Securing Multi Agentic AI Systems

The course "Securing Multi-Agentic AI Systems" offers a deep, structured exploration into the evolving field of agent-based artificial intelligence and the critical security challenges it presents. It begins with foundational insights into the structure, autonomy, and behavioral models of Multi-Agent Systems (MAS), followed by an examination of how these agents coordinate, negotiate, and discover peers within distributed environments. The course then delves into the unique security implications of MAS—including trust boundaries, non-deterministic behavior, and identity challenges—before transitioning into applied threat scenarios defined by the OWASP Agentic AI Threat Framework. Learners investigate specific threats such as identity spoofing, tool misuse, and memory poisoning, and assess how these manifest in real-world MAS failures.

Central to the course is the MAESTRO framework, a layered approach to agentic threat modeling. Participants learn to map vulnerabilities across model, memory, orchestration, tooling, and infrastructure layers, identifying emergent behavior and cross-layer exploits. Specialized modules focus on model drift, prompt injection, RAG vector poisoning, plugin hijacks, and service abuse. Through case studies—including an RPA Expense Agent —students engage in hands-on risk discovery, simulation of cascading failures, and red-teaming of autonomous agents.

The latter part of the course emphasizes detection and defense. Learners design telemetry systems, integrate real-time threat intelligence, and align MAESTRO with MITRE ATT&CK and ATLAS for enterprise-ready threat fusion. Finally, architectural modules guide students through fail-safe design patterns, agent isolation strategies, and the implementation of Zero Trust principles across agent workflows. Whether you’re securing LLM-based agents or blockchain-integrated agents, this course equips professionals with practical skills and strategic models to defend the next generation of autonomous systems.