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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

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835087985
Length 586 pages
Edition 1st Edition
Languages
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Author (1):
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John Sotiropoulos John Sotiropoulos
Author Profile Icon John Sotiropoulos
John Sotiropoulos
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Toc

Table of Contents (27) Chapters Close

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 26. Other Books You May Enjoy

Summary

This concludes our introduction to using Generative AI for adversarial purposes. In this chapter, we focused on GANs and how they can be used in attacks in various scenarios, from traditional cybersecurity attacks to adversarial attacks on AI and, more importantly, deepfake attacks, one of the most challenging aspects of using Generative AI technologies such as GANs.

Then, we covered defenses and mitigations and recognized the need for a multi-pronged attack based on a specific solution’s risks and threat model. We also highlighted that mitigating deepfakes requires more than particular defenses, including ethical AI guidelines, legislation, and education.

GANs are primarily used for images. The next chapter will examine the use of prompt-based attacks with adversarial inputs in text-driven interactions with LLMs.

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