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

LLM Security Testing

LLM Security Testing With Direction

LLM security work gets messy fast: prompts, traces, refusals, tool calls, retrieved context, and behavior notes all compete for attention.

Bring the context. F.R.A.N.K helps turn it into sharper tests, cleaner findings, and next steps that hold shape.

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

    Keeps prompts, logs, model behavior, and evidence tied to the testing objective.

  2. 02

    Helps separate model issues, app issues, retrieval issues, and workflow issues.

  3. 03

    Turns rough observations into finding language, retest criteria, and fix direction.

Use It For This

How a session with F.R.A.N.K actually runs.

Start in Discord
01

Bring the full trace

Paste prompts, outputs, retrieved text, tool calls, policy behavior, notes, screenshots, or report fragments that need order.

02

Find the security signal

Clarify whether the issue sits in prompt handling, data exposure, guardrail behavior, tool scope, memory, or output quality.

03

Move toward review-ready work

Turn the raw material into clearer findings, validation steps, impact language, and remediation notes.

Questions

Operator briefing — LLM Security Testing.

01Is LLM security testing the same as AI red teaming?

Red teaming asks 'can an adversary make this misbehave?'. LLM security testing asks 'where, how often, and is it fixed yet?' — closer to appsec discipline applied to the LLM stack.

02What can I paste into F.R.A.N.K for LLM security work?

Prompts, responses, refusals, retrieved context, tool calls, log excerpts, screenshots, behavior notes, and rough finding drafts. F.R.A.N.K helps sort them into structured findings.

03Does this cover GenAI agent security?

Yes — agent behavior, tool scope, memory leakage, and instruction-handling failures all fit the same workflow.