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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

Developing AI applications with LLMs

LLMs are complex, large-scale models, and are dominated by the ChatGPT models hosted by OpenAI. This has changed the paradigm of AI application development by shifting back to API-based access to an LLM and reducing machine learning development and deployment operations.

With LLMs, we reference these large models as foundational models. These are large-scale general-purpose models that have been pre-trained with vast amounts of web-scale data and are capable of thinking outside the box for a wide range of tasks. ChatGPT 3.5 and ChatGPT4 are well-known and popular foundational models hosted by OpenAI and are available for further training and fine-tuning via APIs. AI application developers interact with APIs without the burden (and flexibility) of MLOps. The following diagram summarizes a basic LLM application topology:

Figure 13.1 – Basic LLM application with the third-party model (for example, OpenAI)

Figure 13.1 – Basic LLM application with the third-party model (for example, OpenAI)

Note...

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