🎯MITRE ATLAS AML.T0015Rule: ATLAS-DEF-001high

Evade ML Model

Description

Adversarial inputs crafted to evade model detection: image perturbation, prompt obfuscation, content-filter bypass.

⚠️ Risk Impact

Models are deterministic — same input produces same output. Adversaries with query access can craft inputs that systematically evade detection. This is the most-studied AI attack pattern in academic literature.

🔍 How EchelonGraph Detects This

ATLAS-DEF-001Automated 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.

🔧 Remediation

Test models against adversarial input batteries (Adversarial Robustness Toolbox, TextAttack, CleverHans). Deploy input filtering. Apply ensemble methods or randomised smoothing for high-stakes use cases.

💀 Real-World Attack Scenario

A spam-classification model deployed by an email provider was tested by red-teamers using TextAttack. They found a 78% evasion rate by adding character-level perturbations invisible to the human reader. Same technique was already in use by spam operators; the provider's spam rate increased steadily over 4 months before the issue was traced.

💰 Cost of Non-Compliance

Evasion-attack impact varies by use case. For content moderation: avg $2-5M brand impact per incident. For fraud detection: direct fraud cost = avoided detection × case value.

📋 Audit Questions

  • 1.When was the last adversarial-robustness test on your top model?
  • 2.What was the evasion rate?
  • 3.What input filtering is in place?
  • 4.How often is adversarial testing repeated?

🎯 MITRE ATT&CK Mapping

MITRE_ATLAS-AML.T0015

⚡ Common Pitfalls

  • ⛔Treating accuracy on clean data as sufficient evidence of robustness
  • ⛔No CI-integrated adversarial testing — tests are run once and forgotten
  • ⛔Input filtering deployed but not updated as new evasion techniques surface

📈 Business Value

Adversarial-robustness testing is the difference between a model that works in benchmarks and a model that works under attack. Material for high-stakes content moderation, fraud, and security AI.

⏱️ Effort Estimate

Manual

2-3 weeks per model for initial adversarial-robustness baseline

With EchelonGraph

EchelonGraph ships adversarial test batteries integrated with model release pipeline

🔗 Cross-Framework References

EUAIA-ART15-ROBUSTNESSAIRMF-MEASURE-2.6

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