# Security Policy

## Overview

DeepXR is committed to maintaining the security and integrity of the Helion-2.5-Rnd model and its associated infrastructure. This document outlines our security policies, vulnerability reporting procedures, and best practices for secure deployment.

## Supported Versions

Security updates and patches are provided for the following versions:

| Version | Supported          | Status |
| ------- | ------------------ | ------ |
| 2.5.x   | :white_check_mark: | Active |
| 2.4.x   | :x:                | EOL    |
| < 2.4   | :x:                | EOL    |

## Security Features

### Model Security

1. **SafeTensors Format**
   - All model weights are stored using SafeTensors format
   - Prevents arbitrary code execution during model loading
   - Validates tensor metadata and structure
   - Faster and safer than pickle-based formats

2. **Weight Integrity**
   - SHA256 checksums provided for all model files
   - Verify file integrity before loading
   - Detect tampering or corruption

3. **No Quantization**
   - Model provided in full precision (BF16/FP16)
   - Eliminates quantization-related vulnerabilities
   - Maintains deterministic behavior

### Inference Security

1. **Input Validation**
   - Maximum token length enforcement
   - Character encoding validation
   - Malicious pattern detection
   - Rate limiting per client

2. **Output Filtering**
   - Content safety filters
   - PII detection and redaction
   - Toxicity monitoring
   - Prompt injection detection

3. **API Security**
   - TLS 1.3 encryption for all communications
   - API key authentication
   - Request signature verification
   - CORS policy enforcement

## Reporting a Vulnerability

### How to Report

If you discover a security vulnerability in Helion-2.5-Rnd, please report it responsibly:

1. **DO NOT** create a public GitHub issue
2. Email security@deepxr.ai with details
3. Include:
   - Description of the vulnerability
   - Steps to reproduce
   - Potential impact assessment
   - Suggested mitigation (if any)

### What to Expect

- **Initial Response**: Within 48 hours
- **Status Update**: Within 7 days
- **Resolution Timeline**: Varies by severity
  - Critical: 1-7 days
  - High: 7-30 days
  - Medium: 30-90 days
  - Low: 90+ days

### Disclosure Policy

- We follow coordinated disclosure principles
- Security advisories published after patches are available
- Credit given to reporters (unless anonymous preferred)
- CVE IDs assigned for significant vulnerabilities

## Security Best Practices

### Deployment Security

#### Network Security

```yaml
# Example: Secure nginx configuration
server {
    listen 443 ssl http2;
    ssl_protocols TLSv1.3;
    ssl_ciphers HIGH:!aNULL:!MD5;
    ssl_prefer_server_ciphers on;
    
    # Security headers
    add_header Strict-Transport-Security "max-age=31536000" always;
    add_header X-Frame-Options "SAMEORIGIN" always;
    add_header X-Content-Type-Options "nosniff" always;
    add_header X-XSS-Protection "1; mode=block" always;
}
```

#### Docker Security

```dockerfile
# Use non-root user
RUN useradd -m -u 1000 helion
USER helion

# Read-only root filesystem
docker run --read-only --tmpfs /tmp:rw,noexec,nosuid

# Drop capabilities
docker run --cap-drop=ALL --cap-add=NET_BIND_SERVICE

# Resource limits
docker run --memory="256g" --cpus="64"
```

#### Kubernetes Security

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: helion-pod
spec:
  securityContext:
    runAsNonRoot: true
    runAsUser: 1000
    fsGroup: 1000
  containers:
  - name: helion
    securityContext:
      allowPrivilegeEscalation: false
      readOnlyRootFilesystem: true
      capabilities:
        drop:
        - ALL
```

### Input Validation

#### Python Implementation

```python
import re
from typing import Optional

class InputValidator:
    """Validate and sanitize user inputs"""
    
    MAX_LENGTH = 131072
    MAX_TOKENS = 8192
    
    @staticmethod
    def validate_prompt(prompt: str) -> tuple[bool, Optional[str]]:
        """Validate prompt input"""
        
        # Length check
        if len(prompt) > InputValidator.MAX_LENGTH:
            return False, "Prompt exceeds maximum length"
        
        # Character validation
        if not prompt.isprintable() and not prompt.isspace():
            return False, "Prompt contains invalid characters"
        
        # Injection detection
        dangerous_patterns = [
            r'<script',
            r'javascript:',
            r'on\w+\s*=',
            r'\beval\(',
            r'\bexec\(',
        ]
        
        for pattern in dangerous_patterns:
            if re.search(pattern, prompt, re.IGNORECASE):
                return False, "Potential injection detected"
        
        return True, None
    
    @staticmethod
    def sanitize_output(text: str) -> str:
        """Sanitize model output"""
        # Remove potential XSS vectors
        text = re.sub(r'<script.*?</script>', '', text, flags=re.DOTALL | re.IGNORECASE)
        text = re.sub(r'javascript:', '', text, flags=re.IGNORECASE)
        return text
```

### Authentication

#### API Key Management

```python
import secrets
import hashlib
from datetime import datetime, timedelta

class APIKeyManager:
    """Secure API key management"""
    
    @staticmethod
    def generate_key() -> str:
        """Generate cryptographically secure API key"""
        return secrets.token_urlsafe(32)
    
    @staticmethod
    def hash_key(key: str) -> str:
        """Hash API key for storage"""
        return hashlib.sha256(key.encode()).hexdigest()
    
    @staticmethod
    def verify_key(key: str, stored_hash: str) -> bool:
        """Verify API key against stored hash"""
        return hashlib.sha256(key.encode()).hexdigest() == stored_hash
```

### Rate Limiting

```python
from collections import defaultdict
from time import time

class RateLimiter:
    """Token bucket rate limiter"""
    
    def __init__(self, requests_per_minute: int = 60):
        self.rate = requests_per_minute / 60.0
        self.buckets = defaultdict(lambda: {'tokens': requests_per_minute, 'last': time()})
    
    def allow_request(self, client_id: str) -> bool:
        """Check if request is allowed"""
        bucket = self.buckets[client_id]
        now = time()
        
        # Add tokens based on elapsed time
        elapsed = now - bucket['last']
        bucket['tokens'] = min(
            self.rate * 60,
            bucket['tokens'] + elapsed * self.rate
        )
        bucket['last'] = now
        
        # Check if request allowed
        if bucket['tokens'] >= 1:
            bucket['tokens'] -= 1
            return True
        return False
```

## Content Safety

### Filtering Implementation

```python
import re
from typing import List, Tuple

class ContentFilter:
    """Content safety filtering"""
    
    # Configurable toxicity patterns
    TOXIC_PATTERNS = [
        r'\b(violence|harm|kill|attack)\b',
        r'\b(hate|racist|sexist)\b',
        r'\b(illegal|unlawful|criminal)\b',
    ]
    
    # PII patterns
    PII_PATTERNS = {
        'email': r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
        'ssn': r'\b\d{3}-\d{2}-\d{4}\b',
        'phone': r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b',
        'credit_card': r'\b\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}\b',
    }
    
    @classmethod
    def check_toxicity(cls, text: str) -> Tuple[bool, List[str]]:
        """Check for toxic content"""
        violations = []
        
        for pattern in cls.TOXIC_PATTERNS:
            if re.search(pattern, text, re.IGNORECASE):
                violations.append(pattern)
        
        return len(violations) == 0, violations
    
    @classmethod
    def detect_pii(cls, text: str) -> List[str]:
        """Detect personally identifiable information"""
        found_pii = []
        
        for pii_type, pattern in cls.PII_PATTERNS.items():
            if re.search(pattern, text):
                found_pii.append(pii_type)
        
        return found_pii
    
    @classmethod
    def redact_pii(cls, text: str) -> str:
        """Redact PII from text"""
        for pii_type, pattern in cls.PII_PATTERNS.items():
            text = re.sub(pattern, f'[REDACTED_{pii_type.upper()}]', text)
        
        return text
```

## Monitoring and Auditing

### Security Logging

```python
import logging
import json
from datetime import datetime

class SecurityLogger:
    """Security event logging"""
    
    def __init__(self, log_file: str = "security.log"):
        self.logger = logging.getLogger("security")
        handler = logging.FileHandler(log_file)
        handler.setFormatter(logging.Formatter('%(message)s'))
        self.logger.addHandler(handler)
        self.logger.setLevel(logging.INFO)
    
    def log_event(self, event_type: str, details: dict):
        """Log security event"""
        event = {
            'timestamp': datetime.utcnow().isoformat(),
            'type': event_type,
            'details': details
        }
        self.logger.info(json.dumps(event))
    
    def log_authentication(self, client_id: str, success: bool):
        """Log authentication attempt"""
        self.log_event('authentication', {
            'client_id': client_id,
            'success': success
        })
    
    def log_violation(self, client_id: str, violation_type: str, details: str):
        """Log security violation"""
        self.log_event('violation', {
            'client_id': client_id,
            'violation_type': violation_type,
            'details': details
        })
```

## Incident Response

### Response Procedure

1. **Detection**: Identify security incident
2. **Containment**: Isolate affected systems
3. **Investigation**: Determine scope and impact
4. **Remediation**: Apply fixes and patches
5. **Recovery**: Restore normal operations
6. **Review**: Post-incident analysis

### Contact Information

- **Security Team**: security@deepxr.ai
- **Emergency**: +1-555-DEEPXR-SEC
- **PGP Key**: Available at https://deepxr.ai/pgp-key.asc

## Compliance

### Standards Adherence

- **OWASP Top 10**: Protection against common vulnerabilities
- **CWE/SANS Top 25**: Mitigation of dangerous software errors
- **NIST Cybersecurity Framework**: Aligned with framework guidelines
- **ISO 27001**: Information security management

### Data Privacy

- **GDPR Compliance**: EU data protection regulation
- **CCPA Compliance**: California Consumer Privacy Act
- **Data Minimization**: Collect only necessary information
- **Right to Erasure**: Support for data deletion requests

## Updates

This security policy is reviewed quarterly and updated as needed. Last updated: 2025-01-30

## Acknowledgments

We thank the security research community for responsible disclosure and continuous improvement of our security posture.

---

**DeepXR Security Team**  
security@deepxr.ai  
https://deepxr.ai/security