AI Security Engineering - Modeling - Detecting - Mitigating New Vulnerabilities

AI Security Engineering - Modeling - Detecting - Mitigating New Vulnerabilities

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Adversarial Goals

12 of 19

12 of 19

Adversarial Goals

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Classroom Contents

AI Security Engineering - Modeling - Detecting - Mitigating New Vulnerabilities

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  1. 1 Intro
  2. 2 Customer Compromise via Adversarial ML-Case Study
  3. 3 Higher Order Bias/Fairness, Physical Safety & Reliability concerns stem from unmitigated Security and Privacy Threats
  4. 4 Adversarial Audio Examples
  5. 5 Failure Modes in Machine Learning
  6. 6 Adversarial Attack Classification
  7. 7 Data Poisoning: Attacking Model Availability
  8. 8 Data Poisoning: Attacking Model Integrity
  9. 9 Poisoning Model Integrity: Attack Example
  10. 10 Proactive Defenses
  11. 11 Threat Taxonomy
  12. 12 Adversarial Goals
  13. 13 A Race Between Attacks and Defenses
  14. 14 Ideal Provable Defense
  15. 15 Build upon the Details: Security Best Practices
  16. 16 Define lower/upper bounds of data input and output
  17. 17 Threat Modeling Al/ML Systems and Dependencies
  18. 18 Wrapping Up
  19. 19 AI/ML Pivots to the SDL Bug Bar

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