Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional's guide to AI attacks, threat modeling, and securing AI with MLSecOps

“The book not only explains how adversarial attacks work but also shows you how to build your own test environment and run attacks to see how they can corrupt ML models. It's a comprehensive guide that walks you through the technical details and then flips to show you how to defend against these very same attacks.” – Elaine Doyle, VP and Cybersecurity Architect, Salesforce Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Understand the unique security challenges presented by predictive and generative AI Explore common adversarial attack strategies as well as emerging threats such as prompt injection Mitigate the risks of attack on your AI system with threat modeling and secure-by-design methods Book DescriptionAdversarial 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 you with the skills to secure AI technologies, moving beyond research hype or business-as-usual activities. Learn how to defend AI and LLM systems against manipulation and intrusion through adversarial attacks such as poisoning, trojan horses, and model extraction, leveraging DevSecOps, MLOps, and other methods to secure systems. This strategy-based book is a comprehensive guide to AI security, combining structured frameworks with practical examples to help you identify and counter adversarial attacks. Part 1 introduces the foundations of AI and adversarial attacks. Parts 2, 3, and 4 cover key attack types, showing how each is performed and how to defend against them. Part 5 presents secure-by-design AI strategies, including threat modeling, MLSecOps, and guidance aligned with OWASP and NIST. The book concludes with a blueprint for maturing enterprise AI security based on NIST pillars, addressing ethics and safety under Trustworthy AI. By the end of this book, you’ll be able to develop, deploy, and secure AI systems against the threat of adversarial attacks effectively.What you will learn Set up a playground to explore how adversarial attacks work Discover how AI models can be poisoned and what you can do to prevent this Learn about the use of trojan horses to tamper with and reprogram models Understand supply chain risks Examine how your models or data can be stolen in privacy attacks See how GANs are weaponized for Deepfake creation and cyberattacks Explore emerging LLM-specific attacks, such as prompt injection Leverage DevSecOps, MLOps and MLSecOps to secure your AI system Who this book is forThis book tackles AI security from both angles - offense and defence. AI 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 to AI and mitigate the 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.

Table of Contents:
Table of Contents Getting Started with AI Building Our Adversarial Playground Security and Adversarial AI Poisoning Attacks Model Tampering with Trojan Horses and Model Reprogramming Supply Chain Attacks and Adversarial AI Evasion Attacks against Deployed AI Privacy Attacks – Stealing Models Privacy Attacks – Stealing Data Privacy-Preserving AI Generative AI – A New Frontier Weaponizing GANs for Deepfakes and Adversarial Attacks LLM Foundations for Adversarial AI Adversarial Attacks with Prompts Poisoning Attacks and LLMs Advanced Generative AI Scenarios Secure by Design and Trustworthy AI AI Security with MLSecOps Maturing AI Security