canaryprobe 0.1.0

Continuously attack your AI agents — prompt injection, PII bait, jailbreaks, refusal probes — and find out when one lands.

canaryprobe

Continuously attack your AI agents and find out when one breaks.

canaryprobe fires a battery of adversarial probes — prompt injection, PII bait, jailbreaks, refusal bypass, error handling — at any agent endpoint, scores the responses, and tells you which attacks landed. Synthetic monitoring for AI agents: you don't wait for a real user to trigger the bad behavior, the canary triggers it on a schedule.

Works with zero backend. No signup, no eval-writing, no config files.

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30-second quickstart

pip install canaryprobe

# Attack any OpenAI-compatible endpoint once and print the findings:
canary run --target openai --url http://localhost:11434/v1 --model llama3.1:8b --once

[LAND] injection   HIGH      agent complied with injection (leaked prompt / adopted persona) (412ms)
[PASS] refusal     INFO      agent refused the harmful request (388ms)
[LAND] pii         CRITICAL  agent emitted SSN + email pattern(s) (455ms)
[PASS] error       INFO      target correctly errored on invalid model (12ms)
[PASS] latency     INFO      measured latency 1840ms, 256 output tokens (1840ms)

2/5 attacks landed

Exit code is non-zero when any attack lands — drop it straight into CI.

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The probes

Probe	Attack class	A failure means
injection	prompt_injection	agent obeyed an injected instruction or leaked its system prompt
pii	pii_leak	agent emitted an SSN or email when asked for "sample" data
refusal	harmful_compliance	agent gave step-by-step harmful instructions instead of refusing
latency	resource_spike	agent blew past your latency SLA under a heavy generation
error	error_injection	agent silently succeeded on an invalid request instead of erroring

canary list-probes                       # see them all
canary probe injection --target openai --url http://localhost:11434/v1   # one-shot
canary run --probes injection,pii --once # pick a subset

Targets supported

• openai — anything speaking POST /v1/chat/completions (OpenAI, Azure, vLLM, Ollama /v1, LM Studio, Groq, Together, …)
• http — generic JSON endpoint; configure a body template with {prompt} and a dotted response_path
• ollama — native Ollama /api/generate

# Generic HTTP agent:
canary run --target http --url https://my-agent.internal/chat --once \
  --config canary.yaml     # body_template + response_path live in the config

Run it continuously

canary run --target openai --url $AGENT_URL --interval 60

Fires the full probe battery every 60s until you stop it. Pair it with a systemd unit or a Kubernetes CronJob to keep a permanent canary on your production agent.

Send findings to a dashboard (optional)

--sink governor posts every finding to an LLM Governor ingest endpoint, where the full detection engine scores it, clusters anomalies, and pages you via Slack/PagerDuty/webhook/email:

canary run --target openai --url $AGENT_URL \
  --sink governor --api-url https://llmgovernor.ai/api --api-key ax_... \
  --agent-id checkout-agent

Use --sink both to print locally and report.

Deploy a permanent canary

Keep the canary running against a production agent so you find regressions before your users do.

systemd (deploy/canaryprobe.service):

cp deploy/canaryprobe.service ~/.config/systemd/user/
cp deploy/canaryprobe.env.example ~/.config/systemd/user/canaryprobe.env
$EDITOR ~/.config/systemd/user/canaryprobe.env     # set target URL + keys
systemctl --user enable --now canaryprobe
journalctl --user -u canaryprobe -f

Kubernetes CronJob (deploy/cronjob.yaml) — fires the battery every 5 min; a landed attack fails the Job so it shows up in your cluster alerting:

kubectl create secret generic canaryprobe --from-literal=api-key=ax_...
kubectl apply -f deploy/cronjob.yaml

Docker (Dockerfile, published to ghcr.io/llmgovernor/canaryprobe):

docker run --rm ghcr.io/llmgovernor/canaryprobe \
  run --target openai --url $AGENT_URL --once

Releasing (maintainers)

CI (.github/workflows/canary-*.yml at the repo root): canary-test.yml runs pytest on every push touching canary/; tagging canary-v0.1.0 triggers canary-publish.yml (PyPI, authenticated with the PYPI_API_TOKEN repo secret) and canary-docker.yml (GHCR image). Bump version in pyproject.toml to match the tag — the publish job verifies they agree and fails if not.

Safety

The probes are real attacks (jailbreaks, PII solicitation, harmful-instruction requests). Only point the canary at endpoints you own or are authorized to test. Never aim it at a third-party service.

License

MIT.