Bring the prompt and the trace
Paste the prompt, response, retrieved text, tool call, log excerpt, or app behavior that shows the injection risk.
F.R.A.N.KAI Red Team SidekickPrompt Injection Testing
Prompt Injection Testing For Real AI Attack Surfaces
Prompt injection testing is where LLM security gets practical: system prompt pressure, tool misuse, RAG poisoning, indirect instructions, data leakage, and policy confusion.
Paste the prompt, response, log, or app behavior. F.R.A.N.K helps turn the signal into a finding, fix path, or better test.
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
Turns prompt injection questions into repeatable test ideas.
- 02
Helps read signals from logs, prompts, responses, and tool behavior.
- 03
Connects prompt injection findings to fixes, validation criteria, and better prompts.
Use It For This
Bring the stuck point. Leave with the next move.
Find the control gap
Trace whether the issue is scope, retrieval, tool permissions, instruction handling, output filtering, or validation.
Hand engineering a clean fix path
Turn the observation into a readable finding with impact, reproduction context, guardrail advice, and retest criteria.
Questions
Operator briefing — Prompt Injection Testing.
01What is indirect prompt injection?
Instructions slipped into content the model retrieves or reads — pages, documents, emails, tool outputs — rather than the user message. The model treats them as instructions because nothing distinguished them.
02How is prompt injection different from jailbreaking?
Jailbreaking targets the model's policy. Prompt injection targets the application around the model — system prompts, retrieval, tools, and the boundary between instructions and data.
03Does F.R.A.N.K help write injection tests?
F.R.A.N.K helps shape test ideas from observed behavior and connect findings to remediation. It is not a payload library.