Security of Artificial Intelligence

• Basics of poisoning attacks
• Staging a simple poisoning attack
• Backdoor poisoning attacks
• Hidden-trigger backdoor attacks
• Clean-label attacks
• Advanced poisoning attacks
• Mitigations and defenses
• Traditional supply chain risks and AI
• AI supply chain risks
• Data poisoning
• AI/ML SBOMs
• Poisoning embeddings in RAG
• Poisoning attacks on fine-tuning LLMs

8 LiveLab in this lesson — see the labs panel →

• Injecting backdoors using pickle serialization
• Injecting Trojan horses with Keras Lambda layers
• Trojan horses with custom layers
• Neural payload injection
• Attacking edge AI
• Model hijacking
• Fundamentals of evasion attacks
• Perturbations and image evasion attack techniques
• NLP evasion attacks with BERT using TextAttack
• Universal Adversarial Perturbations (UAPs)
• Black-box attacks with transferability
• Defending against evasion attacks
• Use of GANs for deepfakes and deepfake detection
• Using GANs in cyberattacks and offensive security
• Defenses and mitigations

9 LiveLab in this lesson — see the labs panel →

• Understanding model inversion attacks
• Types of model inversion attacks
• Example model inversion attack
• Understanding inference attacks
• Attribute inference attacks
• Example attribute inference attack
• Membership inference attacks
• Privacy-preserving ML and AI
• Simple data anonymization
• Advanced anonymization
• Differential privacy (DP)
• Federated learning (FL)
• Split learning
• Advanced encryption options for privacy-preserving ML
• Advanced ML encryption techniques in practice
• Applying privacy-preserving ML techniques

9 LiveLab in this lesson — see the labs panel →

• Secure by design AI
• Building our threat library
• Industry AI threat taxonomies
• AI threat taxonomy mapping
• Threat modeling for AI
• Threat modelling in action
• Enhanced FoodieAI threat model
• Risk assessment and prioritization
• Security design and implementation
• Testing and verification
• Shifting left – embedding security into the AI life cycle
• Live operations
• Beyond security – Trustworthy AI
• Enterprise security AI challenges
• Foundations of enterprise AI security
• Protecting AI with enterprise security
• Operational AI security
• Iterative enterprise security

2 LiveLab in this lesson — see the labs panel →

• Understanding privacy attacks
• Stealing models with model extraction attacks
• Defenses and mitigations
• The MLSecOps imperative
• Toward an MLSecOps 2.0 framework
• Building a primary MLSecOPs platform
• MLSecOps in action
• Integrating MLSecOps with LLMOps
• Advanced MLSecOps with SBOMs

3 LiveLab in this lesson — see the labs panel →