Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835087985
Length 586 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
John Sotiropoulos John Sotiropoulos
Author Profile Icon John Sotiropoulos
John Sotiropoulos
Arrow right icon
View More author details
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

Advanced Generative AI Scenarios

In the previous chapter, we examined in detail how large language models (LLMs) change the attack vectors for poisoning. This is based on the paradigm shift toward external model hosting and access via APIs. However, this is changing, and open source or open-access models are becoming increasingly viable options. This chapter will explore the supply-chain risks third-party LLMs bring, especially with regard to model poisoning and tampering. New fine-tuning techniques, including model merges and model adapters, make these advanced scenarios that we need to understand.

Similarly, the LLM shift has redefined privacy adversarial attacks such as model inversion, influence, and model extraction, making them advanced attack scenarios, too. We will complete our exploration of advanced generative AI (GenAI) scenarios by walking through privacy attacks and LLMs. We will cover the following topics:

  • Supply-chain attacks with open-access models
  • Privacy...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime