Monocular 3D object detection is a cost-efficient alternative to multisensor systems, yet it remains fragile to multi-component adversarial attacks that perturb the image and tamper with camera calibration. Compounded distortions degrade 3D reasoning by disrupting the correspondence between the 3D geometry and 2D image plane. To address this problem, this work proposes MonoPure, a monocular 3D object detection framework that performs multi-component purification via disentangled and projective representations. MonoPure incorporates a disentangled purification and segmentation module that purifies the image data, with a target-region probability map steering diffusion-based purification to focus on task-relevant regions. In addition, MonoPure presents a 3D detection decoder that integrates 2D skeleton keypoints as object-level spatial cues, enabling occlusion-robust 3D detection. Finally, a projective calib-purification module restores compromised intrinsics by iteratively minimizing the reprojection error between projected 3D boxes and calibration-invariant 2D detection boxes. The experiments confirm that MonoPure outperforms prior detectors under multi-component attacks and occlusion.