Course Outline
Introduction to AI Threat Modeling
- Factors that make AI systems susceptible to attack.
- Comparison of the AI attack surface with traditional systems.
- Primary attack vectors across data, model, output, and interface layers.
Adversarial Attacks on AI Models
- Grasping adversarial examples and perturbation techniques.
- Distinguishing between white-box and black-box attacks.
- Exploring FGSM, PGD, and DeepFool methodologies.
- Visualizing and generating adversarial samples.
Model Inversion and Privacy Leakage
- Reconstructing training data from model outputs.
- Analyzing membership inference attacks.
- Privacy implications in classification and generative models.
Data Poisoning and Backdoor Injections
- How compromised data manipulates model behavior.
- Trigger-based backdoors and Trojan horse attacks.
- Strategies for detection and data sanitization.
Robustness and Defense Techniques
- Implementing adversarial training and data augmentation.
- Applying gradient masking and input preprocessing.
- Utilizing model smoothing and regularization techniques.
Privacy-Preserving AI Defenses
- Overview of differential privacy.
- Managing noise injection and privacy budgets.
- Leveraging federated learning and secure aggregation.
AI Security in Practice
- Conducting threat-aware model evaluation and deployment.
- Integrating ART (Adversarial Robustness Toolbox) into real-world scenarios.
- Examining industry case studies involving real-world breaches and mitigation efforts.
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows and model training processes.
- Proficiency with Python and standard ML frameworks like PyTorch or TensorFlow.
- Basic knowledge of security or threat modeling concepts is beneficial.
Target Audience
- Machine learning engineers.
- Cybersecurity analysts.
- AI researchers and model validation teams.
Testimonials (3)
inventory and identifying the different risk exposures within AI
Gary Cook - Cybersecurity and Information Technology Risk Division
Course - Introduction to AI Trust, Risk, and Security Management (AI TRiSM)
I really enjoyed learning about AI attacks and the tools out there to begin practicing and actively using for security testing. I took a lot of knowledge away which I didn't have at the beginning and the course met what I hoped it would be. My favorite part shown from the training was Comet Browser and was amazed at what it could do. Definitely something will be looking into more. Overall it was a great course and enjoyed learning all OWASP GenAI Top 10.
Patrick Collins - Optum
Course - OWASP GenAI Security
The profesional knolage and the way how he presented it before us
