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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
Free with your DRM-free PDF version + access to Packt's next-gen Reader*
Key FeaturesUnderstand the unique security challenges presented by predictive and generative AIExplore common adversarial attack strategies as well as emerging threats such as prompt injectionMitigate the risks of attack on your AI system with threat modeling and secure-by-design methodsBook 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. 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 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.
*Email sign-up and proof of purchase required
What you will learnSet up a playground to explore how adversarial attacks workDiscover how AI models can be poisoned and what you can do to prevent thisLearn about the use of trojan horses to tamper with and reprogram modelsUnderstand supply chain risksExamine how your models or data can be stolen in privacy attacksSee how GANs are weaponized for Deepfake creation and cyberattacksExplore emerging LLM-specific attacks, such as prompt injectionLeverage DevSecOps, MLOps and MLSecOps to secure your AI systemWho 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.
– Elaine Doyle, VP and Cybersecurity Architect, Salesforce
Free with your DRM-free PDF version + access to Packt's next-gen Reader*
Key FeaturesUnderstand the unique security challenges presented by predictive and generative AIExplore common adversarial attack strategies as well as emerging threats such as prompt injectionMitigate the risks of attack on your AI system with threat modeling and secure-by-design methodsBook 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. 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 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.
*Email sign-up and proof of purchase required
What you will learnSet up a playground to explore how adversarial attacks workDiscover how AI models can be poisoned and what you can do to prevent thisLearn about the use of trojan horses to tamper with and reprogram modelsUnderstand supply chain risksExamine how your models or data can be stolen in privacy attacksSee how GANs are weaponized for Deepfake creation and cyberattacksExplore emerging LLM-specific attacks, such as prompt injectionLeverage DevSecOps, MLOps and MLSecOps to secure your AI systemWho 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.
975 pages, Kindle Edition
Published July 26, 2024
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September 8, 2025This book is unusual.
On the one hand, it can be genuinely off-putting while reading, yet I still felt the urge to come back to it. This is not a Stockholm syndrome effect – it’s the value it actually delivers. But let’s get to the point.
At the beginning, we encounter the introduction – and it’s quite strange. On one hand, it touches on the topic of AI, but it does so in a very brief and slogan-like way. It feels as if it’s written only for people who already have solid knowledge in the field. At times, it even resembles listening to a friend who just wants to show off how many complex terms he knows.
Here another peculiarity of the book becomes visible: translating technical terms into Polish. Initially, it’s done quite well – alongside the Polish equivalents the English originals are also provided. However, later on only the Polish versions are used, which makes reading harder because it requires constantly mapping them back to their English sources.
It’s clear that the book was written by someone very technical. It’s not an easy read, yet despite that I always wanted to return to it.
There are very few books on the market that focus on more sophisticated AI attacks. Most of them stop at simpler threats such as Prompt Injection or Unbounded Consumption. And that’s no surprise – they’re easy to imagine, much like the good old SQL Injection or DoS. This book, however, goes further and focuses on less-known, more complex attacks, which often require advanced tools and/or higher mathematics. In this area, it presents an impressive depth of knowledge.
The structure is quite systematic – for each attack we get a description, its types, industry examples, and methods for replication. Each one also comes with a reference to the original research paper.
That’s why I treat this book as a kind of lexicon of AI attacks, built on top of academic research. It’s an excellent resource both for learning and for coming back to when I realize I might use these techniques (in testing, not offense 😉). Its structured nature is actually a strong advantage here.
The same goes for the source code – during the initial read it’s not essential, but when diving deeper into a specific topic, it becomes very useful. A minor drawback is that sometimes the code samples are impossible to analyze without checking the full version on GitHub. Fortunately, that option is available, so the printed snippets can be treated as commentary to the repository. Another inconvenience is that the illustrations were originally in color – converted to grayscale, they are much harder to read and interpret.
In summary: this is a very valuable book, though written in a demanding way. It requires a significant entry barrier but gives a lot of knowledge in return. It’s hard to find another book in this field packed with so much content.
On the one hand, it can be genuinely off-putting while reading, yet I still felt the urge to come back to it. This is not a Stockholm syndrome effect – it’s the value it actually delivers. But let’s get to the point.
At the beginning, we encounter the introduction – and it’s quite strange. On one hand, it touches on the topic of AI, but it does so in a very brief and slogan-like way. It feels as if it’s written only for people who already have solid knowledge in the field. At times, it even resembles listening to a friend who just wants to show off how many complex terms he knows.
Here another peculiarity of the book becomes visible: translating technical terms into Polish. Initially, it’s done quite well – alongside the Polish equivalents the English originals are also provided. However, later on only the Polish versions are used, which makes reading harder because it requires constantly mapping them back to their English sources.
It’s clear that the book was written by someone very technical. It’s not an easy read, yet despite that I always wanted to return to it.
There are very few books on the market that focus on more sophisticated AI attacks. Most of them stop at simpler threats such as Prompt Injection or Unbounded Consumption. And that’s no surprise – they’re easy to imagine, much like the good old SQL Injection or DoS. This book, however, goes further and focuses on less-known, more complex attacks, which often require advanced tools and/or higher mathematics. In this area, it presents an impressive depth of knowledge.
The structure is quite systematic – for each attack we get a description, its types, industry examples, and methods for replication. Each one also comes with a reference to the original research paper.
That’s why I treat this book as a kind of lexicon of AI attacks, built on top of academic research. It’s an excellent resource both for learning and for coming back to when I realize I might use these techniques (in testing, not offense 😉). Its structured nature is actually a strong advantage here.
The same goes for the source code – during the initial read it’s not essential, but when diving deeper into a specific topic, it becomes very useful. A minor drawback is that sometimes the code samples are impossible to analyze without checking the full version on GitHub. Fortunately, that option is available, so the printed snippets can be treated as commentary to the repository. Another inconvenience is that the illustrations were originally in color – converted to grayscale, they are much harder to read and interpret.
In summary: this is a very valuable book, though written in a demanding way. It requires a significant entry barrier but gives a lot of knowledge in return. It’s hard to find another book in this field packed with so much content.
Displaying 1 of 1 review