AIJack provides an open-source simulation tool for testing the security of AI systems against various attacks such as poisoning, model inversion, backdoor, and free-rider attacks. It implements defense mechanisms including differential privacy, homomorphic encryption, k-anonymity, and federated learning. The framework supports over 30 state-of-the-art methods for adversarial machine learning research and evaluation.