Learning Adversarial AI 2026
is a practical guide to advanced artificial intelligence security, focusing on modern attacks, defenses, and real-world adversarial machine learning risks.
This volume explores evasion attacks, poisoning attacks, model extraction, membership inference, advanced defense mechanisms, red teaming autonomous AI agents, multimodal adversarial AI, and future AI security challenges. It is written for students, cybersecurity learners, AI developers, machine learning practitioners, and technology enthusiasts who want to understand how AI systems can be attacked and how they can be protected.
Inside this book, readers will learn about Fast Gradient Sign Method, Projected Gradient Descent, Carlini & Wagner attacks, universal adversarial perturbations, physical-world patches, data poisoning, clean-label poisoning, federated learning attacks, neural backdoors, model stealing, membership inference, adversarial training, randomized smoothing, prompt injection, RAG poisoning, autonomous agent risks, multimodal backdoors, AI watermarking, cryptographic machine learning, and AI security governance.
This book uses structured chapters, diagrams, practical explanations, tables, case studies, and comprehension questions to help readers build a strong foundation in adversarial AI security.
Readers will learn:
• How attackers manipulate AI models during training and inference
• How adversarial examples, poisoning, and backdoors affect production systems
• How model extraction and membership inference threaten privacy
• How to evaluate AI robustness using industry-standard attack methods
• How to defend AI systems using adversarial training, detection, privacy, and secure deployment strategies
• How red teams test LLM agents, RAG systems, and multimodal AI applications
A useful resource for anyone studying AI security, machine learning defense, cybersecurity, or responsible AI system design
