Securing Multi Agentic AI Systems

Architecting Trust and Defense in Autonomous Multi Agentic Ecosystems

What you'll learn
- Understand the structural components, behaviors, and autonomy principles of Multi-Agent Systems (MAS)
- Analyze how agents communicate, negotiate, and discover each other securely across distributed environments
- Identify core security risks in MAS including identity spoofing, trust drift, and behavioral non-determinism
- Apply the OWASP Agentic AI Threat Taxonomy to detect and classify agentic vulnerabilities
- Perform layered threat modeling using the MAESTRO framework across model, memory, orchestration, tool, and transport layers
- Investigate advanced failure scenarios such as memory poisoning, vector store attacks, plugin hijacking, and privilege chaining
- Execute red-teaming simulations to test agentic systems for prompt injection, cascading logic failures, and attack chains
- Design resilient architectures that implement least privilege, runtime trust verification, and agent isolation
- Develop observability mechanisms with telemetry pipelines, HITL (human-in-the-loop) escalation, and log validation
- Integrate Zero Trust principles across agent design—ensuring continuous validation, dynamic policies, and scoped permissions
- Map MAS threats and behaviors to MITRE ATT&CK and MITRE ATLAS for standardized security alignment
- Build and secure agent workflows in enterprise and Web3 settings using real-world case study - RPA Expense Agents

Requirements
- Basic Understanding of Artificial Intelligence

Description
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.

Who this course is for:
- AI Engineers and Multi-Agent System Designers
- Cybersecurity Analysts and Threat Modelers
- Cloud Architects and DevSecOps Professionals
- RPA Developers and Automation Engineers
- Red Teamers and AI Security Researchers
- Technical Product Managers and Governance Leads
- Graduate Students and Researchers in AI Security
More Info