Model Verification in the context of AI security is the process of verifying that a deployed model has not been tampered with, corrupted, or replaced — ensuring model integrity by checking that the model in production matches the validated, approved version.

Verification Methods

• Hash Verification: Compute a cryptographic hash of model weights and compare to the approved hash.
• Behavioral Probes: Send known test inputs and verify expected outputs match the validated model.
• Weight Checksums: Periodic checksum of weight files detects unauthorized modifications.
• TEE Verification: Run inference in a Trusted Execution Environment (TEE) that verifies model integrity.

Why It Matters

• Supply Chain: Verify that a model received from a third party hasn't been trojaned or modified.
• Production Safety: Ensure the model controlling fab equipment is the approved, validated version.
• Compliance: Regulatory requirements may mandate model integrity verification in production.

Model Verification is trust but verify — ensuring that the deployed model is exactly the model that was validated and approved.