Pathology foundation models encode non-biological variation introduced by tissue preparation, staining and scanning, enabling shortcut learning that undermines generalisation across institutions. The Robustness Index (RI} was proposed to assess whether local representation geometry is dominated by biological or non-biological variation. However, its construction suffers from structural limitations that make cross-model comparison unreliable and call for a more principled metric. We introduce the Cross-confounder Robustness Margin (CRoMa), which measures, for each sample, whether biologically matched samples differing in the confounder lie closer in representation space than confounder-matched samples differing in biology. By design, CRoMa recasts robustness as a cohort-wide distribution of sample-level margins. We evaluated frozen representations from 20 tile-level encoders across three benchmarks and 4 slide-level encoders on a fourth. Median CRoMa rankings were broadly consistent across cohorts, yet all encoders contained confounder-dominated subsets whose prevalence and severity varied substantially across models and cohorts. Higher CRoMa margins were associated with smaller shortcut-induced performance drops after supervised adaptation, indicating that CRoMa can be a valuable tool for assessing model robustness on downstream tasks.