Machine-learning algorithms are widely used for cybersecurity applications, including spam, malware detection, biometric recognition. In these applications, the learning algorithm must face intelligent and adaptive attackers who can carefully manipulate data to purposely subvert the learning process. As machine learning algorithms have not been originally designed under such premises, they have been shown to be vulnerable towell-crafted attacks, including test-time evasion and training-time poisoning attacks (also known as adversarial examples).This talk aims to introduce the fundamentals of adversarial machine learning and some techniques to assess the vulnerability of machine-learning algorithms to adversarial attacks. We report application examples including object recognition in images, biometric identity recognition, spam, and malware detection.