Physically adversarial attacks can mislead detectors in the real world and have attracted increasing attention. However, most existing works directly manipulate the model’s final outputs as attack objects while ignoring the inherent characteristics of objects such as multi-scale features, which are easily trapped into model-specific local optimum and degrade the transferability. To address this issue, we propose the Multi-scale Feature-aware Attack (MFA) to generate adversarial camouflages with strong attacking ability and transferability by disrupting multi-scale object-aware critical features. Specifically, we adopt the location and category information of the detector outputs to assign attribution scores to different scale feature layers. Then, we weight each feature according to their attribution results and design a pixel-level loss function in the opposite optimized direction of object detection to generate adversarial camouflages. We conduct extensive experiments in both the digital and physical world on ten detection models (e.g., the up-to-date yolov7) and significantly demonstrate the superior performance of the proposed MFA. Our Code will be available at: https://github.com/ChenWen1997/MFA.