We aim to unify existing works’ diverging opinions on how architectural components affect the adversarial robustness of CNNs. To achieve our goal, we synthesize a suite of generalizable robust architectural design principles: (a) optimal range for depth and width configurations, (b) preferring convolutional over patchify stem stage, and (c) robust residual block design by adopting squeeze and excitation blocks, and non-parametric smooth activation functions. Through extensive experiments across a wide spectrum of dataset scales, adversarial training methods, model parameters, and network design spaces, our principles consistently and markedly improve AutoAttack accuracy: 1–3 percentage points (pp) on CIFAR-10 and CIFAR-100, and 4–9 pp on ImageNet. The code is publicly available at https://github.com/poloclub/robust-principles.