What Is AI Red Teaming?

What Is Red Teaming?

Red teaming is the practice of attacking your own system on purpose to improve safety. A red team plays the adversary by looking for the gap between what your policy says and what your product actually allows, then hands you the findings before that gap becomes a public failure.

The term comes from military and cybersecurity exercises, where a red team simulates an attacker and a blue team defends. AI red teaming applies that same adversarial approach to models instead of networks. Instead of probing for a way into your servers, the red team probes for ways to make your model produce, allow, or assist with something it should not.

What is a red team?

A red team is a group, internal or external, whose job is to think like an attacker, not a user. They do not test whether your product works as intended. They test what happens when someone actively tries to break it by bypassing a filter, jailbreaking a model, or laundering a harmful request through an innocuous-looking prompt.

Quality red teaming does not stop at generic harm categories. It maps findings to a harm taxonomy, distinguishes between harmful behavior that was requested directly and harmful behavior that emerged indirectly, then gives your team labeled evidence it can act on. Red teaming improves the safety of your model before it is released.

What is AI red teaming?

AI red teaming is adversarial testing aimed specifically at a model: a foundation model, a fine-tune, a generative product, or an agent. The targets include:

• Jailbreaks and prompt injection that get a model to ignore its own guardrails
• Attempts to elicit NSFW, CSAM-related, NCII, extremist, or otherwise policy-violating content
• Failures that only show up at scale, not in a small internal eval set
• System prompt and refusal-behavior failures under adversarial pressure

Why use an external red team?

Interest in AI red teaming is growing with new regulatory requirements and public scrutiny. External red teaming validates these requirements before a model is released. Inviting a third party to red team improves trust with users and regulators alike. Scoped red teaming engagements can help identify policy-violating behavior quickly and produce findings that are more useful than generic safety evaluations alone.

Why does red teaming matter more once weights are public?

Standard evaluations and benchmarks can help identify egregious known issues. Red teaming helps find the unknown unknowns in models. These are the harms that real adversaries will quickly discover after release. They will look for the prompts, formats, and edge cases your eval set missed.

That gap is easier to close before launch. It becomes much harder once a model's weights are public, or once a release is being scrutinized by an enterprise customer's procurement team, a journalist, or a regulator.

What's the difference between red teaming and penetration testing?

Red teaming and penetration testing both describe efforts to break a system with the intention of making it stronger. The difference is the target. Penetration testing probes infrastructure: networks, applications, endpoints. AI red teaming probes model behavior: what the model will say, generate, refuse, or do when someone is actively trying to misbehave.

A red team engagement for a generative AI product will usually focus less on network configuration and more on outputs under adversarial pressure: jailbreaks, prompt injection, policy evasion, refusal failures, multimodal edge cases, and safety regressions between model versions.

What does an AI red teaming engagement look like?

At Cinder, AI red teaming means structured adversarial evaluation across model checkpoints. Cinder scopes a red team engagement around specific policy needs, such as NCII and CBRNE. Domain experts then use our understanding of harms and red teaming tactics to build a set of attack prompts to run against your model. Expert human reviewers classify outputs against a detailed harm taxonomy, and findings get mapped back to a policy, workflow, eval, or agent. Customers can use those findings to improve their model and its safeguards.

For Black Forest Labs, Cinder red-teamed FLUX.2 across iterative model cycles before release, testing for CSAM and NCII vulnerabilities across text-to-image and image-to-image attacks. By the end of the engagement, Black Forest Labs reported a greater than 90% reduction in CSAM and NCII vulnerability and more than 10x fewer vulnerabilities than benchmark open-weight models.

For Krea, Cinder evaluated two full model checkpoints and moved from kickoff to final delivery in 8 days. The model came back under 1% on NSFW metrics before it went public, giving the team a concrete safety readout before launch.

Red teaming gives product, policy, and model teams the evidence they need to improve the system before the release is public.

Cinder Red Teaming FAQs

Is red teaming the same as a security audit?

No. A security audit generally checks known vulnerabilities against a checklist. Red teaming assumes there is no complete checklist for what a creative adversary will try next, so it simulates that adversary directly.

Who needs AI red teaming?

Foundation model labs, generative AI products across image, video, chat, and voice, and any platform shipping a new safety policy or entering enterprise procurement where customers will ask for proof before signing.

How long does an engagement take?

It depends on scope. Cinder's Black Forest Labs work included rapid red-team cycles with turnaround times under 48 hours, while the Krea engagement went from kickoff to final delivery in 8 days and evaluated two full model checkpoints.

Is red teaming a one-time thing?

It should not be. Models and policies change with every release. Red teaming needs to run at the cadence of your ship cycle, not once a year.