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We present the MLAAD dataset, which is a multi-language dataset for the task of audio anti-spoofing. This dataset has been created using a diverse set of text-to-speech (TTS) models, and is designed to evaluate the out-of-domain generalization of anti-spoofing systems, both with respect to new languages, as well as new TTS models. Specifically, MLAAD comprises: 678.3 hours of synthetic voice, in 51 different languages, created with 140 TTS models, comprising 78 different architectures. The dataset is supposed to be used in conjunction with the M-AILABS dataset . MLAAD provides only the synthetic audio, while M-AILABS provides the real audio.