🤖NIST AI-RMF MANAGE-3.1Rule: AIRMF-MN-004high

Third-party AI risks managed

Description

Risks from third-party AI components (foundation models, datasets, libraries, hosted APIs) are inventoried and managed.

⚠️ Risk Impact

Modern AI stacks are 80%+ third-party (foundation models, public datasets, open-source libraries). Each third-party component is a supply-chain attack surface. The Hugging Face token leak (2024) compromised 100+ organisations via leaked credentials on the registry.

🔍 How EchelonGraph Detects This

AIRMF-MN-004Automated scanner rule

EchelonGraph's Tier 1 Cloud Scanner automatically checks for this condition across all connected cloud accounts. Violations are flagged as high-severity findings with remediation guidance.

🖥️ Manual Verification

terminal
pip-audit --requirement requirements.txt --format json && cosign verify --certificate-identity ... ghcr.io/your/model:tag

🔧 Remediation

Maintain a 'AI supply chain' inventory: foundation models in use, dataset sources, library dependencies, hosted-API providers. Pin versions; verify signatures where supported (cosign for containers, model cards for HF); scan dependencies (pip-audit, npm audit).

💀 Real-World Attack Scenario

Hugging Face revealed (May 2024) that a vulnerability in its Spaces platform leaked authentication tokens for 100+ organisations. Affected tokens granted access to private models and datasets. Researchers used the tokens to enumerate exposed proprietary models worth an undisclosed but material sum. Affected orgs faced incident response cost + retraining cost + customer-trust impact.

💰 Cost of Non-Compliance

Hugging Face token leak (May 2024): 100+ orgs affected. Avg supply-chain AI breach cost in 2024: $4.6M (IBM 2024). EU AI Act Article 16(j) corrective action: €15M / 3% revenue.

📋 Audit Questions

  • 1.Show me your AI supply-chain inventory.
  • 2.Which foundation models are in production? Which version?
  • 3.What is your signature-verification process for downloaded weights?
  • 4.How are AI-library CVEs triaged and remediated?

🎯 MITRE ATT&CK Mapping

T1195.001 — Compromise Software Dependencies

⚡ Common Pitfalls

  • ⛔Not tracking foundation model version drift — silently moving to a newer model that behaves differently
  • ⛔Skipping signature verification on 'trusted' sources like Hugging Face — until they're breached
  • ⛔Pinning Python dependencies but not the model checkpoints themselves

📈 Business Value

AI supply-chain governance prevents the highest-frequency 2024 attack vector (compromised dependencies). One avoided incident pays for the programme.

⏱️ Effort Estimate

Manual

1-2 weeks initial inventory; ongoing dependency scanning

With EchelonGraph

EchelonGraph SBOM + AI-supply-chain monitor with signature verification + CVE correlation

🔗 Cross-Framework References

OWASP_LLM-LLM03MITRE_ATLAS-AML.T0010

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