Red-Teaming AI Systems

An expert course on systematically attacking your own AI systems before others do. Learners design red-team campaigns, build reusable attack harnesses, and turn findings into fixes.

What this course covers

The course runs across 10 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

Adversarial Testing Methodology for AI — Establish a systematic framework for AI red-teaming, distinguishing it from traditional software pentesting and mapping the adversarial lifecycle.
Threat Modeling and Attack Surface Mapping — Identify vulnerability vectors across the AI pipeline, focusing on indirect prompt injection, data poisoning, and model extraction points.
Leveraging AI Attack Libraries — Explore and configure industry-standard AI red-teaming libraries (such as garak and PyRIT) to automate probe generation and vulnerability discovery.
Customizing Attack Payloads — Programmatically extend attack libraries to craft custom adversarial payloads tailored to bypass specific system prompts and safety classifiers.
Architecting a Reusable Attack Harness — Design the technical blueprint for an automated attack harness that programmatically interfaces with target model APIs to execute scalable campaigns.
Coding the Attack Harness Infrastructure — Build the core Python execution engine for your attack harness, handling API concurrency, rate-limiting, and raw payload injection.
Orchestrating Multi-Turn Jailbreak Campaigns — Develop stateful, multi-turn attack logic within your harness to execute complex conversational jailbreaks and exploit context-window vulnerabilities.
Automated Output Evaluation and Classification — Implement programmatic evaluators within the harness to automatically classify model responses as successful exploits, safe refusals, or partial leaks.
Vulnerability Triaging and Severity Reporting — Translate raw attack harness logs into structured vulnerability reports, scoring risks using AI-specific adaptations of CVSS.
Remediation: Implementing Guardrails and Fixes — Apply systemic fixes to the AI application, integrating input/output guardrail models and prompt-level mitigations to neutralize the discovered exploits.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick.

Who it is for

Security, AI QA, builders. The material is pitched at expert level, and takes roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Category: Safety Engineering track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including guided lab, code lab exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a code assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Also in this track

Guardrails & Safety Engineering — This course teaches the engineering of AI guardrails: layered filtering, policy enforcement, fail-safe design, and monitoring. Learners wrap an AI feature in defenses and prove they hold under pressure.
Prompt Injection Defense — A specialist course on the signature vulnerability of LLM systems. Learners study direct and indirect injection attacks, build layered defenses, and test them against realistic adversarial inputs.
AI Safety & Red-Teaming — Attack and defend AI systems professionally

Frequently asked questions

How long does this course take?

It runs roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code CAT-RT.