claude-flow-novice/docs/security/GNN.md

Claude Flow Novice - Advanced orchestration platform for multi-agent AI workflows with CFN Loop architecture Includes CodeSearch (hybrid SQLite + pgvector), mem0/memgraph specialists, and all CFN skills.

# GNN Security Documentation

## GNN Security Architecture

### Core Components
- Graph Data Layer: Secure storage and access control for graph structures
- Processing Layer: Secure GNN computation with sandboxed execution
- Embedding Layer: Protected embedding generation and storage
- API Layer: Authenticated and rate-limited access endpoints

### Security Boundaries
- Input validation at all entry points
- Isolated execution environments for GNN operations
- Encrypted embedding storage with integrity checks
- Audit trail for all graph modifications and queries

## Threat Model

### Attack Vectors
- **Graph Injection**: Malicious node/edge insertion
- **Traversal Attacks**: Path manipulation for data exfiltration
- **Embedding Poisoning**: Adversarial embedding manipulation
- **Model Extraction**: Query-based model theft
- **Denial of Service**: Resource exhaustion via complex queries

### Trust Boundaries
- Untrusted user inputs
- External graph data sources
- Third-party model components
- Network communications

## Security Findings

### Critical Issues
- Insufficient input validation on graph structures
- Missing traversal depth limits
- Embedding storage lacks integrity verification
- No rate limiting on computationally expensive operations

### Medium Risk
- Incomplete audit logging for ML operations
- Weak access control granularity for graph subsets
- Missing anomaly detection for embedding patterns

### Low Risk
- Insufficient monitoring of model performance drift
- Lack of automated security testing for GNN pipelines

## Data Privacy Considerations

### Graph Anonymization
- Node ID randomization before processing
- Edge weight perturbation for k-anonymity
- Differential privacy in aggregation operations

### Sensitive Data Handling
- Attribute-level encryption for PII
- Secure multi-party computation for federated graphs
- Data minimization in feature extraction

### Compliance Requirements
- GDPR right to explanation for GNN decisions
- CCPA data deletion for graph entities
- HIPAA safeguards for healthcare graphs

## Access Controls

### Authentication
- JWT-based API authentication
- Certificate-based service authentication
- Multi-factor authentication for admin operations

### Authorization
- RBAC for graph access permissions
- Attribute-based access for node subsets
- Time-bound access for temporary operations

### Graph-Level Security
- Subgraph access controls
- Node/edge level permissions
- Query result filtering based on roles

## Embedding Security

### Generation Security
- Input sanitization before embedding
- Bounded embedding dimensions
- Deterministic seed management

### Storage Protection
- Encrypted embedding vectors
- Integrity checksums for embedding batches
- Version-controlled embedding snapshots

### Access Controls
- Role-based embedding retrieval
- Query result size limits
- Embedding watermarking for tracking

## Model Integrity

### Model Protection
- Signed model artifacts
- Runtime model verification
- Secure model loading procedures

### Training Security
- Training data validation
- Gradient clipping for poisoning prevention
- Checkpoint integrity verification

### Inference Security
- Input size and complexity limits
- Output validation and sanitization
- Model versioning and rollback capabilities

## Security Recommendations

### Immediate Actions
1. Implement comprehensive input validation for all graph operations
2. Add traversal depth limits and cycle detection
3. Deploy rate limiting for GNN API endpoints
4. Enable detailed audit logging for ML operations

### Short-term Improvements
1. Implement embedding integrity verification
2. Add anomaly detection for graph queries
3. Deploy automated security testing for GNN pipelines
4. Enhance monitoring for model performance drift

### Long-term Strategy
1. Develop formal verification for critical GNN components
2. Implement federated learning for privacy-preserving training
3. Deploy adversarial training for robustness
4. Establish continuous security monitoring and response

### Implementation Checklist
- [ ] Input validation framework deployment
- [ ] Graph traversal security controls
- [ ] Embedding integrity monitoring
- [ ] Rate limiting configuration
- [ ] Comprehensive audit logging
- [ ] Security testing automation
- [ ] Incident response procedures
- [ ] Security training for ML teams

### Monitoring Metrics
- Graph query complexity scores
- Embedding access patterns
- Model inference latency distributions
- Authentication failure rates
- Data modification audit trails
- Resource utilization thresholds