The threat model accounts for the ML pipeline’s unique architecture (relative to traditional software systems), which introduces novel threats and risks due to new attack surfaces within the ML lifecycle and pipeline such as data collection, model training, and model inference and deployment. Multiple issues enable a denial-of-service attack if an end user can control certain inputs, such as model files, dataset files, or configuration files (TOB-YOLO-9, TOB-YOLO-8, TOB-YOLO-12). For example, the codebase allows engineers to provide their own configuration files, whether they represent a different model architecture or are pre-trained files (given the different applications of the YOLO model architecture).