ai-llm-red-team-handbook/docs/Chapter_20_Outline.md

Chapter 20: Model Theft and Membership Inference - Outline

Introduction

Overview of model confidentiality threats, IP theft, and privacy violations in ML systems.

20.1 Introduction to Model Theft

• 20.1.1 What is Model Extraction?
• 20.1.2 Types of Model Theft Attacks
• 20.1.3 Threat Model and Attacker Capabilities
• 20.1.4 Economic and Security Impact

20.2 Query-Based Model Extraction

• 20.2.1 Basic Extraction Methodology
• 20.2.2 Random Query Strategies
• 20.2.3 Synthetic Data Generation
• 20.2.4 Query Budget Optimization
• 20.2.5 Measuring Extraction Fidelity

20.3 Active Learning Extraction

• 20.3.1 Uncertainty Sampling
• 20.3.2 Boundary Exploration
• 20.3.3 Query-Efficient Techniques
• 20.3.4 Adaptive Query Selection
• 20.3.5 Minimizing Query Costs

20.4 LLM-Specific Model Theft

• 20.4.1 Prompt-Based Extraction
• 20.4.2 Knowledge Distillation from APIs
• 20.4.3 Task-Specific Theft
• 20.4.4 Capability Enumeration
• 20.4.5 Fine-Tuning Data Extraction

20.5 Architecture and Hyperparameter Stealing

• 20.5.1 Discovering Model Architecture
• 20.5.2 Hyperparameter Inference
• 20.5.3 Side-Channel Attacks
• 20.5.4 Timing-Based Inference
• 20.5.5 Memory Footprint Analysis

20.6 Membership Inference Attacks

• 20.6.1 What is Membership Inference?
• 20.6.2 Privacy Implications
• 20.6.3 Basic Inference Techniques
• 20.6.4 Confidence-Based Methods
• 20.6.5 Loss-Based Detection

20.7 Advanced Membership Inference

• 20.7.1 Shadow Model Attacks
• 20.7.2 Metric-Based Inference
• 20.7.3 LLM Membership Inference
• 20.7.4 Perplexity-Based Detection
• 20.7.5 Exact Memorization Checking

20.8 Model Inversion Attacks

• 20.8.1 Reconstructing Training Data
• 20.8.2 Feature Inversion Techniques
• 20.8.3 Privacy Leakage via Inversion
• 20.8.4 Gradient-Based Inversion
• 20.8.5 GAN-Based Reconstruction

20.9 Attribute Inference

• 20.9.1 Inferring Sensitive Attributes
• 20.9.2 Property Inference Attacks
• 20.9.3 Statistical Inference Methods
• 20.9.4 Correlation Exploitation
• 20.9.5 Demographic Inference

20.10 Watermarking and Fingerprinting

• 20.10.1 Model Watermarking Techniques
• 20.10.2 Backdoor-Based Watermarks
• 20.10.3 Fingerprinting for Ownership
• 20.10.4 Verification Methods
• 20.10.5 Robustness Against Removal

20.11 Detecting Model Theft

• 20.11.1 Query Pattern Analysis
• 20.11.2 Anomaly Detection
• 20.11.3 Rate Limiting Strategies
• 20.11.4 Behavioral Monitoring
• 20.11.5 Honeypot Techniques

20.12 Defenses Against Extraction

• 20.12.1 Output Perturbation
• 20.12.2 Prediction Rounding
• 20.12.3 Confidence Masking
• 20.12.4 Query Complexity Pricing
• 20.12.5 Adversarial Training

20.13 Privacy-Preserving ML

• 20.13.1 Differential Privacy Training
• 20.13.2 Federated Learning
• 20.13.3 Secure Multi-Party Computation
• 20.13.4 Homomorphic Encryption
• 20.13.5 Knowledge Distillation for Privacy

20.14 Case Studies

• 20.14.1 Real-World Model Theft Incidents
• 20.14.2 Privacy Breach Examples
• 20.14.3 Successful Defense Deployments
• 20.14.4 Industry Best Practices
• 20.14.5 Lessons Learned

20.15 Legal and Compliance Considerations

• 20.15.1 Intellectual Property Protection
• 20.15.2 GDPR and Privacy Laws
• 20.15.3 Model Ownership Rights
• 20.15.4 API Terms of Service
• 20.15.5 Liability and Penalties

20.16 Summary and Key Takeaways

• Most effective theft techniques
• Critical privacy attacks
• Essential defense strategies
• Compliance requirements
• Future trends

References and Further Reading

• Academic papers
• Industry reports
• Tools and frameworks
• Standards and guidelines

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Total Estimated Length: 1,800-2,200 lines Code Examples: 30-35 Practical Scenarios: 20-25