Deep neural networks excel at solving intuitive tasks that are hard to describe formally, such as classification, but are easily deceived by maliciously crafted samples, leading to misclassification. Recently, it has been observed that the attack-specific robustness of models obtained through adversarial training does not generalize well to novel or unseen attacks. While data augmentation through mixup in the input space has been shown to improve the generalization and robustness of models, there has been limited research progress on mixup in the latent space. Furthermore, almost no research on mixup has considered the robustness of models against emerging on-manifold adversarial attacks. In this paper, we first design a latent-space data augmentation strategy called dual-mode manifold interpolation, which allows for interpolating disentangled representations of source samples in two modes: convex mixing and binary mask mixing, to synthesize semantic samples. We then propose a resilient training framework, LatentRepresentationMixup (LarepMixup), that employs mixed examples and softlabel-based cross-entropy loss to refine the boundary. Experimental investigations on diverse datasets (CIFAR-10, SVHN, ImageNet-Mixed10) demonstrate that our approach delivers competitive performance in training models that are robust to off/on-manifold adversarial example attacks compared to leading mixup training techniques.