Model File & Artifact Scanning
Objective
Detect malware, backdoors, and unsafe serialization in model files before deployment.
Control / requirement
Static analysis + deserialization sandboxing + signature verification.
Business impact
Poisoned models enable persistent compromise. Estimated exposure: $1M–$10M.
Validation approach
Test ID: D4-CTL-02-VTS-001 Test Type: Automated Test Design: Ingest known-malicious model artifacts; measure detection and blocking rate. Execution Steps: 1. Load GAISSF™ malicious model dataset (open-source options: ProtectAI, ModelScan) 2. Run through scanning pipeline 3. Verify block & quarantine 4. Log detection metrics Pass Criteria: malicious_file_blocked = 100%; false_positive_rate < 2%; scan_latency < 5s Independent Verification: Auditor injects auditor-crafted malicious artifacts.
Expected evidence
Not separately specified in the available source.
Mapping and source
Not separately specified in the available source.
Implementation guidance
Translate the requirement into system-specific procedures, responsible roles, technical configurations and review conditions. Retain evidence showing both design and operating performance. Where the source provides no separate implementation instruction, do not infer that a single technical mechanism is sufficient.
Assessment considerations
- Confirm scope and applicability.
- Inspect control design and responsible ownership.
- Test representative operation and adverse conditions where appropriate.
- Evaluate evidence provenance, completeness and contradictory evidence.
- Record limitations and notably absent outcomes.