Lilly Tech Systems
Learn Model Extraction & Stealing
Discover how attackers clone proprietary AI models through strategic API queries, side-channel attacks, and membership inference. Learn to defend your models with rate limiting, output perturbation, watermarking, and API security hardening.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is model extraction? Understand the economics of model stealing, threat actors, and real-world attack cases.
2. Query-Based Extraction
Learn how attackers clone models through systematic API queries, active learning strategies, and knowledge distillation.
3. Side-Channel Attacks
Explore timing attacks, membership inference, model inversion, and hyperparameter extraction through indirect signals.
4. API Protection
Implement rate limiting, query budgets, output perturbation, and anomaly detection to prevent extraction attacks.
5. Watermarking
Embed verifiable watermarks into model outputs and weights to prove ownership and detect stolen model copies.
6. Best Practices
Production defense strategies, legal protections, monitoring frameworks, and comprehensive IP security programs.
What You'll Learn
By the end of this course, you'll be able to:
Understand Extraction Methods
Know how query-based extraction, knowledge distillation, and side-channel attacks work to clone proprietary models.
Protect API Endpoints
Implement rate limiting, query budgets, output perturbation, and anomaly detection to thwart extraction attempts.
Watermark Your Models
Embed robust watermarks in model outputs and weights that survive fine-tuning and prove ownership in disputes.
Build Defense Programs
Create comprehensive model protection strategies combining technical controls, monitoring, and legal safeguards.