The integration of Foundation Models (FMs), such as the Wireless Multimodal Foundation Model (WMFM), into 6G networks provides a unified framework for Integrated Sensing and Communication (ISAC), leveraging generalized representations to simultaneously optimize data transmission and environmental perception. However, the deployment of such data-driven models in safety-critical infrastructure is hindered by the Out-of-Distribution (OOD) problem, which poses a fundamental threat to system trustworthiness. Standard FMs operate under a closed-world assumption, rendering them vulnerable to silent failures when deployed in unseen radio environments. To address this reliability gap and ensure trustworthy network operation, we propose WMFM-OOD, a robust metric-based OOD detection framework. Unlike traditional methods that rely on raw compatibility scores, WMFM-OOD constructs geometric Base Station (BS) Prototypes within the joint latent space to capture the manifold structure of valid radio environments. By employing a temperature-scaled probabilistic scoring mechanism, our approach effectively distinguishes between In-Distribution (ID) and covariate-shifted anomalies. We validate the framework on the DeepVerse6G dataset. Experimental results demonstrate that WMFM-OOD significantly outperforms uncalibrated baselines, achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.8824 and reducing the False Positive Rate (FPR) at 95 % True Positive Rate (TPR), commonly referred to as FPR95, by approximately 17% in the optimal temperature regime, thereby providing an initial layer of detection sensitivity to mitigate catastrophic model failures without completely disrupting network availability.

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