Overview

In the rapidly evolving landscape of artificial intelligence, autonomous LLM agents are transforming how systems reason, act, recall, and interact with the world. But with this autonomy comes unprecedented security challenges: adversarial prompt injections, memory poisoning, plan drift, feedback loop exploits, self-modifying behaviors, and multimodal vulnerabilities that traditional security measures can't address. Secure Agentic AI is your essential blueprint for building and fortifying next-generation autonomous agents that are not only intelligent but also robust against real-world threats. This comprehensive guide delves into agent-specific threat modeling using frameworks like STRIDE, schema-bound prompt controls, gated tool orchestration, role conditioning, memory integrity safeguards, anomaly scoring, safety critics, intent modeling, policy enforcement, red teaming, adversarial testing, continuous threat simulation, and alignment with standards such as NIST AI RMF, OWASP GenAI Top 10, and the EU AI Act. Whether you're an AI engineer, security architect, DevSecOps professional, or ethical AI specialist, you'll gain actionable strategies to mitigate risks in complex reasoning agents, tool-integrated workflows, adaptive learning systems, and enterprise deployments. Equip yourself with the knowledge to create autonomous LLM agents that prioritize safety, trustworthiness, resilience, and compliance in an era of escalating AI threats ensuring your innovations remain secure and reliable.

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