Incidents involving malicious ML models reveal significant weaknesses in standard Digital Forensics and Incident Response (DFIR) procedures, which are traditionally focused on executable malware, scripts, or phishing-based vectors. When the “malware” is a machine learning artifact—such as a .pt or .pkl file—existing tools, training, and playbooks often fall short. Lack of Recognition of ML Artifacts as Threat VectorsDFIR playbooks rarely consider ML model files as potential root cau...
