Threat Modeling for Agentic AI: Attacks, Risks, Controls

Modern AI systems are no longer passive language models. They plan, remember, use tools, and act autonomously.

And that changes everything about security.

Threat Modeling for Agentic AI is a deep, practical course dedicated to one critical reality: traditional threat modeling fails when applied to autonomous agents.

This course teaches you how to identify, analyze, and control risks that emerge only in agentic systems - risks caused by memory poisoning, unsafe tool usage, reasoning drift, privilege escalation, and multi step autonomous execution.

If you are building, reviewing, or securing AI agents, this course gives you the frameworks you cannot find in classical AppSec, cloud security, or LLM tutorials.

Why this course exists

Most AI security content focuses on:

• Prompt injection

• RAG data leaks

• Model hallucinations in isolation

This course focuses on what actually breaks real agentic systems:

• Persistent memory corruption

• Cascading reasoning failures

• Tool chains that trigger real world actions

• Agents escalating their own privileges over time

You will learn how agents fail as systems, not as single model calls.

What makes this course different

This is not a conceptual overview.
This is a system level security course built around real agent architectures.

You will learn:

• How autonomy expands the attack surface

• Why agent memory is a long term liability

• How small hallucinations turn into multi step failures

• Where classical threat models completely miss agent specific risks

Every concept is tied to artifacts, diagrams, templates, and exercises you can reuse in real projects.

What you will learn

By the end of the course, you will be able to:

• Threat model agentic systems end to end, not just individual components

• Identify memory poisoning vectors and design integrity controls

• Analyze unsafe tool invocation and high risk capability exposure

• Detect privilege drift and unsafe delegation inside agent workflows

• Trace cascading failures across planning loops and execution graphs

• Design strict policy and oversight layers for autonomous agents

You will not just understand the risks. You will know how to control them.

Course structure and learning approach

The course is structured as a progressive system analysis, moving from foundations to real failures.

You will work with:

• Agent reference architectures

• Threat surface maps

• Memory and tool security checklists

• Full agent threat model templates

• Incident reconstruction frameworks

Each module builds directly on the previous one, forming a complete mental model of agent security.

Hands on and practical by design

Throughout the course you will:

• Map threats across perception, reasoning, action, and update cycles

• Break down real agent failures step by step

• Identify root causes, escalation paths, and missed controls

• Design mitigations that actually work in production systems

This course treats agentic AI as critical infrastructure, not demos.

Who this course is for

This course is ideal for:

• Security engineers working with AI driven systems

• Software architects designing autonomous agents

• AI engineers building multi tool or multi agent workflows

• AppSec and cloud security professionals expanding into AI

• Technical leaders responsible for AI risk and governance

If you already understand basic LLMs and want to move into serious agent architecture and security, this course is for you.

Why you should start now

Agentic AI is being deployed faster than security models are evolving.
Teams are shipping autonomous systems without understanding how they fail.

This course gives you the missing frameworks before those failures happen in your own systems.

If you want to be ahead of the curve - not reacting to incidents, but preventing them - this is the course you have been waiting for.

Start now and learn how to secure autonomous AI before it secures itself in the wrong way.