As deep learning models are increasingly deployed in resource-constrained scenarios, model compression techniques such as pruning, quantization, and knowledge distillation have become key approaches for improving model efficiency and deployability. However, while model compression offers advantages in performance and efficiency, it is also intricately linked to security and privacy issues. On the one hand, compression may alter a model’s representation space or decision boundary, thus introducing new security risks or amplifying existing threats. On the other hand, security and privacy requirements have also shaped the development of model compression techniques, making security-aware compression an increasingly important research direction. This study systematically reviews recent progress in model compression research from a security perspective, focusing on two main aspects. First, it examines the security and privacy risks faced by compressed models, including backdoor attacks, adversarial examples, privacy leakage, and emerging risks in frontier application scenarios. Second, it surveys security-aware model compression methods for adversarial robustness and privacy protection, summarizing representative studies that incorporate security constraints and privacy-preserving mechanisms into the compression process. In addition, this study identifies key issues in security and privacy research on compressed models, provides risk-aware guidance for typical deployment scenarios, recommends benchmark-based evaluation dimensions for assessing the security of compressed models, and discusses future research directions in terms of underlying mechanism analysis and security protection in real-world deployments.