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Mastering LLM Security
Defending Enterprise AI Systems Against Prompt Injection, Jailbreak Attacks, Model Inversion, and Adversarial Exploits
This book is 100% completeLast updated on 2026-06-05
Artificial Intelligence is transforming the world—but it is also creating an entirely new attack surface. What happens when attackers manipulate AI through language itself? Discover how cybercriminals exploit prompt injections, jailbreak models, steal sensitive information, and bypass AI guardrails. Through real-world examples, hands-on labs, secure coding techniques, and enterprise-ready…
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About the Book
Mastering LLM Security is a practical guide for cybersecurity professionals, AI engineers, security architects, and technology leaders seeking to secure Large Language Model (LLM) applications in production environments. The book explores modern AI attack surfaces, including prompt injection, jailbreaking, data extraction, model inversion, and denial-of-wallet attacks. It provides hands-on labs, secure architecture blueprints, Docker-based implementations, FastAPI security gateways, guardrail engineering techniques, and enterprise defense strategies. Readers will learn how to design, deploy, and maintain resilient AI systems capable of withstanding real-world adversarial threats while maintaining compliance, privacy, and operational integrity.
Author
About the Author
Aurobinda Ojha
Aurobinda Ojha is a Principal Cybersecurity Researcher, AI Systems Architect, and security leader with more than 25 years of experience in cybersecurity, penetration testing, enterprise security architecture, and emerging technology research.
Throughout his career, he has led security assessments, vulnerability management initiatives, application security programs, API security testing, cloud security reviews, and enterprise risk assessments across diverse technology environments. His expertise spans Web Application Security, Mobile Application Security, API Security, Red Team Operations, Threat Intelligence, Secure Software Development, and Security Automation.
Aurobinda is actively involved in advancing the field of Artificial Intelligence Security, specializing in Large Language Model (LLM) Security, Agentic AI Security, Adversarial Machine Learning, AI Guardrails, Autonomous Security Systems, and AI-driven Cyber Defense Architectures. His research focuses on securing intelligent systems against prompt injection, jailbreaking, model inversion, adversarial attacks, and emerging AI threats.
As the founder of AI Spider Lab, he is dedicated to developing innovative security frameworks, practical research methodologies, and enterprise-grade defensive solutions that help organizations securely adopt next-generation AI technologies.
His current work includes Agentic AI-driven cybersecurity platforms, automated vulnerability assessment systems, Red Team AI frameworks, SIGINT-enabled intelligence platforms, OT/ICS security research, and quantum-aware security architectures.
Aurobinda is passionate about knowledge sharing and regularly contributes to technical publications, research initiatives, training programs, and cybersecurity innovation projects aimed at strengthening the global security community.
Areas of Expertise
- Artificial Intelligence Security (AI Security)
- Large Language Model (LLM) Security
- Agentic AI & Autonomous Security Systems
- Adversarial Machine Learning
- Cybersecurity Research & Innovation
- Penetration Testing & Vulnerability Assessment
- API Security Testing
- Red Team Operations
- Threat Intelligence & SIGINT
- Cloud & Enterprise Security Architecture
- OT/ICS Cybersecurity
- Security Automation & Orchestration
- Secure Software Development
- Quantum-Aware Cybersecurity
Mission
"To build secure, intelligent, and resilient AI-powered systems that empower organizations to innovate confidently in an increasingly automated world."
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