Object detection is widely used in various fields such as autonomous driving, industry, and medical care. Using the object detection algorithm to solve key tasks in different fields has gradually become the main method. However, the robustness of the object detection model based on deep learning is seriously insufficient under the attack of adversarial samples. It is easy to make the model prediction wrong by adding the adversarial samples constructed by small perturbations, which greatly limits the application of the object detection model in key security fields. In practical applications, the models are black-box models. Related research on black-box attacks against object detection models is relatively lacking, and there are many problems such as incomplete robustness evaluation, low attack success rate of black-box, and high resource consumption. To address the aforementioned issues, this study proposes a black-box object detection attack algorithm based on a generative adversarial network. The algorithm uses the generative network fused with an attention mechanism to output the adversarial perturbations and employs the alternative model loss and the category attention loss to optimize the generated network parameters, which can support two scenarios of target attack and vanish attack. A large number of experiments are conducted on the Pascal VOC and the MSCOCO datasets. The results demonstrate that the proposed method has a higher black-box transferable attack success rate and can perform transferable attacks between different datasets.