AI red teaming and safety data. Attacks only a specialist would think of.
A generic jailbreak list finds generic failures. The dangerous ones are domain-specific: a plausible dosage, a confident misreading of a statute, a code suggestion with a hole in it. eeko puts credential-checked experts against your model inside an agreed scope, labels every attempt against your harm taxonomy, and writes the safe answer alongside the failure.
What is AI red teaming?
AI red teaming is the deliberate attempt to make a model do something its policy forbids, carried out by people whose job is to find the failure before a user does. The output is not a pass or fail. It is a labeled record of every attempt: what was tried, in which category, how severe the result was, whether the model held, over-refused, or complied, and why an expert judged it that way.
Most red-teaming data is written by generalists working from the same public jailbreak patterns the model was already trained to resist. The failures that cause real harm look different. They are clinically plausible, legally authoritative, chemically specific, or technically subtle, and a non-specialist cannot tell a dangerous answer from a careful one. The people who can are practitioners.
eeko tests the models inside our own deployments this way before they reach a regulated client, because the controls we build for compliance and governance programs are only as good as the testing behind them. The safety data practice applies the same discipline to your model: expert attackers, an agreed taxonomy, controlled handling, and a safe completion written for every failure that matters.
Adversarial prompt datasets and the answers that should have been given.
A campaign produces evaluation data and training data at once. Six pieces of work, scoped together and priced before anything is attempted.
- Multi-turn escalation transcripts
- Outcome recorded per attempt
- Agreed in writing before collection
- Versioned if it changes mid-campaign
- Three-way outcome plus severity
- Rationale and expert ID on each label
- Paired with the failing output
- Ready for preference or SFT pipelines
- Professional-context prompts by domain
- Labeled for expected behavior
- Out-of-scope material excluded, not delivered
The failures a generic jailbreak list will never find.
Each field has its own line between a careful answer and a dangerous one. Only someone who works on that line every day can see where the model crossed it.
Who attacks what
- Medicine and pharmacology — physicians and pharmacologists probing dosing, interactions, self-harm adjacent requests, and advice that sounds clinical and is not.
- Chemistry — chemists testing synthesis and hazard questions against the hard limits, under the tightest access controls in the program.
- Law — attorneys eliciting confident, wrong, or unauthorized legal guidance and testing refusals on legitimate professional questions.
- Finance — compliance specialists probing fraud, market abuse, and evasion framings, and the benign questions a model should still answer.
- Code and security — security engineers pushing for exploitable completions, malicious tooling, and quiet vulnerabilities in otherwise helpful code.
- Language — linguists testing whether the policy holds across languages, dialects, and transliteration tricks.
The hardest categories go only to experts whose profession covers them, and every expert opts in task by task after a content warning.
A red-team campaign, scope to signed bundle.
An illustrative two-week campaign against a clinical assistant across three taxonomy categories.
From policy document to safety dataset.
Fixed scope, fixed price, a pilot round before the full campaign. Repeat campaigns run against the same taxonomy so results compare over time.
LLM red teaming by industry and discipline.
Taxonomy, attackers, and handling rules all follow from where the model will be used.
AI red teaming questions.
How is an AI red teaming engagement scoped?
It begins with your usage policy and the risks you need to measure first. From those we agree a harm taxonomy, the domains in play, the attack styles permitted (single-turn, multi-turn escalation, role-play, indirect requests), and a written list of what is off limits entirely. That scope document determines the expert roster, the permitted techniques, and the point at which an expert stops and escalates. Nothing beyond it is collected, and a pilot round runs against it before any larger campaign.
How do you handle dangerous or unlawful material during red teaming?
Collection stays inside the agreed scope and runs in tasks visible only to the experts assigned to them. Every item passes a block-list hash check and a safety classifier, and anything past a hard line is pulled and escalated, never delivered. Personal data is masked before the bundle is signed. Experts opt in task by task after an explicit content warning, and the hardest categories go only to those whose profession covers them. Once the data reaches your systems, your own handling controls take over.
Do you deliver training data or only findings?
Both, from the same campaign. Every attack arrives with its category, severity, and outcome labels, and for the failures that matter the expert also writes the response the model should have given: a safe completion that still helps, or a refusal worded to your policy. Paired with the failing output, that gives you preference pairs, fine-tuning examples, and a held-out safety eval from one engagement.
Can red teaming results support our regulatory documentation?
They can serve as evidence. Findings arrive structured by taxonomy, with severity labels, expert IDs, and a signed record for every item, which is the shape most testing documentation asks for. eeko does not give legal advice and does not certify compliance with any framework; what the material proves, and whether it satisfies an obligation, is a question for your counsel. We are glad to work alongside them to shape the scope so the output fits.
How much does AI red teaming cost?
It follows the scoped plan. The drivers are which domains are in play and how senior the experts must be, single-turn versus multi-turn attacks, how deep the labeling goes, whether safe completions are written, and how sensitive the categories are. Specialist clinical or chemistry campaigns cost more than general-policy probing, and we quote a fixed price against the scope, run a pilot round first, and price larger campaigns on volume.
What is an over-refusal set?
A collection of benign prompts that sit close to a policy boundary and that a well-calibrated model should answer. A pharmacist asking about interaction thresholds, a penetration tester describing a known exploit class, a novelist researching poisons for a plot. Models tuned hard against harm start refusing these, and the refusal is a failure too. An over-refusal set lets you measure both directions of the boundary at once.
What is a harm taxonomy?
A structured list of the kinds of harm a model must not cause, each with a definition, severity levels, and decision rules for borderline cases. It is the scaffolding that turns red teaming from a pile of transcripts into labeled, countable data. We either adopt your existing policy categories or draft a taxonomy with your policy team before collection begins, and every attack and response is labeled against it, so findings can be compared across campaigns and over time.
Related data services.
Find the failure before a user does.
Five minutes to send a dataset request. Within one business day a data lead comes back with questions, a draft scope and taxonomy, and a price. No deck. A ranked list of what to attack first.
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