Agentic AI Security: Designing and Protecting Autonomous LLM Agents with Advanced Threat Models, Prompt Engineering, and Memory Safeguards
Language: English
Published by Independently published, 2025
- Softcover
- Used

Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
AbeBooks seller since January 28, 2020
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- Title
- Agentic AI Security: Designing and Protecting Autonomous LLM Agents with Advanced Threat Models, Prompt Engineering, and Memory Safeguards
- Author
- Ming, Andrew
- Publisher
- Independently published
- Publication year
- 2025
- Condition
- As New
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798270171551
This comprehensive book provides AI engineers, security architects, DevSecOps professionals, and responsible AI practitioners with a robust framework to safeguard autonomous LLM agents. Across eight expertly crafted chapters, you’ll explore how to mitigate risks like prompt injection, memory poisoning, feedback loop attacks, and self-modifying agent behaviors. Learn to design secure agent architectures, implement layered defenses, and align with emerging compliance standards to ensure your systems are both powerful and trustworthy.
Inside, you’ll discover how to:
- Develop agent-specific threat models using STRIDE and other frameworks tailored for autonomous systems.
- Engineer schema-bound prompts and gated tool orchestration to prevent intent drift and unauthorized actions.
- Implement memory integrity checks, anomaly detection, and write controls to secure agent recall and persistence.
- Embed safety critics, intent modeling, and policy enforcement within the agent’s reasoning loop for real-time protection.
- Conduct red teaming, adversarial testing, and continuous threat simulation to proactively harden agent deployments.
- Navigate compliance with NIST AI RMF, OWASP GenAI Top 10, and the EU AI Act for enterprise-grade, auditable AI systems.
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GreatBookPricesUK
Woodford Green, United Kingdom
AbeBooks seller since January 28, 2020
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