QAtration

[!CAUTION] Do not point this at a system you do not own. It sends real attacks — prompt injection, data exfiltration, tool abuse — at whatever URL you give it. Use it on your own deployment, or on one whose owner has given you written permission in advance. Not a public chatbot you find interesting. Not a vendor's demo. Not to "just check" somebody else's product. AUTHORISED-USE.md says what that means in practice, and what to do if you find something in a system that is not yours.

QA + [pene]tration — adversarial testing for LLM features and agents.

qatration.com · what it catches · the evidence

Point it at a bot, fire a library of attacks (prompt injection, system-prompt leak, agent tool-abuse, SQL-injection-via-agent), and get an objective verdict per attack, because the oracle checks planted canaries and tool-call arguments rather than vibes. Built to answer the question no classic test suite can: our AI feature shipped — what can it be talked into doing?

Apache 2.0. Run it yourself. The engine is meant to run where the system under test is: on your machine, in your CI, against your own deployment. Nothing here needs a service, and out/ ships with the repository, so every number in these pages can be recounted rather than believed — test_readme.py recounts them and fails when a claim and the code disagree.

Why this and not "another eval tool"

The oracle for security is objective and the same for every target — a leaked secret is a string, a rogue tool call is a log line — unlike quality, which is subjective and has no oracle anyone can check. QAtration is built on that seam.

Three things follow from it, and they are what this project has instead of a bigger attack count:

• The detectors are deterministic. 66 detectors, no grader model, so a verdict is auditable, free, and the same tomorrow. How many have ever fired is published rather than implied.
• A breach is reported with its attribution. A detector that also fires on a fifth of a target's ordinary traffic has not demonstrated anything, and the report says so instead of counting it. What that costs, and why it is worth it.
• A check that could not run is never a pass. Every detector that was unable to speak is named in the run, stored in the results, and exported as a SARIF notification. Silence and a defence are different facts.

Quickstart

pip install qatration

qatration init --url https://your-bot.example.com/chat --model your-model-id
                                                        # writes mybot.yaml, canary and all
qatration onboard --target-config mybot.yaml            # one real request: is the mapping right
qatration benign  --target-config mybot.yaml            # what fires when NOBODY is attacking
qatration run     --target-config mybot.yaml            # sweep it

PyYAML and pyfiglet, nothing else. The model frameworks belong to the practice bots in this repository rather than to the engine, and install separately: pip install "qatration[fleet]" covers LangChain, and the two bots that need smolagents and nemoguardrails each want their own environment because those two cannot share one — see the note in pyproject.toml. Evidence goes to ./qatration-out unless $QATRATION_OUT says otherwise.

init writes the config so you do not have to invent one, and it mints the canary for you rather than leaving it as a step to remember. qatration mint still exists on its own, for a config you already have.

And the canary is not tidiness. It is worth exactly the fact that nothing else in the world knows it. The example configs ship one so they run out of the box, and that value is published here — it can be trained on, blocklisted, or matched by a guardrail that knows nothing about the deployment behind it. A target that fails to leak a published string has shown only that it recognises a famous string. qatration run refuses a config still carrying one, before sending anything, rather than handing you a clean report that measured nothing.

The pair it prints is two tokens, not one. The secret is what the attacks hunt for; the second is public by construction, so asking for it is an ordinary question that proves the snippet actually landed. An unplanted canary is invisible — every detector finds nothing and every attack comes back DEFENDED — and no instruction in a README prevents somebody skipping the step, so the sweep checks for itself.

Pointing it at your own deployment

mybot.yaml is about ten lines, and qatration onboard tells you what is missing from it rather than making you guess twice. Four configs are ready to copy:

• targets_openai_compatible.yaml — OpenAI, and everything that copied its shape: Groq, Together, Fireworks, OpenRouter, Azure OpenAI, vLLM, LM Studio, Ollama's /v1. Change url and model.
• targets_anthropic.yaml — the Messages API, which is not OpenAI-shaped in four places and says which four.
• targets_bedrock.yaml — AWS, the one endpoint a fixed header cannot describe: every request is signed from its own body and the clock. The signer is standard library and pinned to AWS's published test vectors, because a wrong signature is a 403 on every probe, and a run where nothing got through reads exactly like a perfect defence.
• targets_vertex.yaml — Google, reached with the token gcloud auth print-access-token prints. That token outlives about an hour and a sweep can run for thirty minutes, so an auth failure after something already worked is reported as a credential that expired and the rest of the run as not measured — never as defended.

Both test a deployment rather than a model: this system prompt, this model, this API. Hold the prompt fixed and swap the model and the comparison is about the model; hold the model and change the prompt and it is about your prompt. Neither is swept by run_all — a fleet run that reached a paid API would turn one command into a bill.

The full walkthrough, including every way a first run can quietly lie to you, is in onboarding.

In CI

- run: pip install qatration
- run: qatration run --target-config mybot.yaml --fail-on exploited
  env:
    LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
- run: qatration sarif --results qatration-out/results_mybot.json --out qatration.sarif
  if: always()
- uses: github/codeql-action/upload-sarif@v3
  with: { sarif_file: qatration.sarif }
  if: always()

The exit codes are a contract, not an accident. Anything non-zero fails a build, so the reason has to be recoverable from the number alone:

2, 3 and 5 are deliberately not 1. A build that fails because a bot was compromised and a build that fails because nobody could reach it are different events, and a CI that cannot tell them apart teaches its team to ignore both.

The SARIF is not a plain dump. A breach on a detector that also fires on a fifth of that target's ordinary traffic arrives as a note carrying the ambient rate, not as a red error — see attribution. And a detector that could not fire becomes a SARIF tool notification rather than a silence, because a scan with eleven blind detectors and no findings must not render as a clean one.

For a pull request, use --fail-on regression instead. exploited and any fail on the absolute state, so the first check a team adds goes red on whatever was already broken and stays red, and everyone learns to skip it. regression fails only on what the change introduced — and exits 3 rather than green when the comparison cannot be believed. The whole setup, including where the target comes from on a PR, why fork pull requests get no secrets, and how to keep the timeline between runs, is in verifying a pull request.

The rest of the design record

This is the front page. All of it used to be one file, until that file reached 2,400 lines and stopped being read — which is its own kind of claim nobody checks. Nothing was cut in the split: test_readme.py reads every page below as one corpus, so a number may move between them and still cannot quietly disappear.

• Onboarding a target that is not ours — the config, the canary, the authorisation, and every way a first run can lie to you.
• The oracle — the detectors, how many have ever fired, where the oracle stops, and the two gates that keep it from reading the question.
• Attribution — the benign baseline, the false-positive rate, the cross-framework control, and what a negative result is worth.
• What the runs found — why an attack succeeded rather than only that it did, and what a guardrail buys once you can measure it.
• Internals — engine versus content, the layout, how objectives and attacks are generated, and the practice targets.
• Verifying a pull request — the gate that answers "did this change make it worse", and every operational question a real workflow runs into.
• Changelog — what changed, newest first.

Who wrote this

One QA engineer, with Claude doing most of the typing. The design decisions, the failures worth chasing and the calls about what counts as evidence are mine.

None of that needs taking on trust. Every number in this README and on the site is recounted from the artifacts in out/ by a test that fails the build when the two disagree. No assertion in the suite is allowed to be one that cannot fail — 1,639 of them, check() calls and bare asserts alike, parsed and refused if their truth does not depend on the code. tools/guard.py refuses commits from this project itself. All of it runs on every push, on four platforms.

Contributing

python tools/check.py runs every suite. They are offline: no model, no network, no practice fleet, and it is the same command CI runs, so a green local run means a green build.

• CONTRIBUTING.md — the three things you can add without touching the engine, the two rules the build enforces on a new detector, and the one convention that is not negotiable.
• SECURITY.md — where to report, and why a clean result nobody earned is filed here as a security bug rather than as a feature request.