NIST Adversarial ML Guidance: How RL Can Secure Your Organization

The report discusses rising adversarial ML risks, highlighting how serialized model formats (e.g., Pickle) can hide malware that executes during deserialization, citing a recent example from the Hugging Face model repository. It promotes ReversingLabs Spectra Assure to scan compiled binaries and serialized ML models, detect malware, vulnerabilities, secrets, tampering, and risky behaviors, and to build ML-BOM/xBOM inventories while securing CI/CD, training, and deployment environments. The piece positions these capabilities as essential for safely adopting and vetting AI-enabled software.