Engineering Secure AI Agents

Your AI agent can be hijacked with nothing but text. No malware, no exploit, no credentials required - just a few sentences embedded where your agent will read them. This is happening to production agents right now, and most engineering teams have no idea their defenses don't cover it.

This book Engineering Secure AI Agents: Prompt Injection Defense, Jailbreak Prevention, and Zero Trust for Autonomous Systems gives you the complete engineering architecture to stop it: prompt injection defense across all five attack vectors, jailbreak prevention that holds against automated adversarial generation, zero-trust identity for autonomous agents, and a production governance framework that ties it all together. Every chapter delivers working Python code, not just theory - guardrail pipelines, authorization policies, behavioral telemetry, and red-team test suites you can deploy the same day.

What sets this book apart from general AI agent guides is its narrow, complete focus: it does not teach you how to build agents - it assumes you already are. It teaches you how to keep the agents you've built from being turned against you, grounded in real documented incidents from 2025 and 2026, not theoretical scenarios.

By the end of this book, you will be able to:

- Classify prompt injection variants and map them to OWASP ASI categories
- Implement a five-layer prompt injection defense pipeline in Python
- Design zero-trust identity architecture with task-scoped credentials
- Build jailbreak detection using both classifier ensembles and architectural controls
- Conduct structured red-team exercises using real-world attack patterns
- Apply the SENTINEL framework to multi-agent system architectures
- Harden MCP server integrations against tool poisoning and supply-chain attacks
- Instrument agents with behavioral telemetry to detect compromise at runtime
- Create a governance policy aligned with OWASP Agentic Top 10 and NIST AI RMF
- Deploy production-ready security controls with audit logging and incident response

The book moves systematically from threat modeling through implementation to governance: Chapters 1-4 build the foundation of injection defense, Chapters 5-8 add jailbreak prevention and zero-trust identity, and Chapters 9-12 cover multi-agent trust, behavioral monitoring, red-teaming, and the SENTINEL production governance framework. Each chapter includes runnable code, a rapid-review table, and real incident case studies.

This book is for AI engineers, security engineers, DevSecOps practitioners, and technical architects who are deploying or maintaining LLM-based agents in production and need a systematic approach to securing them - not a patchwork of disconnected guardrails.

If you are responsible for an agent that reads documents, calls tools, or takes real-world actions, the architecture in this book is the difference between a security posture and a security story.