Bring the full trace
Paste prompts, outputs, retrieved text, tool calls, policy behavior, notes, screenshots, or report fragments that need order.
F.R.A.N.KAI Red Team SidekickLLM 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.
- 01
Keeps prompts, logs, model behavior, and evidence tied to the testing objective.
- 02
Helps separate model issues, app issues, retrieval issues, and workflow issues.
- 03
Turns rough observations into finding language, retest criteria, and fix direction.
Use It For This
Bring the stuck point. Leave with the next move.
Find the security signal
Clarify whether the issue sits in prompt handling, data exposure, guardrail behavior, tool scope, memory, or output quality.
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?
AI red teaming is the broader adversarial practice. LLM security testing tends to focus on systematic vulnerability evaluation across prompts, guardrails, retrieval, and tools — closer to traditional appsec discipline applied to LLM stacks.
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.