Defending and Attacking AI and ML Implementations Workshop by Tonex
This hands-on workshop explores the dual aspects of securing and compromising AI and ML systems. Participants will gain insights into the vulnerabilities inherent in AI/ML implementations, how adversaries exploit these weaknesses, and how to build robust defenses. Through interactive labs, real-world case studies, and expert-led sessions, this workshop equips participants with the knowledge and skills to defend and attack AI and ML implementations effectively.
Learning Objectives
By the end of the workshop, participants will be able to:
- Understand the lifecycle of AI/ML systems and identify potential attack vectors.
- Conduct adversarial attacks against AI models to evaluate their robustness.
- Develop countermeasures to defend AI and ML implementations from exploitation.
- Explore the ethical implications and legal considerations of attacking and defending AI systems.
- Apply best practices to secure AI/ML systems in real-world scenarios.
- Build resilient AI models using techniques like adversarial training and robust data pipelines.
Target Audience:
- Cybersecurity Professionals: Interested in securing AI/ML systems against modern threats.
- Data Scientists and ML Engineers: Focused on understanding the vulnerabilities of their models.
- Penetration Testers and Red Teamers: Looking to expand their expertise into AI and ML domains.
- AI/ML Developers: Keen to build secure and resilient AI applications.
- Academics and Researchers: Exploring the intersection of AI security and adversarial AI.
Workshop Modules:
Day 1: Understanding AI/ML Threats and Attacks
Session 1: AI and ML Security Fundamentals (1.5 hours)
- Overview of AI/ML lifecycle and architecture.
- Security challenges specific to AI and ML systems.
- Key attack vectors: Data poisoning, model evasion, model extraction, and inference attacks.
Session 2: Adversarial Attacks on AI/ML Models (2 hours)
- Introduction to adversarial examples and evasion techniques.
- Hands-on lab: Crafting adversarial inputs to evade a classification model.
- Case studies: High-profile adversarial attacks on AI systems.
Break: 30 minutes
Session 3: Data Poisoning Attacks (2 hours)
- Understanding how adversaries corrupt training datasets.
- Demonstration: Injecting poisoned data into a machine learning pipeline.
- Hands-on lab: Designing and executing a data poisoning attack.
Session 4: Model Extraction and Inference Attacks (1.5 hours)
- Techniques for extracting model architecture and parameters.
- Exploiting ML models to infer sensitive data.
- Hands-on lab: Conducting a model extraction attack using black-box techniques.
Wrap-Up Discussion (30 minutes)
- Participant Q&A and expert insights.
Day 2: Defending AI/ML Implementations
Session 1: Building Robust AI Models (1.5 hours)
- Adversarial training techniques.
- Regularization and data sanitization strategies.
- Demonstration: Enhancing model resilience against evasion attacks.
Session 2: Securing AI Pipelines and Infrastructure (2 hours)
- Best practices for securing the AI/ML pipeline.
- Role of secure software development lifecycle (SDLC) in AI/ML.
- Hands-on lab: Implementing defensive measures in an AI/ML workflow.
Break: 30 minutes
Session 3: Monitoring and Incident Response for AI Systems (1.5 hours)
- Monitoring AI models for adversarial behavior.
- Detecting and mitigating real-time attacks.
- Interactive exercise: Simulating an attack and implementing incident response.
Session 4: Ethics, Regulation, and Future Trends (1.5 hours)
- Ethical considerations in AI security.
- Regulatory and legal frameworks for AI/ML systems.
- Emerging trends in AI/ML security and future threats.
Wrap-Up Panel Discussion and Q&A (1 hour)
- Expert panel: Balancing innovation and security in AI.
- Open floor for participant questions and feedback.