AI-Native LLM Security

Vaibhav Malik is a security leader with over 14 years of experience in industry. He partners with global technology leaders to architect and deploy comprehensive security solutions for enterprise clients worldwide. As a recognized thought leader in Zero Trust Security Architecture, Vaibhav brings deep expertise from previous roles at leading service providers and security companies, where he guided Fortune 500 organizations through complex network, security, and cloud transformation initiatives.Vaibhav champions an identity and data-centric approach to cybersecurity and is a frequent speaker at industry conferences. He holds a Master's degree in Networking from the University of Colorado Boulder, an MBA from the University of Illinois Urbana-Champaign, and maintains his CISSP certification.His extensive hands-on experience and strategic vision make him a trusted advisor for organizations navigating today's evolving threat landscape and implementing modern security architectures.Ken Huang is a prolific author and renowned expert in AI and Web3, with numerous published books spanning business and technical guides as well as cutting-edge research. He is a Research Fellow and Co-Chair of the AI Safety Working Groups at the Cloud Security Alliance, Co-Chair of the OWASP AIVSS project, and Co-Chair of the AI STR Working Group at the World Digital Technology Academy. He is also an Adjunct Professor at the University of San Francisco, where he teaches a graduate course on Generative AI for Data Security. Huang serves as CEO and Chief AI Officer (CAIO) of DistributedApps.ai, a firm specializing in generative AI-related training and consulting. His technical leadership is further reflected in his role as a core contributor to OWASP's Top 10 Risks for LLM Applications and his participation in the NIST Generative AI Public Working Group.A globally sought-after speaker, Ken has presented at events hosted by RSA, OWASP, ISC2, Davos WEF, ACM, IEEE, Consensus, the CSA AI Summit, the Depository Trust & Clearing Corporation, and the World Bank. He is also a member of the OpenAI Forum, contributing to global dialogue on secure and responsible AI development.Ads Dawson is a self-described “meticulous dude” who lives by the philosophy: Harness code to conjure creative chaos—think evil; do good. He is a recognized expert in offensive AI security, specializing in adversarial machine learning exploitation and autonomous red teaming, with a talent for demonstrating capabilities in offensive security focused tasks using agents. As Staff AI Security Researcher at Dreadnode and founding Technical Lead for the OWASP LLM Applications Project, he architects next-gen evaluation harnesses for cyber operations and AI red teaming. Located in Toronto, Canada and an avid bug bounty hunter, he bridges traditional AppSec with cutting-edge AI vulnerability research, positioning him among the few experts capable of conducting full-spectrum adversarial assessments across AI-integrated critical systems.

• Table of ContentsFundamentals and Introduction to Large Language ModelsSecuring Large Language ModelsThe Dual Nature of LLM Risks: Inherent Vulnerabilities and Malicious ActorsMapping Trust Boundaries in LLM ArchitecturesAligning LLM Security with Organizational Objectives and Regulatory LandscapesIdentifying and Prioritizing LLM Security Risks with OWASPDiving Deep: Profiles of the Top 10 LLM Security RisksMitigating LLM Risks: Strategies and Techniques for Each OWASP CategoryAdapting the OWASP Top 10 to Diverse Deployment ScenariosDesigning LLM Systems for Security: Architecture, Controls, and Best PracticesIntegrating Security into the LLM Development Life Cycle: From Data Curation to DeploymentOperational Resilience: Monitoring, Incident Response, and Continuous ImprovementThe Future of LLM Security: Emerging Threats, Promising Defenses, and the Path ForwardAppendix AAppendix B