25 per 400-sample cell. It also outperforms matched-rate random fallback on the original folds, while generic and uncalibrated probabilities retain lower held-out-type NLL. This exploratory recovery localizes harmful combined predictions; the stress test exposes heterogeneous calibration failures without establishing a general structural-calibration penalty or a deployment gain over simpler alternatives.