Attack examples can not only be used as training data to improve the expressive ability of the model but also can be used to evaluate the robustness of the deep learning model. However, the size of the attack examples is limited to the original data by perturbing an existing data point within a small matrix norm. In order to obtain attack examples more efficiently, a multiple attack generative adversarial networks(M-AttGAN) is proposed, where the attackers are not restricted to original data. The proposed network is designed to train two pairs of GANs simultaneously to fit for the distribution of original data and the distribution of the perturbation in the GANs latent space. The trained model, can generate attack examples efficiently without restrictions, and provide more data for adversarial training and improve the robustness of neural networks. We adopt human evaluation and contrastive analysis with other state-of-the-art algorithms to prove that it is feasible to utilize GANs to attack example generation. Experimental results on the MNIST and CIFAR-10 dataset show that the proposed model not only generates high-quality attack examples breaking the limits of the original data, but also has good aggression and attack migration competence.