F.R.A.N.K monogram — AI red team sidekick markF.R.A.N.KAI Red Team Sidekick

LLM Red Teaming

LLM Red Teaming, Without The Babysitter

LLM red teaming is prompt engineering at the limit. Bring the attempts, responses, refusals, and traces — F.R.A.N.K helps turn pressure into proof.

Use F.R.A.N.K when LLM red teaming hits the messy middle: contradictory refusals, partial bypasses, tool misuse, retrieval drift, or unclear severity.

Brief

Bring rough work. Leave with direction.

F.R.A.N.K keeps the useful parts in view: the prompt, the evidence, the question, and the next move.

  1. 01

    Reads prompt traces, refusals, and partial completions in context.

  2. 02

    Helps separate model issues, system prompt leaks, retrieval poisoning, and tool misuse.

  3. 03

    Turns LLM red teaming findings into scope, severity, and retest language that reads cleanly.

Use It For This

Bring the stuck point. Leave with the next move.

Start in Discord
01

Paste the attempt and the trace

Bring jailbreak prompts, refusals, partial bypasses, tool outputs, RAG context, and the behavior pattern you're chasing.

02

Find the layer that bent

Was it the system prompt? The retrieval? Tool scoping? Output filter? F.R.A.N.K helps isolate which layer of the LLM stack actually moved.

03

Turn pressure into proof

Leave with scoped findings, retest steps, severity language, and remediation angles that hand off cleanly to engineering or policy.

Questions

Operator briefing — LLM Red Teaming.

01What's the difference between LLM red teaming and prompt injection testing?

Prompt injection testing is one tactic inside LLM red teaming. LLM red teaming is the broader practice — guardrail evaluation, jailbreaking, tool misuse, retrieval poisoning, agent abuse, and policy stress-testing.

02Can F.R.A.N.K help with LLM red teaming for production apps?

Yes — paste prompts, traces, retrieved context, tool calls, and observed behavior. F.R.A.N.K helps trace which layer (model, system prompt, retrieval, tool, filter) actually moved, and how to retest cleanly.

03Does F.R.A.N.K know about specific models?

F.R.A.N.K stays model-agnostic in its reasoning so the work doesn't rot when a new model lands. It reads the prompt and response you bring it, not the brand on the model card.