Model Inversion
Description
Attacker reconstructs training data from model outputs. Particularly impactful when models are trained on sensitive PII (medical records, facial images, financial data).
⚠️ Risk Impact
Model inversion attacks can recover individual training records from a trained model via repeated, carefully-crafted queries. The attack is feasible on any model that has query access without strong rate limits.
🔍 How EchelonGraph Detects This
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
Apply differential privacy during training (DP-SGD with privacy budget ε). Limit query rate per principal. Monitor for repeated probing patterns. Aggregate outputs before exposure where possible.
💀 Real-World Attack Scenario
Researchers demonstrated (Fredrikson et al., 2015) recovery of recognisable training images from a face-recognition model trained on celebrity faces. The technique generalises to medical imaging models, recovering patient-identifiable features from models trained on medical records.
💰 Cost of Non-Compliance
Model inversion + PII recovery: GDPR Article 32 violation = up to €20M / 4% revenue. HIPAA + state breach notification cost: avg $4.45M (IBM 2024 Health-care breach).
📋 Audit Questions
- 1.What is the privacy budget (ε) for your DP-trained models?
- 2.What is the query-rate limit per principal?
- 3.Have you tested your model against inversion attacks?
- 4.How do you monitor for systematic probing patterns?
🎯 MITRE ATT&CK Mapping
⚡ Common Pitfalls
- ⛔No differential privacy on models trained on sensitive data
- ⛔Per-IP rate limits but not per-principal — attackers use IP rotation
- ⛔No monitoring of query patterns — systematic probing goes undetected
📈 Business Value
Differential privacy + rate limiting prevents the highest-impact privacy attack on ML models. Material for healthcare AI, finance AI, and any AI trained on regulated data.
⏱️ Effort Estimate
4-8 weeks for DP-training integration + monitoring
EchelonGraph monitors inference patterns; alerts on systematic probing
🔗 Cross-Framework References
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