Model Extraction & Privacy Assessment
Test your advanced knowledge of model extraction, model stealing, membership inference, and intellectual property theft attacks against AI systems with 9 questions.
Model Extraction & Privacy Assessment
This assessment covers advanced attacks targeting the model itself: model extraction (stealing model weights or behavior), membership inference (determining if specific data was used in training), and intellectual property theft through API-based model distillation.
What is 'model extraction' and why is it considered both a security and business risk?
What is the 'logit lens' technique and how does it aid model extraction?
What is 'membership inference' in the context of AI privacy attacks?
How does 'model distillation' differ from 'model extraction' in practice, even though both produce surrogate models?
What is a 'model inversion' attack and what type of information can it reveal?
What defense technique makes model extraction significantly more expensive without degrading utility for legitimate users?
Why is 'transfer attack development' the most dangerous downstream consequence of successful model extraction?
What are the legal and regulatory implications of model extraction and training data privacy attacks?
How does 'fingerprinting' or 'watermarking' of model outputs help detect unauthorized model extraction?
Concept Summary
| Concept | Description | Primary Risk |
|---|---|---|
| Model extraction | Creating surrogate through API querying | IP theft, enables white-box attacks |
| Logit exploitation | Using probability outputs for efficient extraction | Amplified extraction efficiency |
| Membership inference | Detecting training data inclusion | Privacy violation |
| Model inversion | Reconstructing training data from outputs | Data privacy breach |
| Transfer attacks | Using surrogate for white-box attack development | Efficient adversarial input generation |
| Model distillation abuse | Unauthorized knowledge transfer | IP theft, ToS violation |
| Output watermarking | Detecting extraction via output signatures | Detection and attribution |
Scoring Guide
| Score | Rating | Next Steps |
|---|---|---|
| 8-9 | Excellent | Strong model extraction and privacy knowledge. Proceed to the Privacy Attack Assessment. |
| 6-7 | Proficient | Review explanations for missed questions and revisit extraction attack literature. |
| 4-5 | Developing | Spend additional time with model security and ML privacy fundamentals. |
| 0-3 | Needs Review | Study ML fundamentals (training, inference, model serving) before retesting. |
Study Checklist
- I understand model extraction via API-based surrogate training
- I can explain how logit access amplifies extraction efficiency
- I understand membership inference attacks and their privacy implications
- I can describe model inversion and training data reconstruction
- I understand transfer attacks as the key motivation for extraction
- I can explain the difference between authorized distillation and extraction
- I know the defenses against model extraction (noise, restrictions, monitoring)
- I understand output watermarking for extraction detection
- I can articulate the legal and regulatory implications of these attacks