Machine-learning triage for memory forensics is usually validated on corpora whose benign class is asserted by provenance and never verified. We report a validation study of a graph-based triage pipeline for Windows memory images: Volatility 3 output is compiled into typed heterogeneous graphs and scored by a temperature-scaled GINE classifier and two one-class encoders, fused with rule risk under an abstention gate. On MalVol-25 a transparent rule layer contradicts the benign label on every control (15/15 and 18/18 flagged; at least 11 of 15 paired controls rated HIGH or CRITICAL), and mean injection and command-and-control counts run higher on controls than on infected captures. We call this rule-label discordance label-signal mismatch; it shows the benign class is unverified, not compromised. Leave-one-family-out AUROC over matched pairs is provably three-valued, so a reported mean and standard deviation identify the fold composition. The learned model (0.786 +- 0.311; 9/4/1, scored in-sample by a frozen checkpoint) equals a logistic model over rule-derived attributes (0.786 +- 0.410; 11/0/3) in mean only, and its significance rests on dropping four saturated ties. The fusion path sends 97.7% of snapshots to analyst review, 95.3% with every gate disabled. Two public feature-level corpora show the same defect: on CIC-MalMem-2022 every benign row carries injected regions and one host-population feature reaches AUROC 0.997 under leave-one-family-out validation; on the Sihwail et al. (2021) sample the injection indicator fires on 53% of benign rows. We close with seven reporting artefacts for learned triage on corpora whose ground truth is asserted.

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