AI Native LLM Security. A comprehensive guide to leveraging OWASP Top 10 for LLM applications and beyond Vaibhav Malik, Ken Huang, Adam Dawson

AI Native LLM Security. A comprehensive guide to leveraging OWASP Top 10 for LLM applications and beyond Vaibhav Malik, Ken Huang, Adam Dawson - okladka książki

Autorzy:: Vaibhav Malik, Ken Huang, Adam Dawson
Serie wydawnicze:: Hands-on
Wydawnictwo:: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
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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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O autorach książki

Vaibhav Malik is a cybersecurity expert with over 12 years of experience in networking and security. As a Partner Solutions Architect at Cloudflare, he designs and implements effective security solutions for global partners. Vaibhav is a recognized industry thought leader in Zero Trust Security Architecture and holds an M.S. in Telecommunications from the University of Colorado Boulder and an M.B.A. from the University of Illinois Urbana-Champaign. His extensive expertise in AI security and his practical experience in designing scalable AI infrastructure make him uniquely qualified to guide readers through the complex landscape of LLM security.

Ken Huang is a renowned AI expert, serving as co-chair of AI Safety Working Groups at Cloud Security Alliance and the AI STR Working Group at World Digital Technology Academy under the UN Framework. As CEO of DistributedApps, he provides specialized GenAI consulting.
A key contributor to OWASP's Top 10 Risks for LLM Applications and NIST's Generative AI Working Group, Huang has authored influential books including Beyond AI (Springer, 2023), Generative AI Security (Springer, 2024), and Agentic AI: Theories and Practice (Springer, 2025)
He's a global speaker at prestigious events such as Davos WEF, ACM, IEEE, and RSAC. Huang is also a member of the OpenAI Forum and project leader for the OWASP AI Vulnerability Scoring System project.

Ads Dawson is a seasoned AI Full-Stack Red Teamer and Staff AI Security Researcher at Dreadnode, boasting extensive expertise in red teaming, ethical hacking, application security engineering, and architecture, particularly in NLP security. As the Technical Lead and founder of the OWASP Top 10 for LLM Applications project and contributing to the MITRE CWE AI Working Group, Ads has played a pivotal role in shaping the industry's benchmarks for LLM security best practices. Committed to fostering hands-on learning and practical application, Ads is dedicated to empowering readers to identify and effectively mitigate LLM security risks within authentic, real-world scenarios.

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Szczegóły książki

Tytuł oryginału:: AI Native LLM Security. A comprehensive guide to leveraging OWASP Top 10 for LLM applications and beyond
ISBN Ebooka:: 978-18-362-0374-2, 9781836203742
Data wydania ebooka :: 2025-06-27 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@ebookpoint.pl.
Język publikacji:: angielski

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Serie wydawnicze: Hands-on

Kategorie

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Popularnonaukowe i akademickie » Matematyka
Informatyka » Hacking » Kryptografia
Informatyka » Hacking » Inne
Informatyka » Sztuczna inteligencja
Informatyka » Biznes IT » Big data » Uczenie maszynowe

Spis treści książki

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

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