Automatic violence detection in videos (VD) has become a major challenge in the field of Computer Vision with the deployment of smart cameras and the increasing volume of videos shared online. Recent works primarily rely on CNN-based models paired with 3D or recurrent layers to capture the spatiotemporal dynamics of video streams. The integration of additional modalities, such as audio or optical flow, has recently attracted growing interest. Particularly, optical flow has demonstrated strong relevance in modeling motion patterns associated with violent events. However, its estimation is computationally intensive, limiting its use for real-time applications. In this work, we introduce CMoD-VD, a novel method for violence detection based on two CNN+BiLSTM models enhanced with spatial, channel, and temporal attentions. Our method relies on cross-modal distillation with privileged motion supervision. A teacher model is first trained with both RGB and optical flow videos. Then, a student model learns to reproduce its behavior using RGB frames only. This strategy enables accurate inference without relying on motion estimation. Experiments on three public datasets RWF-2000, Hockey Fight and Violent-Flows demonstrate that our student model achieves competitive results close to the teacher and state-of-the-art methods, while significantly reducing computational costs.