AI Red Teaming for Beginners: What Testers Actually Do

AI red teaming is penetration testing for systems that use machine learning or large language models. The goal is the same as any security test: find weaknesses before an attacker does. Test only systems you own or have written permission to test.

What Gets Tested

• The model's behavior: can it be tricked into ignoring its instructions or revealing data? See prompt injection explained.
• The application around it: the web app, the API, the permissions the AI is given.
• The data pipeline: poisoned training data can change how a model behaves.
• AI agents: tools and actions an agent can take on a user's behalf.
• The infrastructure and supply chain: model files, libraries and hosting.

It Still Starts with Web and API Basics

Most AI systems are reached through a web application or API. Broken access control, injection and weak authentication still matter, and many AI flaws are ordinary application flaws with a new front end. OWASP Top 10 and API security come first.

A Safe Way to Practice

• Run an open model locally on your own machine, or use a deliberately vulnerable practice application.
• Write a system prompt with a rule, then try to get the model to break it. Record what worked.
• Give a test assistant a harmless tool and see whether crafted input makes it misuse the tool.
• Write each finding down: input, output, impact and a fix.

Skills to Build

• Web and API testing with Burp Suite.
• Basic Python for scripting tests.
• Report writing; see how to write a penetration testing report.

Where This Leads

EC-Council's COASP certification covers offensive AI security in 10 modules; see the COASP exam guide.

Sources and Further Reading

• OWASP Top 10 for LLM Applications