Helion-V2.5-Rnd / SECURITY.md
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# 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
```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**: [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