SECURITY.md

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 [email protected] 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

# 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

# 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

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

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

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

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

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

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: [email protected]
• 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
[email protected]
https://deepxr.ai/security