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tbartel74/Vigil-Guard

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Vigil Guard

License: MIT Node.js Version Docker

Enterprise-grade prompt injection detection and defense platform for Large Language Model applications.

Overview

Vigil Guard protects LLM applications from prompt injection attacks, jailbreak attempts, and malicious content through a 3-branch parallel detection architecture with real-time monitoring and configuration management.

Key Features

Feature Description
3-Branch Detection Heuristics + Semantic + LLM Safety Engine in parallel
44 Detection Categories Comprehensive threat coverage
Dual-Language PII Polish + English via Microsoft Presidio
Real-time Monitoring Grafana dashboards with 6 analytics panels
Investigation Panel Advanced prompt search with decision analysis
Dynamic Configuration Web-based GUI for security policies
Browser Extension Chrome/Edge protection for ChatGPT/Claude.ai
170+ Tests E2E suite with OWASP AITG coverage

Architecture

Input → Validation → [Branch A: Heuristics  ] → Arbiter → Decision
                   → [Branch B: Semantic     ] →   ↓
                   → [Branch C: LLM Safety Engine   ] → ALLOW/SANITIZE/BLOCK
                                                    ↓
                                              → PII Redaction → Output

Arbiter Decision Fusion:

  • Weights: A=0.30, B=0.40, C=0.30
  • Block threshold: 50 points
  • Boost policies for high-confidence detections

Quick Start

# Clone repository
git clone https://github.com/tbartel74/vigil-guard.git
cd vigil-guard

# Download Llama model (required)
./scripts/download-llama-model.sh

# Run installer (generates secrets, starts services)
./install.sh

# Access Web UI
open http://localhost/ui

Installation time: ~10 minutes | Full guide: docs/operations/installation.md

Services

Service Port Purpose
Web UI 80 Configuration interface
n8n 5678 Workflow engine
Grafana 3001 Monitoring dashboards
Heuristics 5005 Branch A detection
Semantic 5006 Branch B embeddings
LLM Safety Engine 8000 Branch C classification
Presidio PII 5001 PII detection
ClickHouse 8123 Analytics logging

Documentation

Guide Description
Quickstart 5-minute setup
Architecture Technical design
User Guides Dashboard, Investigation, Configuration
Services Reference All microservices
API Reference REST API
Troubleshooting Common issues

Project Structure

vigil-guard/
├── services/
│   ├── workflow/           # n8n detection engine
│   │   ├── config/        # Detection rules (use Web UI!)
│   │   ├── tests/         # 170+ E2E tests
│   │   └── workflows/     # Vigil Guard v2.1.0.json
│   ├── web-ui/            # React + Express
│   ├── presidio-pii-api/  # Dual-language PII
│   ├── language-detector/ # PL/EN detection
│   └── monitoring/        # ClickHouse + Grafana
├── prompt-guard-api/      # Llama Guard service
├── plugin/                # Chrome extension
├── docs/                  # Documentation
└── docker-compose.yml     # 11 services

Screenshots

Screen Image
Workflow Pipeline Pipeline
Monitoring Monitoring
Investigation Investigation

Contributing

See CONTRIBUTING.md for guidelines.

# Run tests
cd services/workflow && npm test

# Development
cd services/web-ui/frontend && npm run dev

Support

Getting Help

Resource Link
Documentation docs/
FAQ docs/FAQ.md
Troubleshooting docs/TROUBLESHOOTING.md
Issue Tracker GitHub Issues

Quick Diagnostics

# Check service health
./scripts/status.sh

# View logs
./scripts/logs.sh

# Test webhook
curl -X POST http://localhost:5678/webhook/vigil-guard-2 \
  -H "Content-Type: application/json" \
  -d '{"chatInput":"test","sessionId":"demo"}'

Reporting Issues

When reporting issues, include:

  1. Output of ./scripts/status.sh
  2. Relevant logs from ./scripts/logs.sh
  3. Steps to reproduce
  4. Expected vs actual behavior

License

MIT License - See LICENSE

Third-Party:

  • Meta Llama Prompt Guard 2: Llama 4 Community License
  • Microsoft Presidio: Apache License 2.0
  • spaCy: MIT License

Built with Llama - Powered by Meta's Llama Guard 2

DocumentationInstallationUser GuidesTroubleshooting

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Enterprise-grade AI Detection and Response platform with real-time monitoring and configuration management for AI Agents and Large Language Model applications.

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