Adversarial AI Attacks, Mitigations, and Defense Strategies: A Cybersecurity professional's guide to AI attacks, threat modeling, and securing AI with MLSecOps by John Sotiropoulos
English | July 26, 2024 | ISBN: 1835087981 | 586 pages | EPUB | 27 Mb
Understand how adversarial attacks work against predictive and generative AI, and learn how to safeguard AI and LLM projects with practical examples leveraging OWASP, MITRE, and NIST
Key FeaturesUnderstand the connection between AI and security by learning about adversarial AI attacksDiscover the latest security challenges in adversarial AI by examining GenAI, deepfakes, and LLMsImplement secure-by-design methods and threat modeling, using standards and MLSecOps to safeguard AI systemsPurchase of the print or Kindle book includes a free PDF eBookBook 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.
What you will learnUnderstand poisoning, evasion, and privacy attacks and how to mitigate themDiscover how GANs can be used for attacks and deepfakesExplore how LLMs change security, prompt injections, and data exposureMaster techniques to poison LLMs with RAG, embeddings, and fine-tuningExplore supply-chain threats and the challenges of open-access LLMsImplement MLSecOps with CIs, MLOps, and SBOMsWho this book is 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.
Table of ContentsGetting Started with AIBuilding Our Adversarial PlaygroundSecurity and Adversarial AIPoisoning AttacksModel Tampering with Trojan Horses and Model ReprogrammingSupply Chain Attacks and Adversarial AIEvasion Attacks against Deployed AIPrivacy Attacks - Stealing ModelsPrivacy Attacks - Stealing DataPrivacy-Preserving AIGenerative AI - A New FrontierWeaponizing GANs for Deepfakes and Adversarial AttacksLLM Foundations for Adversarial AIAdversarial Attacks with PromptsPoisoning Attacks and LLMsAdvanced Generative AI Scenarios(N.B. Please use the Read Sample option to see further chapters)
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