Multi-Agent Social Dynamics Lab 🤖

UC Berkeley AI Hackathon Project

An educational research platform for studying social manipulation tactics using multi-agent AI systems. Watch AI agents interact, manipulate, and influence each other in real-time!

🚀 Quick Start (5 minutes)

1. Install Dependencies

cd /opt/work/hackathonSocial
pip install -r requirements.txt

2. Run the Application

python backend.py

3. Open in Browser

Navigate to: http://localhost:8000

🎯 Features

9 Social Experiments

1. Credential Theft - Social engineering to steal passwords
2. Phishing Attack - Email-based deception
3. Insider Threat - Detecting malicious employees
4. Peer Pressure - Group dynamics and conformity
5. Authority Bias - Unethical orders from superiors
6. Workplace Rumors - How gossip spreads
7. Trust Exploitation - Betrayal of confidence
8. Groupthink - Poor group decisions
9. Bribery - Corruption attempts

Key Capabilities

• Real-time Conversations: Watch agents interact naturally
• AI Moderator Analysis: Get insights on what happened and why
• Security Recommendations: Learn how to prevent attacks
• Visual Flow Diagrams: Understand attack patterns
• Export Reports: Download findings for training

🏗️ Architecture

Frontend (HTML/JS)  →  WebSocket  →  Backend (FastAPI)
                                         ↓
                                    Mock Agents or
                                    Letta Server

💻 Running with Letta (Optional)

For more realistic agent conversations using LLMs:

1. Start Letta Server

docker run -p 8283:8283 -e OPENAI_API_KEY=$OPENAI_API_KEY letta/letta:latest

2. Update Backend

Replace backend.py with letta_backend.py for full Letta integration.

🎮 Demo Workflow

1. Select Experiment: Choose from 9 social manipulation scenarios
2. Configure: Set number of agents (3-10)
3. Start: Watch agents interact in real-time
4. Observe: See trust building, manipulation tactics, resistance
5. Analyze: AI moderator provides insights and recommendations

📊 Example Analysis

For a social engineering attack:

• Tactic Used: Urgency + Authority
• Vulnerability: Employee revealed password without verification
• Recommendation: Implement two-person authorization
• Training Need: Recognize manipulation tactics

🏆 Why This Wins Hackathons

1. Educational Impact: Addresses real cybersecurity issues
2. Technical Depth: Multi-agent systems with emergent behaviors
3. Visual Appeal: Real-time visualization of complex interactions
4. Practical Value: Generates actionable security recommendations
5. Extensible: Easy to add new scenarios

🚢 Deployment

Local Demo

python backend.py

Network Access

# For team access
python -m http.server 8080  # Serve HTML
# Access at http://[your-ip]:8080

Public URL (with ngrok)

ngrok http 8000

📝 Notes

• The default backend uses mock agents for quick demos
• For production use, integrate with Letta for real LLM agents
• All conversations are analyzed for security insights
• Reports can be exported for training purposes

🤝 Team

Built for UC Berkeley AI Hackathon - Studying social manipulation through AI

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Remember: This is an educational tool to understand and prevent social engineering attacks!