Few-shot learning (FSL) facilitates a variety of computer vision tasks yet remains vulnerable to adversarial attacks. Existing adversarially robust FSL methods rely on either visual similarity learning or class concept learning. Our analysis reveals that these two learning paradigms are complementary, exhibiting distinct robustness due to their unique decision boundary types (concepts clustering by the visual similarity label vs. classification by the class labels). To bridge this gap, we propose a novel framework unifying adversarially robust similarity learning and class concept learning. Specifically, we distill parameters from both network branches into a "unified embedding model" during robust optimization and redistribute them to individual network branches periodically. To capture generalizable robustness across diverse branches, we initialize adversaries in each episode with cross-branch class-wise "global adversarial perturbations" instead of less informative random initialization. We also propose a branch robustness harmonization to modulate the optimization of similarity and class concept learners via their relative adversarial robustness. Extensive experiments demonstrate the state-of-the-art performance of our method in diverse few-shot scenarios.