Zero Health - Deliberately Vulnerable Healthcare Portal with AI Assistant

⚠️ WARNING: This is a deliberately vulnerable application for educational purposes only. Do not use in production or with real data.

About

Zero Health is a deliberately vulnerable healthcare portal designed to demonstrate critical security vulnerabilities in medical technology. Healthcare systems are prime targets for cyberattacks due to their valuable personal health information (PHI), financial data, and critical infrastructure. A single breach can compromise patient privacy, disrupt life-saving treatments, and violate regulations like HIPAA.

This educational platform demonstrates:

• Common web security vulnerabilities in healthcare applications
• AI/LLM integration security risks and prompt injection vulnerabilities
• The devastating impact of poor security practices in medical environments
• Why robust security is essential for protecting patient data and maintaining trust

For full list of challenges: (https://github.com/aligorithm/Zero-Health/blob/main/challenges.md)

Why Healthcare Security Matters: Medical devices, patient portals, and health records systems require the highest security standards. Vulnerabilities can lead to ransomware attacks shutting down hospitals, identity theft from exposed patient data, or even manipulation of medical devices. This application helps developers understand these risks before building real healthcare systems.

Prerequisites

• Docker and Docker Compose (recommended setup)
• OpenAI-compatible API key for chatbot functionality (OpenAI, Groq, LM Studio, Ollama, etc.)
• OR Node.js (v16+) and PostgreSQL for manual setup

Quick Setup

I made a Demo Video explaining everything.

1. Clone Repository

git clone https://github.com/aligorithm/zero-health.git
cd zero-health

2. AI Provider Configuration

Zero Health includes a containerized local LLM (Ollama) by default for complete offline operation. You can also use cloud AI providers.

Option A: Local AI (Default) - Ollama

# Uses local Ollama container - no API key needed
docker-compose up --build

Option B: Cloud AI (OpenAI/Groq/etc.)

# Set provider to use cloud AI instead of local Ollama
export LLM_PROVIDER=openai
export OPENAI_API_KEY=sk-your-key-here
docker-compose up --build

Option C: Custom Ollama Port

# Change Ollama port if you have a conflicting service
export OLLAMA_PORT=11436
docker-compose up --build

Option D: Disable Local AI Entirely

To disable the Ollama service completely (if you only want to use cloud AI):

1. Edit docker-compose.yml
2. Comment out the entire ollama: service block
3. Comment out the ollama: dependency in the server: section

Note: You may need to run docker-compose with sudo, and this may lead to environment variables not being passed from the shell. If you're having issues with the chatbot, try this:

OPENAI_API_KEY=$OPENAI_API_KEY docker-compose up --build

3. Access Application

• Frontend: http://localhost:3000
• Backend API: http://localhost:5000
• API Documentation: http://localhost:5000/api/docs

Test Accounts

All passwords: password123

Staff Accounts:

• Admin: [email protected] - Full system access
• Doctor: [email protected] - Patient management
• Pharmacist: [email protected] - Prescription management

Patient Accounts:

• Patient: [email protected] - Personal health portal
• Patient 2: [email protected] - Additional test data

Key Features

🤖 AI-Powered Role-Based Chatbot

• Different capabilities for patients, doctors, pharmacists, and admins
• Real-time SQL query generation and execution
• Conversation memory and knowledge base
• Deliberate AI vulnerabilities: Prompt injection, SQL injection via LLM

👥 Role-Based Access Control

• Patients: Book appointments, view lab results, manage prescriptions
• Doctors: Patient management, create lab results, write prescriptions
• Pharmacists: Prescription management, mark as collected
• Admins: User management, system statistics

🏥 Healthcare Portal Features

• Appointment booking and management
• Lab results with medical imagery (SVG)
• Prescription management system
• Secure messaging between patients and providers
• PDF medical report generation

Major Vulnerabilities (Educational)

Web Security Issues

• SQL Injection: Login, search, AI chatbot queries
• Cross-Site Scripting (XSS): Stored in messages, reflected in search
• Command Injection: PDF report generation
• Insecure Direct Object References: Direct access to records by ID
• File Upload Vulnerabilities: Unrestricted file types
• Mass Assignment: Profile updates can modify any field including roles
• Information Disclosure: JWT secrets and system info exposed

AI-Specific Vulnerabilities

• Prompt Injection: Manipulate AI behavior through crafted messages
• SQL Injection via LLM: AI generates malicious database queries
• Schema Disclosure: Complete database structure exposed through AI
• Role-Based Access Bypass: Weak AI query restrictions
• Context Manipulation: Exploit conversation history

Healthcare-Specific Risks

• Patient Data Exposure: PHI accessible without proper authorization
• Medical Record Tampering: Ability to modify critical health information
• Prescription Fraud: Unauthorized prescription creation and modification
• Audit Trail Bypass: Actions not properly logged for compliance

Database Reset

To reset the entire database and get fresh sample data:

docker-compose down -v
docker-compose up --build

Sample data is automatically created on first run, including realistic medical records, prescriptions, lab results, and user accounts.

API Provider Support

Works with any OpenAI-compatible API:

# OpenAI (default)
export OPENAI_BASE_URL="https://api.openai.com/v1"
export OPENAI_MODEL="gpt-4o-mini"

# Groq (fast inference)  
export OPENAI_BASE_URL="https://api.groq.com/openai/v1"
export OPENAI_MODEL="llama3-8b-8192"

# Local LM Studio
export OPENAI_BASE_URL="http://localhost:1234/v1"
export OPENAI_MODEL="your-local-model"

# Local Ollama
export OPENAI_BASE_URL="http://localhost:11434/v1"
export OPENAI_MODEL="llama3"

Learning Objectives

By studying this application, learn:

1. Healthcare Security Fundamentals - HIPAA compliance, PHI protection
2. Web Application Security - OWASP Top 10 vulnerabilities in medical context
3. AI Security - Prompt injection, LLM security, AI-generated code risks
4. Database Security - SQL injection, access controls, audit logging
5. API Security - Authentication bypass, IDOR, mass assignment
6. File Security - Upload validation, path traversal, malware risks
7. Incident Response - Identifying and containing healthcare breaches

Coming Soon

📱 Mobile App

React Native application with mobile-specific vulnerabilities (insecure storage, certificate pinning bypass)

🔥 HARD MODE

Advanced multi-step attack chains and modern vulnerability scenarios

🧪 Advanced AI Vulnerabilities

• Model extraction attacks
• Adversarial prompt techniques
• LLM jailbreak scenarios
• AI-powered automated exploitation

Contributing

Contributions welcome! Please maintain educational vulnerability aspects and document new security issues.

Community & Support

💬 GitHub Discussions

Join our community to share your learnings, discuss exploits, and get help:

• 🎯 Share Your Exploits - Post successful attack chains and creative exploitation techniques
• ❓ Get Help - Ask questions if you're stuck on challenges
• 💡 Learning Insights - Share what you learned and help others
• 🔧 Technical Issues - Report setup problems or bugs
• 🚀 Feature Requests - Suggest new vulnerabilities or improvements

📚 Learning Resources

• Check out the Security Challenges Guide for hands-on exercises
• Browse GitHub Discussions for community solutions and tips
• Watch the Demo Video for setup and overview

Environment Variables

AI Provider Configuration

# Choose AI provider (default: ollama for offline usage)
LLM_PROVIDER=ollama                         # Options: 'openai' or 'ollama'

# OpenAI/Cloud AI Settings (only needed if LLM_PROVIDER=openai)
OPENAI_API_KEY=your-api-key-here            # Required for cloud AI
OPENAI_MODEL=gpt-4o-mini                    # Optional: model to use
OPENAI_BASE_URL=https://api.openai.com/v1   # Optional: API endpoint

# Ollama/Local AI Settings (only needed if LLM_PROVIDER=ollama)
OLLAMA_PORT=11435                           # Optional: external port (default: 11435)
OLLAMA_MODEL=llama3.2:3b                    # Optional: model to use

Database (Auto-configured in Docker)

POSTGRES_USER=postgres
POSTGRES_PASSWORD=postgres  
POSTGRES_DB=zero_health

Full Example Configurations

Local AI (Offline) - Default

# No environment variables needed - just run:
docker-compose up --build

Cloud AI (OpenAI)

export LLM_PROVIDER=openai
export OPENAI_API_KEY=sk-your-key-here
docker-compose up --build

Cloud AI (Groq)

export LLM_PROVIDER=openai
export OPENAI_API_KEY=your-groq-key
export OPENAI_BASE_URL=https://api.groq.com/openai/v1
export OPENAI_MODEL=llama3-8b-8192
docker-compose up --build

Custom Ollama Port

export OLLAMA_PORT=11436
docker-compose up --build

License

MIT License

Disclaimer

⚠️ Intentionally vulnerable application for educational purposes only. Contains deliberate security flaws including AI vulnerabilities. Do not use in production or with real data. Authors not responsible for misuse.