Model Update Hijacking

Model update hijacking targets the *deployment pipeline* rather than the training pipeline. g., TIES, DARE, SLERP), and LoRA adapter composition. Each of these processes involves combining or replacing model weights — operations that can be subverted to inject adversarial modifications.

• Model checkpoint integrity verification with cryptographic attestation (hardware-rooted signing)
• Federated learning contribution analysis: statistical profiling of per-participant gradient updates
• Model behavior regression testing before every deployment update
• Weight-space anomaly detection: compare model weights against expected trajectories

Model update hijacking chains to T13 (Supply Chain Attacks) as the primary delivery mechanism for compromised models. Federated learning poisoning (T6-AP-008B) chains to T6-AT-003 (Backdoor Insertion) as an alternative insertion vector.