AI model scanning is essential for ensuring the quality, integrity, and reliability of AI models. By analyzing internal components like weights, tensor structures, and control-flow operations, scanners can identify issues such as untrained (zero) layers, unusually large biases, or corrupted tensor dimensions. These may degrade performance or indicate poor model construction. These checks also help enforce standards, detect inconsistencies across model exports, and confirm that the model behaves as expected before deployment. From a security perspective, model scanning reduces the attack surface by detecting malicious artifacts such as hidden control-flow nodes (Loop, If, etc.), suspicious hardcoded constants, duplicate tensors, and unsupported data types. These patterns can signal the presence of backdoors, obfuscation attempts, or model tampering. Catching such anomalies early prevents vulnerabilities from propagating into production systems, making AI model scanning a critical part of secure MLOps and trustworthy AI deployment. Flawnter supports scanning .onnx, .safetensors, .h5 and .ot model types. You can check our documentation page for the model types Flawnter supports scanning. If model type we don't support please contact us so we can add to our scan.