How does EchelonGraph detect MITRE ATLAS AML.T0000 violations?

EchelonGraph automatically detects MITRE ATLAS AML.T0000 violations using scanner rule ATLAS-RECON-001. The scanner checks cloud infrastructure configurations in real-time and flags non-compliant resources.

What audit questions are asked for MITRE ATLAS AML.T0000?

How do you monitor public registries for your model artefacts? What is the rotation procedure if a leak is detected? Have any leaks been detected and remediated in the last 12 months? Are canary samples maintained for leak detection?

What is the business value of MITRE ATLAS AML.T0000 compliance?

Active model-registry monitoring cuts leak detection time from 42 days to \u003c24 hours, preserving IP value during the early-detection window.

🎯MITRE ATLAS AML.T0000Rule: ATLAS-RECON-001medium

Reconnaissance: AI Model Search

Description

Attackers search public model registries (HuggingFace Hub, ModelScope, Replicate, OpenAI fine-tune APIs) for target organisations' models or fine-tunes.

⚠️ Risk Impact

Public model registries are search-indexed. A fine-tuned model leaked to a public registry (via a misconfigured CI job, an over-permissive bucket, or a careless engineer) becomes the attacker's free reconnaissance.

🔍 How EchelonGraph Detects This

ATLAS-RECON-001Automated scanner rule

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

🖥️ Manual Verification

terminal

huggingface-cli search 'your-company-name' --type model

🔧 Remediation

Monitor public model repos for fine-tunes of your base models. Rotate API keys + signed-cookies if leaked weights are detected. Maintain canary samples in private fine-tunes to detect leaks.

💀 Real-World Attack Scenario

A research org's internal fine-tune of Llama-3 was accidentally pushed to a HuggingFace public repository during a CI pipeline misconfiguration. The fine-tune contained proprietary data summarisation patterns. A competitor discovered it via HuggingFace search 9 days later; downloaded; analysed; replicated the proprietary capability.

💰 Cost of Non-Compliance

Avg cost of leaked fine-tune model: $1.2M-$4M in IP impact (Stanford HAI 2024). Detection lag: avg 42 days without active monitoring.

📋 Audit Questions

1.How do you monitor public registries for your model artefacts?
2.What is the rotation procedure if a leak is detected?
3.Have any leaks been detected and remediated in the last 12 months?
4.Are canary samples maintained for leak detection?

🎯 MITRE ATT&CK Mapping

T1591 — Gather Victim Org Information

⚡ Common Pitfalls

⛔No monitoring — leaks discovered via news rather than internal detection
⛔Slow rotation post-detection — the window of value to the attacker stays open
⛔Public-by-default registry configs in CI

📈 Business Value

Active model-registry monitoring cuts leak detection time from 42 days to <24 hours, preserving IP value during the early-detection window.

⏱️ Effort Estimate

Manual

1 week initial monitor setup + 30 min weekly review

With EchelonGraph

EchelonGraph monitors HuggingFace + GitHub + GitLab for your org's model artefacts; alerts on leak

🔗 Cross-Framework References

AIRMF-MANAGE-3.1OWASP_LLM-LLM03

Automate MITRE ATLAS AML.T0000 compliance

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