Paste the attempt and the trace
Bring jailbreak prompts, refusals, partial bypasses, tool outputs, RAG context, and the behavior pattern you're chasing.
F.R.A.N.KAI Red Team SidekickLLM Red Teaming
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
F.R.A.N.K keeps the useful parts in view: the prompt, the evidence, the question, and the next move.
Reads prompt traces, refusals, and partial completions in context.
Helps separate model issues, system prompt leaks, retrieval poisoning, and tool misuse.
Turns LLM red teaming findings into scope, severity, and retest language that reads cleanly.
Use It For This
Bring jailbreak prompts, refusals, partial bypasses, tool outputs, RAG context, and the behavior pattern you're chasing.
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.
Leave with scoped findings, retest steps, severity language, and remediation angles that hand off cleanly to engineering or policy.
Questions
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.
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.
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.