chorus-services/modules/shhh/SHHH_HYPERCORE_MONITOR_PLAN.md

🔒 SHHH Hypercore Log Monitor - Implementation Plan

Executive Summary

This plan outlines the creation of a Python application that monitors our hypercore log to ensure no secrets are leaked in BZZZ messages, based on the SHHH module's secrets detection framework.

Project Overview

Objective

Create a real-time monitoring system that:

• Monitors hypercore log entries for secret patterns
• Detects potential secrets in BZZZ P2P messages before they propagate
• Quarantines suspicious entries and triggers automatic remediation
• Provides audit trails and security dashboard for compliance

Architecture Integration

• Hypercore Log: Source of truth for all CHORUS Services events
• BZZZ Network: P2P messaging layer that could inadvertently transmit secrets
• SHHH Module: Existing secrets detection framework and patterns
• Monitoring App: New Python application bridging these systems

Technical Requirements

1. Hypercore Log Integration

# Real-time log monitoring
- Stream hypercore entries as they're written
- Parse BZZZ message payloads for secret patterns
- Filter for message types that could contain secrets
- Handle log rotation and recovery scenarios

2. Secret Detection Engine

Based on SHHH's patterns.yaml framework:

patterns:
  AWS_ACCESS_KEY:
    regex: "AKIA[0-9A-Z]{16}"
    severity: "HIGH"
    confidence: 0.95
    active: true
  GITHUB_TOKEN:
    regex: "ghp_[0-9A-Za-z]{36}"
    severity: "HIGH"
    confidence: 0.92
    active: true
  PRIVATE_KEY:
    regex: "-----BEGIN [A-Z ]*PRIVATE KEY-----"
    severity: "CRITICAL"
    confidence: 0.98
    active: true

3. Quarantine & Response System

• Immediate: Block message propagation in BZZZ network
• Log: Store quarantined entries in PostgreSQL
• Alert: Notify security team via webhooks
• Revoke: Trigger automatic secret revocation APIs

Implementation Architecture

Phase 1: Core Monitoring System (Weeks 1-2)

1.1 Hypercore Log Reader

# /shhh-monitor/core/hypercore_reader.py
class HypercoreReader:
    def __init__(self, log_path: str):
        self.log_path = log_path
        self.position = 0
        
    def stream_entries(self) -> Iterator[LogEntry]:
        """Stream new hypercore entries in real-time"""
        # Tail-like functionality with inotify
        # Parse hypercore binary format
        # Yield structured LogEntry objects
        
    def parse_bzzz_message(self, entry: LogEntry) -> Optional[BzzzMessage]:
        """Extract BZZZ message payload from hypercore entry"""
        # Decode BZZZ message format
        # Extract message content and metadata
        # Return structured message or None

1.2 Secret Detection Engine

# /shhh-monitor/core/detector.py
class SecretDetector:
    def __init__(self, patterns_file: str = "patterns.yaml"):
        self.patterns = self.load_patterns(patterns_file)
        
    def scan_message(self, message: BzzzMessage) -> List[SecretMatch]:
        """Scan BZZZ message for secret patterns"""
        matches = []
        for pattern_name, pattern in self.patterns.items():
            if pattern["active"]:
                matches.extend(self.apply_regex(message, pattern))
        return matches
        
    def redact_secret(self, text: str, match: SecretMatch) -> str:
        """Redact detected secret while preserving context"""
        # Replace secret with asterisks, keep first/last chars
        # Maintain log readability for analysis

1.3 Quarantine System

# /shhh-monitor/core/quarantine.py
class QuarantineManager:
    def __init__(self, db_connection: str):
        self.db = psycopg2.connect(db_connection)
        
    def quarantine_message(self, message: BzzzMessage, matches: List[SecretMatch]):
        """Store quarantined message and block propagation"""
        # Insert into quarantine table
        # Generate alert payload
        # Trigger BZZZ network block
        
    def send_alert(self, severity: str, secret_type: str, redacted_content: str):
        """Send webhook alerts for detected secrets"""
        # POST to security webhook endpoints
        # Different payloads for AWS, GitHub, Slack tokens
        # Include revocation recommendations

Phase 2: BZZZ Network Integration (Weeks 3-4)

2.1 BZZZ Message Interceptor

# /shhh-monitor/integrations/bzzz_interceptor.py
class BzzzInterceptor:
    def __init__(self, bzzz_config: Dict):
        self.bzzz_client = BzzzClient(bzzz_config)
        
    def install_message_hook(self):
        """Install pre-send hook in BZZZ network layer"""
        # Intercept messages before P2P transmission
        # Scan with SecretDetector
        # Block or allow message propagation
        
    def block_message(self, message_id: str, reason: str):
        """Prevent message from propagating in P2P network"""
        # Mark message as blocked in BZZZ
        # Log blocking reason
        # Notify sender agent of security violation

2.2 Real-time Processing Pipeline

# /shhh-monitor/pipeline/processor.py
class MessageProcessor:
    def __init__(self, detector: SecretDetector, quarantine: QuarantineManager):
        self.detector = detector
        self.quarantine = quarantine
        
    async def process_hypercore_stream(self):
        """Main processing loop for hypercore monitoring"""
        async for entry in self.hypercore_reader.stream_entries():
            if bzzz_message := self.parse_bzzz_message(entry):
                matches = self.detector.scan_message(bzzz_message)
                if matches:
                    await self.handle_secret_detection(bzzz_message, matches)
                    
    async def handle_secret_detection(self, message: BzzzMessage, matches: List[SecretMatch]):
        """Handle detected secrets with appropriate response"""
        # Determine severity level
        # Quarantine message
        # Send alerts
        # Trigger revocation if needed
        # Update detection statistics

Phase 3: Admin Dashboard & Feedback Loop (Weeks 5-6)

3.1 FastAPI Backend

# /shhh-monitor/api/main.py
from fastapi import FastAPI, Depends
from .models import QuarantineEntry, SecretPattern, RevocationEvent

app = FastAPI(title="SHHH Hypercore Monitor API")

@app.get("/quarantine", response_model=List[QuarantineEntry])
async def get_quarantine_entries():
    """List all quarantined messages"""
    
@app.post("/quarantine/{entry_id}/review")
async def review_quarantine_entry(entry_id: int, action: str):
    """Mark quarantine entry as reviewed/false positive"""
    
@app.get("/patterns", response_model=List[SecretPattern])
async def get_detection_patterns():
    """List all secret detection patterns"""
    
@app.post("/patterns/{pattern_name}/update")
async def update_pattern(pattern_name: str, pattern: SecretPattern):
    """Update regex pattern based on feedback"""

3.2 React Dashboard Frontend

// /shhh-monitor/dashboard/src/components/QuarantineDashboard.tsx
interface QuarantineDashboard {
  // Real-time quarantine feed
  // Pattern management interface
  // Revocation status tracking
  // Security metrics and charts
  // Alert configuration
}

Phase 4: Automated Response & Learning (Weeks 7-8)

4.1 Automated Secret Revocation

# /shhh-monitor/automation/revocation.py
class SecretRevoker:
    def __init__(self):
        self.aws_client = boto3.client('iam')
        self.github_client = github.Github()
        self.slack_client = slack.WebClient()
        
    async def revoke_aws_key(self, access_key_id: str):
        """Automatically deactivate AWS access key"""
        self.aws_client.update_access_key(
            AccessKeyId=access_key_id,
            Status='Inactive'
        )
        
    async def revoke_github_token(self, token: str):
        """Revoke GitHub personal access token"""
        # Use GitHub's token scanning API
        # Or organization webhook for automatic revocation
        
    async def revoke_slack_token(self, token: str):
        """Revoke Slack bot/user token"""
        # Use Slack Admin API
        # Invalidate token and rotate if possible

4.2 Meta-Learning System

# /shhh-monitor/learning/meta_curator.py
class MetaCurator:
    def __init__(self, llm_client):
        self.llm = llm_client
        
    async def analyze_false_positives(self, entries: List[QuarantineEntry]):
        """Use LLM to improve regex patterns"""
        # Analyze patterns in false positives
        # Generate regex refinements
        # Submit for human approval
        
    async def detect_new_secret_types(self, quarantine_history: List[QuarantineEntry]):
        """Identify new types of secrets to detect"""
        # Look for patterns in undetected secrets
        # Generate new regex proposals
        # Calculate confidence scores

Database Schema

Core Tables

-- Quarantined messages
CREATE TABLE quarantine (
    id SERIAL PRIMARY KEY,
    timestamp TIMESTAMPTZ NOT NULL,
    hypercore_position BIGINT NOT NULL,
    bzzz_message_id TEXT NOT NULL,
    secret_type TEXT NOT NULL,
    severity TEXT CHECK (severity IN ('LOW', 'MEDIUM', 'HIGH', 'CRITICAL')),
    confidence NUMERIC(3,2),
    redacted_content TEXT NOT NULL,
    full_content_hash TEXT NOT NULL, -- For audit purposes
    reviewed BOOLEAN DEFAULT FALSE,
    review_action TEXT, -- 'false_positive', 'confirmed', 'uncertain'
    reviewer TEXT,
    review_timestamp TIMESTAMPTZ
);

-- Pattern history and evolution
CREATE TABLE patterns_history (
    id SERIAL PRIMARY KEY,
    pattern_name TEXT NOT NULL,
    old_regex TEXT,
    new_regex TEXT,
    action TEXT CHECK (action IN ('add', 'update', 'remove')),
    confidence NUMERIC(3,2),
    status TEXT CHECK (status IN ('approved', 'pending', 'rejected')),
    submitted_by TEXT NOT NULL, -- 'human', 'meta_curator', 'feedback_system'
    approved_by TEXT,
    decision_reason TEXT,
    timestamp TIMESTAMPTZ DEFAULT CURRENT_TIMESTAMP
);

-- Revocation events tracking
CREATE TABLE revocations (
    id SERIAL PRIMARY KEY,
    quarantine_id INTEGER REFERENCES quarantine(id),
    secret_type TEXT NOT NULL,
    revocation_method TEXT NOT NULL, -- 'aws_api', 'github_api', 'manual'
    status TEXT CHECK (status IN ('success', 'failed', 'pending')),
    response_data JSONB, -- API response details
    timestamp TIMESTAMPTZ DEFAULT CURRENT_TIMESTAMP
);

-- Performance metrics
CREATE TABLE detection_metrics (
    id SERIAL PRIMARY KEY,
    date DATE NOT NULL,
    total_messages_scanned INTEGER,
    secrets_detected INTEGER,
    false_positives INTEGER,
    patterns_updated INTEGER,
    avg_detection_latency_ms INTEGER
);

Security Considerations

1. Secure Secret Storage

• Never store actual secrets in quarantine database
• Use cryptographic hashes for audit trails
• Redact sensitive content while preserving detection context
• Implement secure deletion for expired quarantine entries

2. Access Control

• Role-based access to dashboard (security admin, reviewer, read-only)
• Audit logging for all administrative actions
• OAuth integration with existing identity provider
• API key authentication for automated systems

3. Network Security

• TLS encryption for all API communication
• VPN/private network access to monitoring systems
• Rate limiting to prevent API abuse
• IP allowlisting for critical endpoints

Deployment Architecture

Development Environment

# docker-compose.dev.yml
services:
  shhh-monitor:
    build: .
    environment:
      - DATABASE_URL=postgresql://dev:dev@postgres:5432/shhh_dev
      - HYPERCORE_LOG_PATH=/data/hypercore.log
      - BZZZ_CONFIG_PATH=/config/bzzz.yaml
    volumes:
      - ./data:/data
      - ./config:/config
      
  postgres:
    image: postgres:15
    environment:
      POSTGRES_DB: shhh_dev
      POSTGRES_USER: dev
      POSTGRES_PASSWORD: dev
      
  redis:
    image: redis:7-alpine
    # For caching and real-time notifications

Production Deployment

# docker-compose.prod.yml
services:
  shhh-monitor:
    image: registry.home.deepblack.cloud/tony/shhh-monitor:latest
    deploy:
      replicas: 2
      placement:
        constraints:
          - node.role == manager
    environment:
      - DATABASE_URL=postgresql://shhh:${SHHH_DB_PASSWORD}@postgres:5432/shhh_prod
      - HYPERCORE_LOG_PATH=/hypercore/current.log
    networks:
      - shhh_network
      - tengig # For dashboard access

Performance Requirements

Latency Targets

• Log Processing: <50ms per hypercore entry
• Secret Detection: <10ms per BZZZ message
• Alert Generation: <100ms for critical secrets
• Dashboard Response: <200ms for UI queries

Throughput Targets

• Message Scanning: 1000 messages/second
• Concurrent Users: 10+ dashboard users
• Alert Volume: 100+ alerts/hour during peak
• Database Queries: <5ms average response time

Monitoring & Observability

Metrics Collection

# Prometheus metrics
messages_scanned_total = Counter('shhh_messages_scanned_total')
secrets_detected_total = Counter('shhh_secrets_detected_total', ['secret_type', 'severity'])
detection_latency = Histogram('shhh_detection_latency_seconds')
quarantine_size = Gauge('shhh_quarantine_entries_total')

Health Checks

• Hypercore connectivity: Verify log file access
• Database health: Connection pool status
• BZZZ integration: P2P network connectivity
• Alert system: Webhook endpoint validation

Logging Strategy

# Structured logging with correlation IDs
{
  "timestamp": "2025-08-02T13:45:00Z",
  "level": "WARNING",
  "event": "secret_detected",
  "correlation_id": "req_123",
  "secret_type": "AWS_ACCESS_KEY",
  "severity": "HIGH",
  "hypercore_position": 58321,
  "bzzz_message_id": "msg_abc123",
  "redacted_content": "AKIA****XYZ found in agent message"
}

Testing Strategy

Unit Tests

• Regex pattern validation: Test against known secret formats
• Message parsing: Verify hypercore and BZZZ format handling
• Quarantine logic: Test storage and retrieval functions
• Alert generation: Mock webhook endpoint testing

Integration Tests

• End-to-end workflow: Log → Detection → Quarantine → Alert
• Database operations: PostgreSQL CRUD operations
• BZZZ integration: Message interception and blocking
• API endpoints: FastAPI route testing

Security Tests

• Input validation: SQL injection, XSS prevention
• Access control: Role-based permission testing
• Data protection: Verify secret redaction and hashing
• Performance: Load testing with high message volume

Rollout Plan

Phase 1: Foundation (Weeks 1-2)

• ✅ Core monitoring system with hypercore integration
• ✅ Basic secret detection using SHHH patterns
• ✅ PostgreSQL quarantine storage
• ✅ Simple alerting via webhooks

Phase 2: Integration (Weeks 3-4)

• ✅ BZZZ network message interception
• ✅ Real-time processing pipeline
• ✅ Enhanced pattern management
• ✅ Performance optimization

Phase 3: Dashboard (Weeks 5-6)

• ✅ FastAPI backend with full CRUD operations
• ✅ React dashboard for quarantine management
• ✅ Pattern editor and approval workflow
• ✅ Security metrics and reporting

Phase 4: Automation (Weeks 7-8)

• ✅ Automated secret revocation APIs
• ✅ Meta-learning system for pattern improvement
• ✅ Production deployment and monitoring
• ✅ Documentation and team training

Success Criteria

Security Effectiveness

• Zero secret leaks in BZZZ P2P network after deployment
• <1% false positive rate for secret detection
• <30 seconds average time to detect and quarantine secrets
• 99.9% uptime for monitoring system

Operational Excellence

• Complete audit trail for all security events
• Self-improving pattern detection through feedback
• Scalable architecture supporting growth in CHORUS usage
• Team adoption with trained security administrators

Risk Mitigation

Technical Risks

• Performance impact: Monitor hypercore processing overhead
• False positives: Implement feedback loop for pattern refinement
• BZZZ integration: Maintain compatibility with P2P protocol evolution
• Data loss: Backup quarantine database and implement recovery procedures

Security Risks

• Bypassing detection: Regular pattern updates and meta-learning
• System compromise: Network isolation and access controls
• Secret exposure: Implement proper redaction and audit procedures
• Alert fatigue: Tune detection thresholds to minimize noise

Conclusion

This SHHH Hypercore Log Monitor provides comprehensive protection against secret leakage in the CHORUS Services BZZZ P2P network. By implementing real-time detection, automated response, and continuous learning, we ensure that sensitive information remains secure while maintaining the performance and functionality of the distributed AI orchestration platform.

The system builds upon the existing SHHH framework while adding the specific hypercore and BZZZ integrations needed for CHORUS Services. The phased rollout ensures stability and allows for iterative improvement based on real-world usage patterns.