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Adversarial AI Attacks, Mitigations, and Defense Strategies
Adversarial AI Attacks, Mitigations, and Defense Strategies

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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Adversarial AI Attacks, Mitigations, and Defense Strategies

Part 1: Introduction to Adversarial AI

In this part, you will get an overview of AI, cybersecurity, and adversarial AI. You will learn the fundamental concepts and terms you need to know to embark on your journey of mastering adversarial AI and AI security. This will cover algorithms, models, model development and deployment, and inference APIs. We will set up our environment and create our first sample AI solution, which we will use later in the book. We will also cover cybersecurity fundaments and how to apply them to our sample solution, including vulnerability and code scanning, while demonstrating our first adversarial attack on our sample AI service.

This part has the following chapters:

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Key benefits

  • Understand the connection between AI and security by learning about adversarial AI attacks
  • Discover the latest security challenges in adversarial AI by examining GenAI, deepfakes, and LLMs
  • Implement secure-by-design methods and threat modeling, using standards and MLSecOps to safeguard AI systems
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

Adversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips cybersecurity professionals with the skills to secure AI technologies, moving beyond research hype or business-as-usual strategies. The strategy-based book is a comprehensive guide to AI security, presenting a structured approach with practical examples to identify and counter adversarial attacks. This book goes beyond a random selection of threats and consolidates recent research and industry standards, incorporating taxonomies from MITRE, NIST, and OWASP. Next, a dedicated section introduces a secure-by-design AI strategy with threat modeling to demonstrate risk-based defenses and strategies, focusing on integrating MLSecOps and LLMOps into security systems. To gain deeper insights, you’ll cover examples of incorporating CI, MLOps, and security controls, including open-access LLMs and ML SBOMs. Based on the classic NIST pillars, the book provides a blueprint for maturing enterprise AI security, discussing the role of AI security in safety and ethics as part of Trustworthy AI. By the end of this book, you’ll be able to develop, deploy, and secure AI systems effectively.

Who is this book for?

This book tackles AI security from both angles - offense and defense. AI builders (developers and engineers) will learn how to create secure systems, while cybersecurity professionals, such as security architects, analysts, engineers, ethical hackers, penetration testers, and incident responders will discover methods to combat threats and mitigate risks posed by attackers. The book also provides a secure-by-design approach for leaders to build AI with security in mind. To get the most out of this book, you’ll need a basic understanding of security, ML concepts, and Python.

What you will learn

  • Understand poisoning, evasion, and privacy attacks and how to mitigate them
  • Discover how GANs can be used for attacks and deepfakes
  • Explore how LLMs change security, prompt injections, and data exposure
  • Master techniques to poison LLMs with RAG, embeddings, and fine-tuning
  • Explore supply-chain threats and the challenges of open-access LLMs
  • Implement MLSecOps with CIs, MLOps, and SBOMs
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 26, 2024
Length: 586 pages
Edition : 1st
Language : English
ISBN-13 : 9781835087985
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Length: 586 pages
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ISBN-13 : 9781835087985
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Table of Contents

26 Chapters
Part 1: Introduction to Adversarial AI Chevron down icon Chevron up icon
Chapter 1: Getting Started with AI Chevron down icon Chevron up icon
Chapter 2: Building Our Adversarial Playground Chevron down icon Chevron up icon
Chapter 3: Security and Adversarial AI Chevron down icon Chevron up icon
Part 2: Model Development Attacks Chevron down icon Chevron up icon
Chapter 4: Poisoning Attacks Chevron down icon Chevron up icon
Chapter 5: Model Tampering with Trojan Horses and Model Reprogramming Chevron down icon Chevron up icon
Chapter 6: Supply Chain Attacks and Adversarial AI Chevron down icon Chevron up icon
Part 3: Attacks on Deployed AI Chevron down icon Chevron up icon
Chapter 7: Evasion Attacks against Deployed AI Chevron down icon Chevron up icon
Chapter 8: Privacy Attacks – Stealing Models Chevron down icon Chevron up icon
Chapter 9: Privacy Attacks – Stealing Data Chevron down icon Chevron up icon
Chapter 10: Privacy-Preserving AI Chevron down icon Chevron up icon
Part 4: Generative AI and Adversarial Attacks Chevron down icon Chevron up icon
Chapter 11: Generative AI – A New Frontier Chevron down icon Chevron up icon
Chapter 12: Weaponizing GANs for Deepfakes and Adversarial Attacks Chevron down icon Chevron up icon
Chapter 13: LLM Foundations for Adversarial AI Chevron down icon Chevron up icon
Chapter 14: Adversarial Attacks with Prompts Chevron down icon Chevron up icon
Chapter 15: Poisoning Attacks and LLMs Chevron down icon Chevron up icon
Chapter 16: Advanced Generative AI Scenarios Chevron down icon Chevron up icon
Part 5: Secure-by-Design AI and MLSecOps Chevron down icon Chevron up icon
Chapter 17: Secure by Design and Trustworthy AI Chevron down icon Chevron up icon
Chapter 18: AI Security with MLSecOps Chevron down icon Chevron up icon
Chapter 19: Maturing AI Security Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
(13 Ratings)
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4 star 7.7%
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Dwayne Natwick Sep 02, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I recently received a copy of @Packt Publishing’s Adversarial AI Attacks, Mitigations, and Defense Strategies from @John Sotiropoulos. This book outlines that various types of AI attacks along with the anatomy and design of these attacks. The author provides information on the architecture and setup that can be used for detailed analysis, mitigation, and defense of these attacks. There is also a complete section on generative AI and how it is used for a new level of attacks. Throughout this book, the author has done a great job of developing understanding and knowledge for the reader about these attacks, how they are created, and how you can protect your environment against them. This book is highly recommended for anyone that is looking for a level of understanding about how to architect, monitor, and defend against AI attacks.
Amazon Verified review Amazon
T J Coup Aug 01, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A highly necessary book in the field, this comprehensive guide to AI security offers a structured understanding of key issues, complete with hands-on examples. A must-read for all IT professionals this summer.
Amazon Verified review Amazon
Matthew Kiely Oct 28, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is an essential read for anyone looking to deepen their understanding of adversarial AI.It goes beyond merely explaining how these attacks operate, it shows you how to set up a test environment to simulate these attacks and observe their impact on machine learning models.It’s indepth and not for the faint hearted!The hands-on approach allows you to see how adversarial techniques can corrupt AI systems.It is a well-rounded resource for both aspiring and seasoned AI professionals
Amazon Verified review Amazon
Andy Aug 29, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The author really knows his stuff and lays it out in a very approachable way. The writing and graphics are good, and the layout is very logical. That said, you better have solid AI design and cybersecurity in your recent past. Sample code for setting up the environment and defenses against the top AI attacks for predictive and generativeAI environments. Guidance is provided for DevSecOps, MLOps, and LLMOps, so you can build security in from the planning stage or apply mitigation strategies to environments already in operation. I love that he provides reference architecture diagrams that include the potential attacks (the snipped graphic on this review is from the book) and on which part of the architecture different attacks focus.The book is just under 600 pages, there isn’t any fluff, and it is very hands-on. It hits multiple audiences with various role focuses. That said, it is more like an encyclopedia for teams involved in AI at a company than it is a book for an individual, one that should be read end-to-end and then referenced as needed. Nice job.
Amazon Verified review Amazon
Brandon Lachterman Sep 06, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I really enjoyed this book, and it will be a fixture on my virtual shelf for reference. In a subject just getting more traction, this book gets down to brass tax and covers the subjects any reader is looking for, while leaving out the extra fluff. Very technical, but clear to understand. Highly recommend.
Amazon Verified review Amazon
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