Adversarial Robust Distillation (ARD) has emerged as a potent defense mechanism tailored to small models against adversarial threats. However, mainstream ARD methods typically exploit teachers’ response as the transferred knowledge, while neglecting the analysis of involved target-related knowledge to mitigate adversarial attacks. Furthermore, these methods primarily focus on logits-level distillation, which overlook the features-level knowledge in teacher models. In this paper, we introduce a novel Hybrid Decomposed Distillation (HDD) approach, which attempts to identify the vital knowledge against adversarial threats through dual-level distillation. Specifically, we first seek to separate the predictions of teacher model into target-related and target-unrelated knowledge for flexible yet efficient logits-level distillation. Besides, to further boost the distillation efficacy, HDD leverages the channel correlations to decompose intermediate features into highly and less relevant components. Extensive experiments on two benchmarks demonstrate that our HDD achieves superior performance in both clean accuracy and robustness, in contrast to current state-of-the-art methods.