a hands-on course

Secure Your
AI Agent

(AI Agent Security)

The agent you built can be hijacked by the text it reads. Learn to see the attacks and shut them down — in Python, in about half an hour.

A free, hands-on tutorial on securing AI agents. The agent you built earlier acts on text it reads — and that text can be attacker-controlled. See prompt injection hijack it live, then defend it: least privilege, path confinement, human-in-the-loop approvals, host-side secrets, sandboxing and audit logging. The third course in the agent arc, after Build an AI Agent and Build an MCP Server.

in the real world

Every team putting agents into production in 2026 is wrestling with exactly this — prompt injection, secrets handling, and how much to let an agent do unattended. These are the defenses that separate a demo from something you can safely point at real data and real tools.

Start with the threat model →

Part 1 — How agents get attacked

1

Why agents are a new risk

An agent acts on text it reads — and that text can be written by an attacker. Meet the 'lethal trifecta'.

2

Prompt injection, live

Hide instructions in a file the agent reads, and watch it obey the attacker instead of you. The #1 agent threat.

Part 2 — Defend it

3

Least privilege

The best defense is a small blast radius: narrow tools, not a shell; allow-lists, not deny-lists; confine every path.

4

Put a human in the loop

Some actions should never run unattended. Tier tools by risk and require a person to approve the dangerous ones.

Part 3 — Ship it safely

5

Secrets & sandboxing

Keep credentials where the agent can never read them, run tools in a box, and cut off the exfiltration route.

6

Verify, log & ship

Validate every input, record every action, and follow the checklist that lets you deploy an agent you can trust.