Adversarial AI attacks present a unique set of security challenges, exploiting the very foundation of how AI learns. This book explores these threats in depth, equipping cybersecurity professionals with the tools needed to secure generative AI and LLM applications. Rather than skimming the surface of emerging risks, it focuses on practical strategies, industry standards, and recent research to build a robust defense framework.
Structured around actionable insights, the chapters introduce a secure-by-design methodology, integrating threat modeling and MLSecOps practices to fortify AI systems. You’ll discover how to leverage established taxonomies from OWASP, NIST, and MITRE to identify and mitigate vulnerabilities. Through real-world examples, the book highlights best practices for incorporating security controls into AI development life cycles, covering key areas like CI/CD, MLOps, and open-access LLMs.
Built on the expertise of its co-authors—pioneers in the OWASP Top 10 for LLM applications—this guide also addresses the ethical implications of AI security, contributing to the broader conversation on Trustworthy AI. By the end of this book, you’ll be able to develop, deploy, and secure AI technologies with confidence and clarity.

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Spis treści

AI Native LLM Security. A comprehensive guide to leveraging OWASP Top 10 for LLM applications and beyond eBook -- spis treści

• 1. Introduction to Large Language Models and AI Security
• 2. Securing Large Language Models in Practice
• 3. The Dual Nature of LLM Risks: Inherent Vulnerabilities and Malicious Actors
• 4. Key Trust Boundaries and Attack Surfaces in LLM Systems
• 5. Aligning LLM Security with Organizational Objectives and Regulatory Landscapes
• 6. Identifying and Prioritizing LLM Security Risks with OWASP
• 7. Diving Deep: Profiles of the Top 10 LLM Security Risks
• 8. Mitigating LLM Risks: Strategies and Techniques for Each OWASP Category
• 9. Adapting the OWASP Top 10 to Diverse LLM Use Cases and Deployment Scenarios
• 10. Designing LLM Systems for Security: Architecture, Controls, and Best Practices
• 11. Integrating Security into the LLM Development Lifecycle: From Data Curation to Deployment
• 12. Operational Resilience: Monitoring, Incident Response, and Continuous Improvement
• 13. The Future of LLM Security: Emerging Threats, Promising Defenses, and the Path Forward