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Adversarial AI Attacks, Mitigations, and Defense Strategies

You're reading from Adversarial AI Attacks, Mitigations, and Defense Strategies A cybersecurity professional's guide to AI attacks, threat modeling, and securing AI with MLSecOps

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781835087985

Length 586 pages

Edition 1st Edition

Languages

Python

Tools

DeepFakes

Concepts

Artificial Intelligence

Author (1):

John Sotiropoulos

View More author details

Table of Contents (27) Chapters

Preface

1. Part 1: Introduction to Adversarial AI FREE CHAPTER

2. Chapter 1: Getting Started with AI

3. Chapter 2: Building Our Adversarial Playground

4. Chapter 3: Security and Adversarial AI

5. Part 2: Model Development Attacks

6. Chapter 4: Poisoning Attacks

7. Chapter 5: Model Tampering with Trojan Horses and Model Reprogramming

8. Chapter 6: Supply Chain Attacks and Adversarial AI

9. Part 3: Attacks on Deployed AI

10. Chapter 7: Evasion Attacks against Deployed AI

11. Chapter 8: Privacy Attacks – Stealing Models

12. Chapter 9: Privacy Attacks – Stealing Data

13. Chapter 10: Privacy-Preserving AI

14. Part 4: Generative AI and Adversarial Attacks

15. Chapter 11: Generative AI – A New Frontier

16. Chapter 12: Weaponizing GANs for Deepfakes and Adversarial Attacks

17. Chapter 13: LLM Foundations for Adversarial AI

18. Chapter 14: Adversarial Attacks with Prompts

19. Chapter 15: Poisoning Attacks and LLMs

20. Chapter 16: Advanced Generative AI Scenarios

21. Part 5: Secure-by-Design AI and MLSecOps

22. Chapter 17: Secure by Design and Trustworthy AI

23. Chapter 18: AI Security with MLSecOps

24. Chapter 19: Maturing AI Security

25. Index

Why subscribe?

26. Other Books You May Enjoy

Summary

In this chapter, we moved beyond individual threats and attacks and looked at integrating them into a systemic and risk-based approach to building SbD AI.

We discussed how the interplay of security with safety and ethics becomes essential in AI to deliver Trustworthy AI.

Secure-by-design relies on two important conveyor belts that help us deliver, scale, and mature security: MLSecOPs and AI governance. In the next chapter, we will delve into MLSecOps.

The rest of the chapter is locked

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Authors (1)

John Sotiropoulos

John Sotiropoulos

John Sotiropoulos is a senior security architect at Kainos where he is responsible for AI security and works to secure national-scale systems in government, regulators, and healthcare. John has gained extensive experience in building and securing systems in roles such as developer, CTO, VP of engineering, and chief architect. A co-lead of the OWASP Top 10 for Large Language Model (LLM) Applications and a core member of the AI Exchange, John leads standards alignment for both projects with other standards organizations and national cybersecurity agencies. He is the OWASP lead at the US AI Safety Institute Consortium. An avid geek and marathon runner, he is passionate about enabling builders and defenders to create a safer future.

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