🎯MITRE ATLAS AML.T0010Rule: ATLAS-INIT-001critical

ML Supply Chain Compromise

Description

Attacker compromises ML supply chain components — datasets, models, libraries, container images — that downstream organisations depend on.

⚠️ Risk Impact

ML supply chain compromise is the single most damaging AI-specific attack pattern. One compromised upstream component propagates to thousands of downstream deployments before detection.

🔍 How EchelonGraph Detects This

ATLAS-INIT-001Automated scanner rule

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

🖥️ Manual Verification

terminal
syft scan ai-workload:latest -o spdx-json | jq '.packages[] | select(.name | contains("torch") or contains("transformers"))'

🔧 Remediation

Verify cryptographic signatures on model artefacts (cosign for containers, model card hashes). Pin dependency versions. Scan dependencies for known CVEs. Maintain an SBOM for AI workloads.

💀 Real-World Attack Scenario

The PyTorch supply-chain attack of December 2022: the malicious 'torchtriton' package was uploaded to PyPI matching a Triton release. Anyone pip-installing nightly PyTorch builds for one weekend pulled the malicious package, which exfiltrated SSH keys and gpg keys via DNS. Estimated affected installations: high thousands.

💰 Cost of Non-Compliance

PyTorch supply-chain attack (Dec 2022): widespread impact. Avg ML supply-chain breach cost in 2024: $4.6M (IBM). Detection lag: typically 3-7 days for actively-monitored orgs.

📋 Audit Questions

  • 1.What is your SBOM coverage for AI workloads?
  • 2.How are model artefacts cryptographically verified?
  • 3.Show me the last CVE remediation cycle for an AI dependency.
  • 4.What is your pinning strategy for AI libraries?

🎯 MITRE ATT&CK Mapping

T1195.001 — Compromise Software DependenciesT1195.002 — Compromise Software Supply Chain

🏗️ Infrastructure as Code Fix

main.tf
resource "kubernetes_pod" "ai_workload" {
  spec {
    image_pull_secrets { name = "private-registry-secret" }
    container {
      name  = "ai"
      image = "gcr.io/your-project/ai-workload:v1.2.3@sha256:abc123..."  # Digest-pinned, not just tag
    }
  }
}

⚡ Common Pitfalls

  • ⛔Pinning by tag only (mutable) instead of digest (immutable)
  • ⛔No SBOM for ML workloads — only containers
  • ⛔Skipping signature verification because 'we trust HuggingFace'

📈 Business Value

Verified ML supply chain prevents the highest-frequency 2024-2025 AI breach vector. One avoided incident pays for the programme.

⏱️ Effort Estimate

Manual

3-4 weeks for SBOM + signature verification + admission policy

With EchelonGraph

EchelonGraph ships SBOM + AI-dependency vuln scanning per workload

🔗 Cross-Framework References

OWASP_LLM-LLM03EUAIA-ART15-CYBERSECISO42001-8.4

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