Biometric recognition systems powered by deep learning have gained widespread adoption for their effectiveness in providing secure and convenient identity verification. A key element of these systems is the feature template, which extracts and stores essential information from users’ biometric data to facilitate identity validation. However, vulnerabilities in the storage and transmission of biometric templates have become significant sources of potential privacy breaches and security risks. Among these vulnerabilities, template-based biometric reconstruction attacks pose a serious threat to both biometric security and user privacy. These attacks aim to reverse-engineer stored or transmitted biometric templates to reconstruct the user’s original biometric data—such as facial images, fingerprints, or iris patterns—thereby enabling unauthorized access through methods like deep generative models, reverse engineering, and traditional statistical techniques. This reverse reconstruction not only jeopardizes individual privacy but also compromises the security of the entire biometric recognition system. As a result, safeguarding feature templates has become a critical concern in biometric recognition technology. In this chapter, we systematically analyze the threat models, reconstruction attack techniques and defense mechanisms. By evaluating the limitations of existing defense methods in light of future technological advancements, this chapter aims to provide insights into the design of biometric protection methods that achieve both high security and accuracy, thereby enhancing the long-term security and privacy.