AI Red Teaming: A Practical Guide for Security Leaders

AI red teaming is the practice of deliberately attacking an AI system — prompting, probing, and manipulating it — to find failures before adversaries do. Unlike traditional pentesting, which targets networks and code, AI red teaming targets model behavior: jailbreaks that bypass safety guardrails, prompt injection that hijacks instructions, training-data extraction, and harmful or biased outputs. The goal is to produce a concrete list of exploitable weaknesses and the mitigations that close them.

What red teaming in AI actually tests

A useful AI red team exercise covers a defined threat taxonomy rather than random poking. The categories that matter most for a language or agentic model:

• Jailbreaks — role-play, obfuscation, or multi-turn attacks that get the model to ignore its safety policy.
• Prompt injection — malicious instructions hidden in retrieved documents, web pages, or user data that override system prompts. This is the top risk in the OWASP Top 10 for LLM Applications.
• Sensitive data disclosure — extracting training data, system prompts, API keys, or PII the model shouldn't reveal.
• Excessive agency — an agent taking unauthorized actions (sending emails, executing code, moving funds) because a tool or permission was over-scoped.
• Harmful content and bias — outputs that are toxic, discriminatory, or provide dangerous capability uplift.

Document your findings against the model's stated boundaries. This is where model cards — introduced by Mitchell et al. (2019) at Google and now an industry-standard documentation format — become the red teamer's baseline. A model card records intended use, performance benchmarks, ethical considerations, and known limitations. If the model behaves outside its declared intended use, you have a finding, not a debate.

Which AI and tools are best for red teaming

There is no single "best AI for red teaming" — you use a mix of attacking models and purpose-built frameworks. Practical stack:

• PyRIT (Microsoft's Python Risk Identification Toolkit) — automates adversarial prompt generation and scoring across attack objectives.
• Garak — an open-source LLM vulnerability scanner that runs hundreds of probes for jailbreaks, prompt injection, and data leakage out of the box.
• Promptfoo — good for regression-testing prompts and building a repeatable red-team suite in CI.
• An uncensored or less-restricted model as the attacker — you want a model willing to generate adversarial payloads to probe your target model. Pair it with a strong "judge" model (like a frontier GPT or Claude) to score whether an attack succeeded.

For skills, the popular training paths are worth naming: HTB (Hack The Box) ships AI red teaming labs and challenges, and OffSec has been expanding into AI attack content alongside its established offensive-security certs. An AI red teaming course from either is a reasonable on-ramp if your team is coming from a traditional pentest background.

What agentic AI red teaming adds

Agentic AI red teaming targets systems that don't just answer — they act. An agent with tools (a browser, a code interpreter, a database connector, a payment API) has a far larger attack surface than a chat model. New failure modes appear:

• Tool poisoning — an attacker manipulates a tool's output so the agent chains into a harmful action.
• Chained prompt injection — a single injected instruction propagates across multiple agent steps and sub-agents.
• Goal hijacking — the agent's objective is subtly redirected mid-task.

Test agents in a sandboxed environment with real (but disposable) tool access, because injection that looks harmless in a chat window becomes a data exfiltration or remote-execution path once the model can actually call functions. Apply least privilege to every tool and require human confirmation for irreversible actions.

How much do AI red teamers get paid, and how to start

Compensation tracks senior offensive-security and ML-security roles: in the US market, dedicated AI red team and AI security engineer positions commonly land in the roughly $150,000–$250,000+ total-comp range at major AI labs and large enterprises, with frontier-lab specialists at the top end. Demand is outpacing supply, which is why the traditional pentest and ML-engineering talent pools are both being recruited into it.

To run your first exercise: (1) pull the model card and list intended use and stated limitations; (2) pick a threat taxonomy — OWASP LLM Top 10 is a solid default; (3) run automated probes with Garak or PyRIT for coverage; (4) add manual, creative multi-turn attacks where automation is weak; (5) score results with a judge model and log every successful attack as a ticket with a mitigation. Then re-run the suite on every model or prompt change — red teaming is a regression test, not a one-off.

If you're scoping AI risk before a red team