Adversarial AI Attacks, Mitigations, and Defense Strategies
Adversarial AI Attacks, Mitigations, and Defense Strategies
A cybersecurity professional's guide to AI attacks, threat modeling, and securing AI with MLSecOps
About the Book
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.
About the Author
Table of Contents
- Getting Started with AI
- Building Our Adversarial Playground
- Security and Adversarial AI
- Poisoning Attacks
- Model Tampering with Trojan Horses and Model Reprogramming
- Supply Chain Attacks and Adversarial AI
- Evasion Attacks against Deployed AI
- Privacy Attacks – Stealing Models
- Privacy Attacks – Stealing Data
- Privacy-Preserving AI
- Generative AI – A New Frontier
- Weaponizing GANs for Deepfakes and Adversarial Attacks
- LLM Foundations for Adversarial AI
- Adversarial Attacks with Prompts
- Poisoning Attacks and LLMs
- Advanced Generative AI Scenarios
- Secure by Design and Trustworthy AI
- AI Security with MLSecOps
- Maturing AI Security
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