Back to Attack Vectors

Model Extraction & Stealing

Techniques to replicate or steal proprietary AI models through query-based attacks and API abuse

Attack Techniques
Query-Based Model Extraction
Critical

Systematically query target model to collect input-output pairs and train surrogate model

Cost: High query volume required
Detection: Medium - unusual query patterns
Knowledge Distillation Attacks
Critical

Use soft labels from target model to train student model that mimics behavior

Cost: Lower query volume with soft labels
Detection: Low - appears as normal usage
Model Inversion
High

Reconstruct training data or sensitive features from model predictions

Cost: Medium query volume
Detection: Medium - targeted queries
API Abuse & Rate Limit Bypass
High

Circumvent API protections using distributed queries or credential rotation

Cost: Low with automation
Detection: High with proper monitoring
Defense Strategies
Query Rate Limiting
Medium

Limit number of queries per user/IP

Prediction Perturbation
High

Add noise to model outputs

Watermarking
High

Embed unique signatures in model behavior

Query Monitoring
Medium

Detect suspicious query patterns

Differential Privacy
High

Provide privacy guarantees on outputs

Economic Impact
$100M+
Estimated cost of stolen models
85%
Model accuracy achievable
10K-1M
Queries needed for extraction