This paper investigates the effect of adversarial perturbations on the hyperbolicity of graphs. Learning low-dimensional embeddings of graph data in certain curved Riemannian manifolds has recently gained traction due to their desirable property of acting as useful geometrical inductive biases. More specifically, models of Hyperbolic geometry such as Poincar\'{e} Ball and Hyperboloid Model have found extensive applications for learning representations of discrete data such as Graphs and Trees with hierarchical anatomy. The hyperbolicity concept indicates whether the graph data under consideration is suitable for embedding in hyperbolic geometry. Lower values of hyperbolicity imply distortion-free embedding in hyperbolic space. We study adversarial perturbations that attempt to poison the graph structure, consequently rendering hyperbolic geometry an ineffective choice for learning representations. To circumvent this problem, we advocate for utilizing Lorentzian manifolds in machine learning pipelines and empirically show they are better suited to learn hierarchical relationships. Despite the recent proliferation of adversarial robustness methods in the graph data, this is the first work that explores the relationship between adversarial attacks and hyperbolicity property while also providing resolution to navigate such vulnerabilities.