Does this call an LLM?

No. The stress tester generates the adversarial test inputs locally in your browser — it does not run them. You take the generated inputs and run them against your own model so you control cost and data.

Why test adversarially before shipping?

The happy path always works in a demo. Real users — and attackers — send empty strings, giant pastes, injection payloads, and nonsense. Probing those before launch is how you find the prompt's brittle spots while it is cheap to fix.

Will higher risk levels change the tests?

Yes. At higher risk levels the suite leans harder on injection, data-exfiltration, and safety-bypass cases, because those are the failures that actually hurt in a customer-facing or money-moving feature.

What do I do when a test fails?

Tighten the prompt — add explicit scope boundaries, restate the output format, instruct the model to ignore embedded instructions, and define a refusal/fallback. Then re-run the suite until the failures stop.

Prompt Stress Tester

Q: What kinds of failures does it probe?

Empty and overlong input, prompt injection and instruction override, format-breaking input, multilingual and encoding edge cases, ambiguous or contradictory requests, out-of-scope questions, and attempts to extract the system prompt.

Prompt stress tester

A prompt that works on your three test inputs is not a prompt that works in production. Real input is empty, enormous, multilingual, contradictory, and occasionally hostile — and a prompt that has never seen those will break in ways you only discover from angry users. This tool generates 10 adversarial and edge-case inputs tailored to your prompt’s purpose and risk level, each paired with the specific failure it is designed to expose, so you can harden the prompt before you ship it.

How it works

You paste your prompt, describe its intended use, and pick a risk level. The tool assembles a test suite across the categories that break LLM features most often: empty and overlong input, prompt injection and instruction override, format-breaking content, multilingual and encoding edge cases, ambiguous and contradictory requests, out-of-scope questions, and system-prompt extraction attempts. Higher risk levels weight the suite toward injection and data-exfiltration cases. Each test states the failure to watch for. The tool generates the inputs locally and never runs them — you take them to your own model, which keeps both cost and data in your hands. Copy individual tests or the whole suite.

Tips and example

Run the whole suite, not the easy ones. The injection and extraction tests are the point; skipping them defeats the exercise.
Watch for the named failure. Each test tells you what “fail” looks like — a leaked system prompt, broken JSON, an answer to an out-of-scope question.
Fix by constraining, not by adding examples. Most failures are cured by an explicit scope boundary, a restated output format, and an instruction to ignore embedded commands.
Re-run after every prompt change. Hardening one case often loosens another; the suite is cheap to re-run.