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Modern machine learning models, that excel on computer vision tasks such as classification and object detection, are often overconfident in their predictions for Out-of-Distribution (OOD) examples, resulting in unpredictable behaviour for open-set environments. Recent works have demonstrated that the free energy score is an effective measure of uncertainty for OOD detection given its close relationship to the data distribution. However, despite that free energy-based methods representing a significant empirical advance in OOD detection, our theoretical analysis reveals previously unexplored and inherent vulnerabilities within the free energy score formulation such that in-distribution and OOD instances can have distinct feature representations yet identical free energy scores. This phenomenon occurs when the vector representing the feature space difference between the in-distribution and OOD sample belongs to the null space of the last layer of a neural-based classifier. To mitigate these issues, we explore lower-dimensional feature spaces to reduce the null space footprint and introduce novel regularisation to maximize the least singular value of the final linear layer, hence enhancing inter-sample free energy separation. We refer to these techniques as Free Energy Vulnerability Elimination for Robust Out-of-Distribution Detection (FEVER-OOD). Extensive experimentation shows that FEVER-OOD improves OOD detection in CIFAR-10 with 28.22% FPR95 and 94.78 AUROC vs 33.66% FPR95 and 92.15 AUROC of the baseline model, CIFAR-100 with 42.77% FPR95 and 89.98 AUROC vs 50.96% FPR95 and 88.06 AUROC of the baseline model, and achieves state of the art OOD detection in Imagenet-100, with average OOD FPR95 of 36.50% and an AUROC of 92.74 when used with the Dream-OOD model, compared with a 39.33% and 91.84 AUROC without FEVER-OOD.