AI Red Teaming
Bring prompts, findings, model behavior, and stuck questions. The messy parts stay in one frame, then turn into direction you can defend.
Use it to sharpen scope, evidence, prompt pressure, report language, and the next pass.
Reviewed July 4, 2026
Operator Brief
Use this like a working session, not a reading assignment. Paste the trace, name the target, and turn the loose pieces into a next move you can defend.
What F.R.A.N.K keeps in view
Keeps prompts, responses, notes, and scope tied to the same objective.
Turns rough observations into evidence gaps, retest direction, and review language.
Names what still needs proof before the finding should move forward.
Input
Bring the prompt, the response, the expected behavior, the policy boundary, and any notes about what felt off. F.R.A.N.K treats the packet like evidence, not a vibe check.
A scoped behavior readout: what changed, which layer appears involved, what evidence is still missing, and what the next test should prove.
Pressure
If the first pass only produced a partial refusal, vague drift, or inconsistent behavior, F.R.A.N.K helps tighten the objective and strip away prompts that are not testing anything.
A cleaner retest plan with success criteria, comparison prompts, expected failure modes, and language a reviewer can follow.
Handoff
F.R.A.N.K turns rough AI red-team notes into a finding structure: scope, reproduction context, observed behavior, impact, fix direction, and retest criteria.
Report-ready wording that still sounds like an operator wrote it, not a template risk paragraph.
Actual Use
Paste the material you already have: model responses, guardrail observations, prompt attempts, rough notes, and the question still blocking the finding.
Turn scattered context into sharper scope, cleaner assumptions, useful severity language, and a next pass that makes sense.
Walk away with report wording, retest direction, prompt improvements, and the evidence still needed before review.
Questions
F.R.A.N.K reads the prompts, responses, and behavior notes you already have, then helps sharpen scope, evidence, severity language, and the next test. It rides with the operator instead of running the engagement.
No. F.R.A.N.K is a sidekick, not an assistant trying to drive. The operator stays in command. F.R.A.N.K sharpens the thinking and writing around the work.
They overlap, but LLM red teaming focuses on the language-model layer: prompts, guardrails, jailbreaks, and retrieval. AI red teaming is broader and can include classifiers, agents, multimodal systems, and the product around the model.