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, in this paper, we propose M-AttGAN, a new attack example generation model based on GAN, where the attackers are not restricted to original data. The proposed network is designed to train two pairs of GAN simultaneously to fit for the distribution of original data and the distribution of the perturbation in the GAN latent space. Once our model is trained, it can generate attack examples efficiently and without restrictions, providing more data for adversarial training and improving 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 GAN to attack example generation. Our experimental results on the MNIST and CIFAR-10 dataset show that our model not only generates high-quality attack examples breaking the limits of the original data but also has good aggression and attack migration competence.