Product guide

Adversarial AI Attacks, Mitigations, and Defense Strategies

Adversarial AI Attacks, Mitigations, and Defense Strategies is a Packt cybersecurity guide covering ways AI systems can be attacked and defended. It pairs attack walkthroughs with countermeasures and secure-by-design topics, including threat modeling and MLSecOps. The listing says readers should have basic security, machine-learning, and Python knowledge.

At a glance

Explains attacks including poisoning, evasion, model tampering, supply-chain threats, model or data theft, and prompt injection.
Describes hands-on adversarial attack exploration alongside defensive measures.
Covers threat modeling, secure-by-design methods, DevSecOps, MLOps, and MLSecOps.
Includes generative AI and LLM topics such as prompt injection and poisoning of embeddings in RAG and fine-tuning.

Price

$26.91Save 55%

Reference price: ~~$59.99~~

Price checked: Nov 14, 2026

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Pros and cons

Pros

Treats offensive techniques and defensive mitigations together, making the guide relevant to both threat analysis and secure system design.
Its scope spans model development, deployed AI, and generative AI attack scenarios.
The listing outlines practical exercises in an adversarial playground as well as frameworks and enterprise security guidance.

Cons

The listing assumes readers already understand basic security and ML concepts and have basic Python knowledge.

Technical specifications

8 specifications

Publication details
Publisher	Packt Publishing
Publication date	July 26, 2024
Language	English
Print length	586 pages
ISBN-10	1835087981
ISBN-13	978-1835087985

Dimensions and installation
Weight listed	2.25 pounds
Dimensions	7.5 x 1.36 x 9.25 inches

Who is it for?

A good fit for

Cybersecurity professionals, including security architects, analysts, engineers, penetration testers, ethical hackers, and incident responders.
AI developers and engineers seeking to consider security during model and system development.
Security leaders looking for threat-modeling and secure-by-design guidance for AI.

Less suited to

Readers seeking a beginner introduction to cybersecurity, machine learning, or Python without prior foundational knowledge.
Readers looking for a narrow reference on only LLM prompt injection or only AI governance, since the described scope covers multiple attack categories and defenses.

Customer reviews on Amazon

4.6 / 5 · 58 ratings

5 stars76%76%
4 stars17%17%
3 stars5%5%
2 stars0%0%
1 star2%2%

Aggregate ratings lean positive, with most ratings in the five-star category and a smaller share in the one- to three-star categories. Ratings alone do not explain why readers responded as they did or establish how well the book's attack walkthroughs and defenses work in practice. Consider the stated technical prerequisites and broad subject coverage when judging its fit.

Frequently asked questions

What topics does the book cover?

The listing describes poisoning, trojan horses, evasion, supply-chain and privacy attacks, model and data theft, GAN misuse, prompt injection, and defenses including threat modeling and MLSecOps.

What background does the listing recommend?

It says readers should have a basic understanding of security, ML concepts, and Python.

Does the listing describe practical exercises?

Yes. It says readers can set up an adversarial playground to explore attacks and examine countermeasures.

Is a digital edition included?

The listing claims a DRM-free PDF and access to Packt's Reader, with email sign-up and proof of purchase required.