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Securing Autonomous Agents: Practical Defenses for LLM-Based Systems Prompt Injection, Memory Poisoning, Tool Orchestration & Red Teaming
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Publisher: Independently Published
Pages: 200
Weight: 0.78
Height: 0.42 Width: 7.00 Depth: 10.00
Language: English
Inside, you'll discover how to:
- Model real-world threats for autonomous LLMs using frameworks like STRIDE, ATT&CK, and AI-specific taxonomies.
- Defend against prompt injection, memory poisoning, role escalation, and adversarial inputs that exploit agent reasoning.
- Design schema-bound prompts, gated tool orchestration, and multi-step safety checks to prevent plan drift and rogue actions.
- Secure agent memory with integrity validation, anomaly detection, and write-controls to block hallucinated recall.
- Embed layered defenses inside the reasoning loop with policy enforcement, intent modeling, and safety critics.
- Apply adversarial red teaming and continuous simulation to test and harden agent behavior before deployment.
- Align with compliance standards including NIST AI RMF, OWASP GenAI Top 10, ISO/IEC 42001, and the EU AI Act.
The future of AI isn't just about smarter models-it's about secure and resilient agents. Start building them today.
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