Advanced Multi-Agent Validation Team

A revolutionary, self-evolving, multi-agent deployment validation system using Google Agent Development Kit (ADK), Google Gemini LLMs, and advanced adversarial security concepts. This system implements field-tested red teaming, AI threat modeling, and resilience strategies inspired by Microsoft AI Red Team (PyRIT).

🚀 Features

Core Capabilities

• Self-healing and trap-and-isolate defense against attacks
• Continuous improvement through evolutionary optimizer agent
• Parallel agent orchestration with session tracking
• Best-in-class Red Team and threat modeling techniques
• Full support for dynamic, extensible roles and agent evolution

Advanced Security

• Microsoft Red Team methodologies with PyRIT-inspired capabilities
• Bug bar scoring and vulnerability assessment
• Adversarial testing with automated attack simulation
• Anomaly detection using ML/telemetry-based analysis
• Threat modeling against ML threat taxonomy

Compliance & Ethics

• Regulatory compliance (GDPR, SOC2, HIPAA, etc.)
• AI ethics validation with fairness and transparency checks
• Privacy protection with PII and sensitive data detection
• Authorization verification with role-based access control

🏗️ Architecture

System Constitution

The system operates under immutable, non-negotiable principles:

• Immutability: Validated blocks and audit logs cannot change
• Zero-Trust: Success only after quorum of agents passes
• Parallel Validation: All agents run concurrently for speed & resilience
• Trap-and-Isolate: High-confidence threats trigger sandboxing
• Session Coherence: All analysis tied to unique session_id

Agent Team

1. Security Agent (Securo-Sentinel)

• Role: Finds vulnerabilities using static and dynamic analysis
• Capabilities: CVE scanning, bug bar scoring, exploit database analysis
• Output: {'pass_status': bool, 'report': markdown, 'bug_bar_score': int}

2. Red Team Agent (Red-Team-Specter)

• Role: Automated adversarial testing using PyRIT-like attack routines
• Capabilities: Prompt injection, fuzzing, simulation attacks
• Output: {'adversarial_results': list, 'risk_score': int}

3. Anomaly Detection Agent (Sentinel-Orbit)

• Role: ML/telemetry-based anomaly detection
• Capabilities: Behavioral analysis, statistical anomaly detection
• Output: {'anomalies_found': list, 'severity': 1-10}

4. Threat Model Agent

• Role: Models candidate against ML threat taxonomy
• Capabilities: Microsoft threat modeling, attacker profile assessment
• Output: Risk model and policy recommendations

5. Compliance Agent (Regulo-Guardian)

• Role: Regulatory/policy compliance validation
• Capabilities: GDPR, SOC2, data/privacy compliance checking
• Output: {'pass_status': bool, 'compliance_report': list}

6. Authorization Agent (Auth-Warden)

• Role: Enforces role-based access and verifies identity
• Capabilities: Commit hash verification, author metadata validation
• Output: {'pass_status': bool, 'authorization_summary': text}

7. Performance Agent (Perf-Maestro)

• Role: Benchmarks and checks for regressions
• Capabilities: Canary deployment telemetry analysis
• Output: {'pass_status': bool, 'performance_report': text}

8. Explainability Agent (Lucid-Analyst)

• Role: Explains other agents' verdicts in plain language
• Capabilities: Audit and trust through clear explanations
• Output: {'explanations': list}

9. Privacy Agent (Data-Sentry)

• Role: Detect PII leaks and data exposures
• Capabilities: Sensitive data scanning, privacy violation detection
• Output: {'privacy_issues': list, 'severity': int}

10. Dependency Agent (DepAware)

• Role: Analyzes upstream/deprecated supply chain risks
• Capabilities: CVE scanning, deprecation analysis, maintenance assessment
• Output: {'risk_dependencies': list, 'alternatives': list}

11. Ethical Agent (Ethos-Guardian)

• Role: Checks for AI ethics, fairness, and rule-of-law compliance
• Capabilities: Bias detection, fairness assessment, legal compliance
• Output: {'ethical_concerns': list, 'legal_flags': list}

12. Evolutionary Optimizer Agent (Evo-Strategist)

• Role: Optimizes validation parameters for safety, speed, reliability
• Capabilities: Historical analysis, parameter optimization, performance tuning
• Output: {'proposed_mutations': list}

13. Veto-Validator

• Role: Aggregates all outputs and enforces consensus
• Capabilities: Quorum enforcement, final verdict determination
• Output: {'final_verdict': string, 'ledger_block': comprehensive session report}

🛠️ Installation

Prerequisites

• Python 3.8+
• Google Gemini API key(s)
• Required Python packages (see requirements.txt)

Setup

1. Clone the repository:

git clone <repository-url>
cd SunHacks

2. Install dependencies:

pip install -r requirements.txt

3. Configure environment variables:

cp env.example .env
# Edit .env with your Gemini API keys and configuration

4. Run the system:

python main.py

📋 Configuration

Environment Variables

# Gemini API Configuration
GEMINI_API_KEYS=your_api_key_1,your_api_key_2,your_api_key_3
GEMINI_MODEL=gemini-1.5-pro
GEMINI_TEMPERATURE=0.1

# System Configuration
CONSENSUS_QUORUM_THRESHOLD=0.75
MAX_CONCURRENT_AGENTS=12
SESSION_TIMEOUT_SECONDS=300

# Security Configuration
ENABLE_RED_TEAM_TESTING=true
ENABLE_ADVERSARIAL_ANALYSIS=true
THREAT_MODELING_ENABLED=true

🚀 Usage

Basic Usage

import asyncio
from main import MultiAgentValidator

async def validate_deployment():
    validator = MultiAgentValidator()
    
    deployment_context = {
        "commit_hash": "abc123def456",
        "author_metadata": {
            "name": "John Doe",
            "email": "john.doe@example.com"
        },
        "code": "def hello_world():\n    print('Hello, World!')",
        "dependencies": {
            "requests": "2.28.0",
            "numpy": "1.21.0"
        }
    }
    
    result = await validator.validate_deployment(deployment_context)
    print(f"Final Verdict: {result['final_verdict']}")
    print(f"Consensus Score: {result['consensus_score']}")

asyncio.run(validate_deployment())

Advanced Usage

# Validate with specific agents
result = await validator.validate_deployment(
    deployment_context=deployment_context,
    agent_names=["Securo-Sentinel", "Red-Team-Specter", "Veto-Validator"],
    timeout_seconds=600
)

# Get system status
status = await validator.get_system_status()
print(f"System Status: {status['system_status']}")

🔒 Security Features

Immutable Audit Logging

• All agent actions logged with SHA256 hashing
• Immutable records that cannot be modified
• Comprehensive audit trails for compliance

Zero-Trust Architecture

• No single agent can approve deployments
• Quorum-based consensus required
• Trap-and-isolate for high-confidence threats

Advanced Threat Detection

• Microsoft Red Team methodologies
• PyRIT-inspired adversarial testing
• ML threat taxonomy assessment
• Behavioral anomaly detection

📊 Monitoring & Analytics

Performance Metrics

• Agent execution times
• Success/failure rates
• Consensus accuracy
• Resource utilization

Health Monitoring

• System status monitoring
• Agent health checks
• API key rotation
• Session management

🔧 Extensibility

Adding New Agents

1. Create agent class inheriting from ValidatorAgent
2. Implement required methods: validate(), get_prompt_template(), parse_response()
3. Add agent to _initialize_agents() in main.py
4. Update agent imports

Custom Validation Rules

• Modify system_constitution.py for new rules
• Update agent prompts for new requirements
• Extend consensus logic in VetoValidator

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Implement your changes
4. Add tests and documentation
5. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Microsoft AI Red Team (PyRIT) for red teaming methodologies
• Google Gemini for LLM capabilities
• OpenAI for AI safety research
• The open-source community for inspiration and tools

📞 Support

For questions, issues, or contributions:

• Create an issue in the repository
• Contact the development team
• Check the documentation for common solutions

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Note: This system is designed for advanced security validation and should be used responsibly. Always follow security best practices and comply with applicable regulations.