Face recognition systems are increasingly being used in different applications. In such systems, some features (also known as embeddings or templates) are extracted from each face image. Then, the extracted templates are stored in the system’s database during the enrolment stage and are later used for recognition. In this talk, I explain template inversion attacks against face recognition systems, where an adversary gains access to the templates stored in the database of the system and tries to reconstruct underlying face from facial templates. The reconstructed face images not only reveal privacy-sensitive information (such as age, gender, etc.), but can also be used to impersonate. As a matter of fact, the reconstructed face images jeopardize both security and privacy of users: the adversary can use the reconstructed face image to impersonate and enter the system (security threat). In addition, the reconstructed face image not only reveal privacy-sensitive information of the enrolled user, such as age, gender, ethnicity, etc, but also provide a good estimation of subject’s face (privacy threat). Our experimental results demonstrate a critical vulnerability in face recognition systems.