Deep neural policies have unlocked agile flight for quadcopters, adaptive grasping for manipulators, and reliable navigation for ground robots, yet their millions of weights conflict with the tight memory and real-time constraints of embedded microcontrollers. Second-order pruning methods, such as Optimal Brain Damage (OBD) and its variants, including Optimal Brain Surgeon (OBS) and the recent SparseGPT, compress networks in a single pass by leveraging the local Hessian, achieving far higher sparsity than magnitude thresholding. Despite their success in vision and language, the consequences of such weight removal on closed-loop stability, tracking accuracy, and safety have remained unclear. We present the first mathematically rigorous robustness analysis of second-order pruning in nonlinear discrete-time control. The system evolves under a continuous transition map, while the controller is an $L$-layer multilayer perceptron with ReLU-type activations that are globally 1-Lipschitz. Pruning the weight matrix of layer $k$ replaces $W_k$ with $W_k+δW_k$, producing the perturbed parameter vector $\widehatΘ=Θ+δΘ$ and the pruned policy $π(\cdot;\widehatΘ)$. For every input state $s\in X$ we derive the closed-form inequality $ \|π(s;Θ)-π(s;\widehatΘ)\|_2 \le C_k(s)\,\|δW_k\|_2, $ where the constant $C_k(s)$ depends only on unpruned spectral norms and biases, and can be evaluated in closed form from a single forward pass. The derived bounds specify, prior to field deployment, the maximal admissible pruning magnitude compatible with a prescribed control-error threshold. By linking second-order network compression with closed-loop performance guarantees, our work narrows a crucial gap between modern deep-learning tooling and the robustness demands of safety-critical autonomous systems.