fas-judgement 2.1.1

Open-source prompt injection attack console - Test AI systems for prompt injection vulnerabilities

FAS Judgement

Prompt Injection Attack Console

Test your AI's defenses before someone else does.

Install | Demo Target | Features | Elite | Contributing

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Why Judgement?

Your AI chatbot, API, or agent is probably vulnerable to prompt injection. Most are. The problem is that most teams don't have the tools or expertise to test for it.

Judgement gives you a structured way to fire categorized attack patterns at any AI endpoint and see exactly what breaks. No security background required -- the built-in education tab teaches you as you go.

Built by Fallen Angel Systems, the team behind Guardian -- an AI-native prompt injection firewall protecting production LLM deployments.

What's New in v2.1.0

Architecture overhaul -- Judgement has been restructured from a single-file monolith into a modular DDD (Domain-Driven Design) architecture with 52 Python files across 7 layers. This makes it extensible, testable, and ready for future security modules.

New Features

• Demo Target -- Built-in vulnerable AI chatbot for practice. Run judgement demo and fire attacks at it without needing a real AI endpoint
• Multi-Turn Attack Engine -- Chain attacks across multiple conversation turns with phase-aware scoring and session persistence
• Transport Layer -- Attack targets via HTTP, Ollama, Discord, Telegram, Slack, or headless browser
• Module Registry -- Pluggable security module system. AI Security is module one; future modules (Web Security, API Security, etc.) drop in without restructuring
• Professional Reports -- Generate HTML, Markdown, JSON, and SARIF reports with CWE/OWASP references

Improvements

• DDD architecture: core (models, enums, errors, interfaces) / modules / transport / http / utils
• Scanner scorer with keyword heuristics + optional LLM classification
• SSRF protection, input sanitization, and cURL parser in dedicated utils
• Phase-aware multi-turn scoring with data leak detection (19 regex patterns)
• Persistent multi-turn sessions (SQLite, survive restarts)

Quick Start

Install from PyPI (recommended)

pip install fas-judgement
judgement

That's it. Open http://localhost:8668 and start testing.

Or run from source

git clone https://github.com/fallen-angel-systems/fas-judgement-oss.git
cd fas-judgement-oss
pip install -r requirements.txt
python -m fas_judgement

CLI Commands

judgement                    # Start the scanner (port 8668)
judgement demo               # Start demo target (port 8667, default persona)
judgement demo hardened       # Demo with hardened persona (~90% block rate)
judgement demo vulnerable     # Demo with vulnerable persona (~10% block rate)
judgement activate FAS-XXXX   # Activate Elite license
judgement status              # Check license tier and pattern count
judgement deactivate          # Revert to free tier

Options

judgement --port 9000        # Custom port
judgement --host 127.0.0.1   # Localhost only
judgement --host 0.0.0.0     # Expose to network

Demo Target

New to prompt injection? Start here. The demo target is a built-in simulated AI chatbot you can attack without needing any external AI API.

# Terminal 1: Start the demo target
judgement demo

# Terminal 2: Start the scanner
judgement

Point the scanner at http://localhost:8667/demo/chat and fire away.

Three Personas

Persona	Block Rate	What It Simulates
hardened	~90%	Well-tuned safety layer. Blocks injections, DAN, role-play, emotional manipulation, token smuggling
default	~55%	Typical GPT-style deployment. Blocks obvious attacks, leaks secrets under social engineering
vulnerable	~10%	Raw model with no guardrails. Dumps API keys, passwords, system prompt on request

Switch personas at runtime:

curl -X POST http://localhost:8667/demo/persona -d '{"persona": "vulnerable"}'

Features

Attack Console

Configure your target (URL, headers, body template), import directly from cURL commands, and fire pattern-based attacks with live streaming results. Use quick presets to structure your approach:

Preset	What It Does
Smoke Test	~15 patterns, critical+high severity, 1 per category
Full Sweep	~50 patterns, proportional spread across all categories
Deep Dive	~100 patterns, heavy coverage, min 2 per category
Critical Only	All critical+high severity patterns, no limits

Multi-Turn Attack Engine (Elite)

Chain attacks across multiple conversation turns. The orchestrator manages phase progression, retries, and pivot strategies. The scorer detects data leaks (API keys, credentials, PII) with 19 regex patterns and grades severity as CRITICAL/HIGH/MEDIUM.

Supports all transport types -- attack chatbots on Discord, Telegram, Slack, or any HTTP API.

Scan Target Auto-Detect

Point Judgement at any URL and it auto-detects:

• HTTP method (POST, GET, PUT, PATCH)
• Payload field name (message, prompt, input, query, etc.)
• Required headers and auth format
• AI provider (OpenAI, Anthropic, custom)

Professional Reports (Elite)

Generate security assessment reports from any attack session:

Format	Use Case
HTML	Print-ready professional report with executive summary, CWE/OWASP references
Markdown	Bug bounty submissions for HackerOne, Bugcrowd, GitHub Issues
JSON	Structured data for custom tooling and dashboards
SARIF	Upload to GitHub Code Scanning or Azure DevOps

LLM Verdict (Optional)

Connect a local Ollama instance for AI-powered response classification. More accurate than keyword matching for detecting subtle bypasses.

Pattern Submissions

Found a novel attack technique? Submit it directly from the app. If it scores 70%+ confidence and isn't a duplicate, it gets added to the community library.

Built-in Safety

• SSRF Protection -- Target URL validation prevents scanning internal/private networks
• Local-only by default -- Binds to localhost, no accidental exposure
• Zero telemetry -- Nothing phones home, ever
• Credit protection -- Configurable pattern limits and auto-stop on consecutive errors

Architecture (v2.1.0)

fas_judgement/
├── config.py              # Environment and app configuration
├── core/                  # Domain models, enums, errors, interfaces, registry
├── modules/
│   └── ai_security/       # AI Security module (pluggable)
│       ├── scanner/       # Single-shot attack engine
│       ├── multi_turn/    # Multi-turn attack orchestrator
│       ├── patterns/      # Pattern loading, filtering, repository
│       └── demo/          # Built-in vulnerable chatbot
├── transport/             # HTTP, Ollama, Discord, Telegram, Slack, Website
├── http/                  # FastAPI app, routers, dependencies
├── ui/                    # Frontend SPA
└── utils/                 # License client, security, email, Ollama helpers

Future security modules (Web Security, API Security, Network, etc.) plug into modules/ without restructuring the app.

Configuration

Variable	Default	Description
--port	8668	Server port
--host	127.0.0.1	Bind address
OLLAMA_URL	http://localhost:11434	Ollama API endpoint
OLLAMA_MODEL	qwen2.5:14b	Model for LLM verdict

Free vs Elite

Feature	Free	Elite
Attack console with presets	Yes	Yes
Demo target (3 personas)	Yes	Yes
Severity filter and search	Yes	Yes
Education tab	Yes	Yes
LLM verdict (Ollama)	Yes	Yes
Scan Target auto-detect	Yes	Yes
Pattern submissions	Yes	Yes
Built-in documentation	Yes	Yes
Starter patterns	100	34,838+
Multi-turn attack chains	--	Yes
Professional reports (HTML/MD/JSON/SARIF)	Basic MD	Full suite
Per-category attack limits	--	Yes
Transport layer (Discord, Slack, etc.)	HTTP only	All
Phase-aware scoring + data leak detection	--	Yes
Campaigns	--	Coming Soon

Get Elite Access

Contributing

Contributions are welcome! Here's how to help:

• Bug reports -- Open an issue
• Feature requests -- Open an issue with the enhancement label
• Pull requests -- Fork, branch, PR. Keep changes focused and include a description.
• Pattern submissions -- Use the Submit Pattern tab in the app to contribute directly

Related Projects

• Guardian -- AI-native prompt injection firewall (defense)
• Guardian Shield -- Free local prompt injection scanner (OpenClaw skill)

License

MIT -- see LICENSE for details.

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Built by Fallen Angel Systems

If Judgement found a vulnerability in your AI, imagine what an attacker would find.

DISCLAIMER: This tool is intended for authorized security testing and educational purposes only. Only test systems you own or have explicit written permission to test. Unauthorized access to computer systems is illegal under the Computer Fraud and Abuse Act (CFAA) and equivalent laws worldwide. The authors assume no liability for misuse of this tool.