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https://github.com/dewitt4/ai-model-security-monitor
Security monitoring tool that helps protect AI models from common attacks.
https://github.com/dewitt4/ai-model-security-monitor
ai ai-security security-tools
Last synced: 16 days ago
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Security monitoring tool that helps protect AI models from common attacks.
- Host: GitHub
- URL: https://github.com/dewitt4/ai-model-security-monitor
- Owner: dewitt4
- License: mit
- Created: 2024-11-21T16:07:33.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-21T16:26:11.000Z (about 2 months ago)
- Last Synced: 2024-11-21T17:23:12.829Z (about 2 months ago)
- Topics: ai, ai-security, security-tools
- Language: Python
- Homepage:
- Size: 27.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Security: security_team_alerts.py
Awesome Lists containing this project
README
# ai-model-security-monitor
Security monitoring tool that helps protect AI models from common attacks.
Author: DeWitt Gibson https://www.linkedin.com/in/dewitt-gibson/
The tool provides these key security features:
## Input validation:
Size limits
Value range checks
Null/empty input detection
Format validation
## Attack detection:
Adversarial pattern detection
Unusual input structure detection
Gradient analysis
## Request monitoring:
Request logging
Rate monitoring
Repeated input detection
Pattern analysis
## Detailed reporting:
Validation results
Detected issues
Request analysis
Security recommendations
## Security Team Alerting
# AI Model Security Tools
A comprehensive security toolkit for protecting AI model deployments, including model protection, threat monitoring, and security assessment capabilities.
## Components
1. **AIModelProtector**: Real-time protection and monitoring for AI model endpoints
2. **AISecurityMonitor**: Advanced threat detection and team notification system
3. **ChatbotThreatModeler**: Threat assessment and security evaluation for chatbot implementations
## Installation
```bash
pip install -r requirements.txt
```
Required dependencies:
```
numpy>=1.21.0
pandas>=1.3.0
typing>=3.7.4
logging>=0.5.1.2
smtplib
email
```
## Quick Start
### Basic Protection Setup
```python
from ai_model_protector import AIModelProtector
from ai_security_monitor import AISecurityMonitor
from chatbot_threat_modeler import ChatbotThreatModeler
# Initialize base protection
protector = AIModelProtector(
model_name="production_model",
input_constraints={
"max_size": 1000000,
"max_value": 1.0,
"min_value": -1.0,
"max_gradient": 50
}
)
# Setup security monitoring
monitor = AISecurityMonitor(
model_name="production_model",
alert_settings={
"email_recipients": ["[email protected]"],
"smtp_settings": {
"server": "smtp.company.com",
"port": 587,
"sender": "[email protected]",
"use_tls": True
},
"alert_thresholds": {
"max_requests_per_minute": 100,
"suspicious_pattern_threshold": 0.8
}
}
)
# Initialize threat modeling
threat_modeler = ChatbotThreatModeler()
```
### Deployment Integration
```python
def process_model_request(input_data, request_metadata):
# 1. Check security protections
security_check = protector.protect(input_data)
if not security_check["allow_inference"]:
return {"error": "Security check failed", "details": security_check}
# 2. Monitor for threats
threat_assessment = monitor.detect_threat({
"ip_address": request_metadata["ip"],
"input_data": input_data
})
if threat_assessment["severity"] == "high":
return {"error": "Request blocked due to security risk"}
# 3. Process request if safe
try:
prediction = model.predict(input_data)
return {"prediction": prediction}
except Exception as e:
monitor.log_incident({
"type": "inference_error",
"details": str(e)
})
return {"error": "Processing failed"}
```
## Security Features
### Model Protection
- Input validation and sanitization
- Pattern analysis for adversarial attacks
- Request rate limiting
- Anomaly detection
### Security Monitoring
- Real-time threat detection
- Team notifications
- Incident logging
- Traffic analysis
- IP-based monitoring
### Threat Modeling
- Security control assessment
- Risk scoring
- Threat identification
- Compliance checking
- Recommendation generation
## Configuration
### Environment Variables
```bash
SECURITY_LOG_PATH=/path/to/logs
ALERT_SMTP_SERVER=smtp.company.com
ALERT_SMTP_PORT=587
[email protected]
[email protected]
```
### Security Thresholds
```python
SECURITY_CONFIG = {
"max_requests_per_minute": 100,
"suspicious_pattern_threshold": 0.8,
"max_failed_attempts": 5,
"session_timeout": 3600,
"min_request_interval": 1.0
}
```
## Best Practices
1. **API Key Management**
- Rotate keys regularly
- Use separate keys for different environments
- Monitor key usage
2. **Logging**
- Enable comprehensive logging
- Store logs securely
- Implement log rotation
3. **Monitoring**
- Set up alerts for suspicious activities
- Monitor resource usage
- Track error rates
4. **Regular Assessment**
- Run threat modeling weekly
- Review security controls
- Update security thresholds
## Contributing
Please see CONTRIBUTING.md for guidelines on contributing to this project.
## License
MIT License - See LICENSE.md for details