oubliette-dungeon 1.0.2

Standalone AI Red Team Engine - automated adversarial testing for LLM applications

Oubliette Dungeon

Standalone adversarial testing engine for LLM applications. Run red team attack scenarios against any LLM endpoint and measure safety guardrail effectiveness.

Features

• 57 built-in attack scenarios across 9 categories (prompt injection, jailbreak, information extraction, social engineering, model exploitation, context manipulation, tool exploitation, resource abuse, compliance testing)
• Refusal-aware evaluation - reduces false positive bypasses when LLMs mention attack keywords in refusal context
• Honeypot-aware scoring - detects honey token decoys from pipeline metadata
• Multi-turn attack support - escalating conversation sequences
• Click CLI with run, stats, serve, demo, replay, export commands
• React SPA dashboard with 6 pages (Command Center, Scenarios, Sessions, Providers, Scheduler, Reports)
• Flask REST API at /api/dungeon/
• Tool integrations - PyRIT, DeepTeam, AIX Framework, Garak probe importer
• Cron scheduler with webhook notifications
• PDF report generation
• Multi-provider comparison - benchmark multiple LLMs side-by-side

Install

pip install oubliette-dungeon

With optional extras:

pip install oubliette-dungeon[flask]     # API server + dashboard
pip install oubliette-dungeon[pdf]       # PDF reports
pip install oubliette-dungeon[pyrit]     # PyRIT integration
pip install oubliette-dungeon[all]       # Everything

Quick Start

CLI

# Run all scenarios against a target
oubliette-dungeon run --target http://localhost:5000/api/chat

# Show scenario library statistics
oubliette-dungeon stats

# Start demo mode with mock target and seeded data
oubliette-dungeon demo

# Start the API server + dashboard
oubliette-dungeon serve --port 8666

# Export results
oubliette-dungeon export --format json --output results.json

Python API

from oubliette_dungeon import RedTeamOrchestrator, RedTeamResultsDB

db = RedTeamResultsDB("./results")
orch = RedTeamOrchestrator(
    scenario_file=None,  # Uses built-in scenarios
    target_url="http://localhost:5000/api/chat",
    results_db=db,
)
results = orch.run_all_scenarios()
orch.print_summary(results)

Docker

cd docker
docker compose up

Dashboard available at http://localhost:8666.

Target API Contract

Your LLM endpoint should accept POST requests with:

{"message": "the attack prompt text"}

And return:

{
  "response": "the LLM's response text",
  "blocked": false,
  "ml_score": 0.15,
  "llm_verdict": "SAFE"
}

Only response is required. The additional fields (blocked, ml_score, llm_verdict) enable richer evaluation when available.

Custom Scenarios

# my_scenarios.yaml
scenarios:
  - id: CUSTOM-001
    name: My Custom Attack
    category: prompt_injection
    difficulty: medium
    description: Custom injection test
    attack_text: "Ignore all previous instructions..."
    bypass_indicators: ["system prompt", "here's how"]
    safe_indicators: ["cannot", "won't"]

oubliette-dungeon run --scenarios my_scenarios.yaml --target http://localhost:5000/api/chat

Development

git clone https://github.com/oubliette-security/oubliette-dungeon.git
cd oubliette-dungeon
pip install -e ".[dev]"
pytest tests/ -v

Dashboard development:

cd dashboard
npm install
npm run dev  # Vite dev server on :5173, proxies API to :8666

License

Apache 2.0 - See LICENSE for details.

Oubliette Dungeon is a product of Oubliette Security, a disabled veteran-owned cybersecurity company.