Moat is a dynamic analysis approach that secures ML model execution by monitoring host system interactions during well-defined model lifecycle phases. Re-Moat, its reference implementation, detects all evaluated attack classes with a near-zero false-positive rate across 77,974 real-world models and multiple frameworks, outperforming existing static model-scanning solutions.