Graph Neural Networks (GNNs) have demonstrated remarkable success across diverse fields, yet remain susceptible to subtle adversarial perturbations that significantly degrade performance. Addressing this vulnerability remains a formidable challenge. Current defense strategies focus on edge-specific regularization within adversarial graphs, often overlooking the inter-edge structural dependencies and the interplay of various robustness attributes. This paper introduces a novel tensor-based framework for GNNs, aimed at reinforcing graph robustness against adversarial influences. By employing tensor approximation, our method systematically aggregates and compresses diverse predefined robustness features of adversarial graphs into a low-rank representation. This approach harmoniously combines the integrity of graph structure and robustness characteristics. Comprehensive experiments on real-world graph datasets demonstrate that our framework not only effectively counters diverse types of adversarial attacks but also surpasses existing leading defense mechanisms in performance.