Model Scanning

Just like application code is scanned for vulnerabilities before deployment, AI models need to be evaluated for hidden risks. These include security threats, ethical concerns, licensing violations, and technical flaws that can affect performance or trust.

Model scanning assesses:

• Embedded risks: Hardcoded credentials, exposed PII, or malicious payloads
• Toxic behavior: Unfiltered harmful, biased, or offensive output
• Unusual logic: Irregular behavior under edge-case prompts or agent use
• Compliance violations: Use of unauthorized data or license-restricted components
• Model metadata: Origin, ownership, training details, and chain of custody

This scanning can be applied to foundation models, fine-tuned versions, and even small-scale internal AI models. It’s essential for meeting internal AI review policies and external audit standards.

How PointGuard AI HelpsPointGuard performs deep model scanning as part of its AI Supply Chain Security suite. It evaluates downloaded, hosted, and in-house models for vulnerabilities, content safety, license issues, and behavioral threats—flagging them before they’re integrated into applications or deployed into production.

Explore: https://www.pointguardai.com/supply-chain

References:

IBM: Understanding Model Scanning

Aquasec: The Importance of Model Scanning