Deep Neural Networks remain highly susceptible to perturbation-based attacks, which seek small input modifications that induce model failure. These attacks manifest as either individual or universal adversarial perturbations (IAPs, UAPs), where the former are designed for specific inputs, whereas the latter are input-agnostic. While the simpler setting of IAPs has seen rapid methodological progress, UAP advancements remain comparatively limited, as adapting methods to the universal setting is often nontrivial. In this work, we propose Universal Perturbation Distillation (UPD), a domain-decoupled formulation for learning universal adversarial perturbations from off-the-shelf IAP methods. By treating individual adversarial examples as representation-level supervision, UPD leverages IAP techniques for the universal setting. We instantiate UPD on both large language model jailbreak as well as on image classification settings, achieving and often surpassing state-of-the-art performance, with substantial improvements on robust models.