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

A brief introduction to LLMs

LLMs are a subset of artificial intelligence (AI) that are designed to process, understand, and generate human language. These models employ deep learning techniques and are trained on extensive text data to understand context, generate coherent text, and interact in human-like conversation. As mentioned in the previous chapter, LLMs resulted from the evolutionary journey of NLP in AI. This journey had some key milestones:

  • Early stages of NLP: Initially, NLP models were rule-based, relying on manually crafted language rules. The advent of machine learning brought statistical models that learned from data. These models, however, were limited by their inability to understand language contextually.
  • Breakthrough with Transformer models: The introduction of Transformer models in 2017 marked a significant evolution. As discussed in the previous chapter, using “attention mechanisms,” these models could focus on different parts of input...
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 €18.99/month. Cancel anytime