Prohibited items detection is crucial for maintaining security in public areas like airports and train stations. Despite progress in target detection and semantic segmentation algorithms that aid in identifying contraband, X-ray imaging presents unique difficulties due to the severe distortion of pixel information caused by object stacking and density variations. Many existing detection methods fail to account for the complex interplay between foreground and background pixels, resulting in subpar performance. We introduce XMamba, a specialized architecture derived from the VMamba model, designed specifically for detecting prohibited items in X-ray images. XMamba incorporates an Equalization Perception Module (EPM) and a Foreground Channel Decoupling (FCD) method to tackle the challenges of weak features, internal pseudo-contours, and perspective interferences. By leveraging state-space modeling (SSM) to reduce self-attention complexity, XMamba effectively captures global information and minimizes distortion caused by these challenges. Our approach strikes a balance between parameter count, GFLOPs, and accuracy, outperforming both CNN-based and transformer-based detectors. Experimental results demonstrate that our proposed XMamba detector, when combined with cascaded Mask R-CNN, comprehensively surpasses all state-of-the-art methods.