What do you think?
Rate this book
Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional's guide to AI attacks, threat modeling, and securing AI with MLSecOps
Understand how adversarial attacks work against predictive and generative AI, and learn how to safeguard AI 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 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 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 how GANs can be used for attacks and deepfakesDiscover how LLMs change security, including prompt injections and data exposureUnderstand privacy-preserving ML techniques and apply them using Keras and PyTorchExplore LLM threats with RAG, embeddings, and privacy attacksFind out how to poison LLMs by finetuning APIs or direct accessExamine model benchmarking and the challenges of open-access LLMsDiscover how to automate AI security using MLSecOps, including CI, MLOps, and SBOMs practicesWho this book is forThis 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 AI/MLBuilding our EnvironmentSecurity and AIAdvanced AI Adversarial AttacksModel Theft and TamperingPrivacy-Preserving MLPoisoning Attacks Transfer Learning and its security risksDefend Deployed EndpointsFool
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 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 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 how GANs can be used for attacks and deepfakesDiscover how LLMs change security, including prompt injections and data exposureUnderstand privacy-preserving ML techniques and apply them using Keras and PyTorchExplore LLM threats with RAG, embeddings, and privacy attacksFind out how to poison LLMs by finetuning APIs or direct accessExamine model benchmarking and the challenges of open-access LLMsDiscover how to automate AI security using MLSecOps, including CI, MLOps, and SBOMs practicesWho this book is forThis 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 AI/MLBuilding our EnvironmentSecurity and AIAdvanced AI Adversarial AttacksModel Theft and TamperingPrivacy-Preserving MLPoisoning Attacks Transfer Learning and its security risksDefend Deployed EndpointsFool
586 pages, Paperback
Published July 26, 2024
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 of 1 review
Read
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