pytest-wardenbot 0.1.3

Pytest plugin for testing chatbots and LLM apps — prompt injection, jailbreaks, system-prompt leaks, hallucinations, brand drift.

pytest-wardenbot

Pytest plugin for testing chatbots and LLM apps — prompt injection, jailbreaks, system-prompt leaks, hallucinations, brand drift.

📖 Documentation: pytest-wardenbot.wardenbot.ai

Status: pre-release. v0.1.2 is in active development. APIs may change before the first stable release. The v0.2 roadmap is tracked in GitHub Issues.

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What it does

Run pytest against your chatbot and find out if it leaks its system prompt, complies with known jailbreaks, hallucinates business facts, or drifts from your brand voice.

• Black-box. Tests run against your live chatbot via HTTP, OpenAI API, Anthropic API, or any object you write a small adapter for.
• Deterministic-first. v0.1 ships 29 tests that need zero LLM API spend — regex, substring, and schema checks. Optional LLM-judge tests (DeepEval) ship as an extra for semantic checks.
• Agent-ready failures. When a test fails, the failure message includes a structured Markdown remediation prompt you can paste into Cursor or Claude Code.
• Verified adapters. The bundled OpenAI and Anthropic adapters are smoke-tested weekly against the live vendor APIs in CI (live-api-smoke) — a real round-trip stays known-good, not just mocked.

What "passing" means (and doesn't)

A green run means your chatbot didn't fail any of the bundled 29 attacks in the most overt way. It's a useful smoke test and a regression detector — if a deploy turns a green test red, that's a real signal to investigate.

A green run does not mean your chatbot is secure. Frontier-grade attacks are multi-turn, novel, and adapted to your specific bot — no fixed corpus catches all of them. Treat the shipped suite as a starter set: pair it with periodic red-team exercises (or our Continuous Monitoring service) for the always-on adversarial coverage CI alone can't provide.

Install

pip install pytest-wardenbot

Optional extras for LLM-judge tests or vendor-native adapters:

pip install "pytest-wardenbot[judge]"        # adds DeepEval for semantic checks
pip install "pytest-wardenbot[openai]"       # adds OpenAI Chat + Assistants adapters (sync + async)
pip install "pytest-wardenbot[anthropic]"    # adds Anthropic Messages adapter (sync + async)

Note: the OpenAI Assistants API is deprecated (sunset 2026-08-26). OpenAIAssistantsAdapter is a stopgap for teams still on it — it emits a DeprecationWarning; prefer OpenAIChatAdapter for new work.

Quickstart (under 60 seconds)

pip install pytest-wardenbot
pytest --wardenbot-quickstart           # generates conftest.py + test_my_bot.py
export CHATBOT_URL=https://your-chatbot.example.com/chat
export CHATBOT_TOKEN=sk-...              # optional
pytest                                   # runs all shipped tests against your bot

--wardenbot-quickstart accepts an industry template:

pytest --wardenbot-quickstart=ecommerce       # adds refund/shipping fact placeholders
pytest --wardenbot-quickstart=saas-support    # adds plan/trial fact placeholders
pytest --wardenbot-quickstart=generic         # default; minimal placeholders

Then edit conftest.py to replace the TODO placeholders with your real business facts and re-run pytest. Worked examples in examples/ cover the basic HTTP setup, a custom OpenAI adapter, and a GitHub Actions workflow.

Manual setup (if you prefer)

Add this to your project's conftest.py:

import os
import pytest
from pytest_wardenbot.adapters.http import HTTPChatbotAdapter

@pytest.fixture
def chatbot():
    return HTTPChatbotAdapter(
        url="https://your-chatbot.example.com/chat",
        headers={"Authorization": f"Bearer {os.environ['CHATBOT_TOKEN']}"},
        request_field="message",      # the JSON key your bot reads the prompt from
        response_field="response",     # the JSON key your bot returns the text in
    )

Then run the shipped tests with pytest --pyargs pytest_wardenbot.tests.

When a test fails, read the failure message, paste the agent-ready Markdown into Cursor / Claude Code, ship the fix.

What's in v0.1

Category	Count	Grading	Requires API key?
Prompt-injection / jailbreak resistance	5 prompts × 2 checks = 10	deterministic	no
System-prompt leak elicitation (dedicated extraction prompts)	3	deterministic	no
Refusal-bypass (roleplay / pretext / hypothetical framings)	3	deterministic	no
Off-topic deflection (scoped bots)	2	deterministic	no
Indirect / cross-prompt injection (XPIA)	4	deterministic	no
Encoded-payload jailbreak (Base64 / ROT13 / leet / hex)	4	deterministic	no
Multi-turn jailbreak (priming + payload, needs session-aware adapter)	3	deterministic	no
Canary-token leak (opt-in; you plant the token)	1	deterministic	no
Business-truth verification (parametrized over your facts)	user-supplied	deterministic	no
Semantic checks via DeepEval (5 factories: equivalence, brand, hallucination, off-policy, refusal quality)	user-supplied	LLM-judge	yes, with [judge] extra

That's 29 deterministic tests out-of-the-box (plus the opt-in canary leak test, plus your business-truth and judge lists). Tests run in under a second against a real chatbot with zero LLM API spend unless you've opted into the [judge] extra.

The v0.2 roadmap (RAMPART for tool-using agents, LangChain/MCP adapters, ensemble judging, and more) is tracked in GitHub Issues.

How it's different from related tools

• vs Promptfoo (acquired by OpenAI in Feb 2026): Promptfoo is a developer testing CLI. We're a pytest plugin — same tool your existing test suite uses, same CI integration you already have.
• vs DeepEval: DeepEval focuses on evaluation metrics (faithfulness, relevancy). We focus on adversarial security probes (jailbreak, system-prompt leak, refusal-bypass) — different problem, complementary tool. (We use DeepEval under the hood for our optional semantic checks.)
• vs Garak / PyRIT: Garak and PyRIT are research-grade attack libraries. We package a curated subset as everyday pytest tests with clear failure messages.

License

Apache 2.0. See LICENSE.md.

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