OWASP LLM Top 10: ISOIEC20547 4 2020

# Data Validation and Protection Script Aligned with ISO/IEC 20547-4:2020

This Python script provides examples of how to address the OWASP Top 10 vulnerabilities for LLMs with practices aligned to ISO/IEC 20547-4:2020 standards. It includes data validation, secure data handling, and recommendations for further actions and tools.

```python
import re
from typing import Any, Dict
# Assuming a hypothetical encryption module for demonstration
from secure_module import encrypt, decrypt, rate_limiter, access_control

# Utilities for validation and sanitation
def sanitize_input(input_text: str) -> str:
    """Sanitize inputs to prevent injection attacks."""
    return re.sub(r'[^\w\s]', '', input_text)

def sanitize_output(output_text: str) -> str:
    """Sanitize outputs to prevent XSS and other injection vulnerabilities."""
    return (
        output_text.replace('&', '&amp;')
        .replace('<', '&lt;')
        .replace('>', '&gt;')
        .replace('"', '&quot;')
        .replace("'", '&#x27;')
    )

# Example data validator
def validate_data_structure(data: Any, schema: Dict) -> bool:
    """Validate data against a predefined schema."""
    # Implementation of schema validation goes here
    return True

# LLM03: Training Data Poisoning
def validate_training_data(data: Any) -> bool:
    """Placeholder function to demonstrate training data validation."""
    # Implement validation logic here
    return True

# LLM04: Model Denial of Service
# Implemented as a decorator for rate limiting
@rate_limiter(limit=100, per=60)  # Example: limit to 100 requests per minute
def process_request(request_data: Any):
    """Process an API request with rate limiting."""
    pass  # Actual request processing logic goes here

# LLM05: Supply-Chain Vulnerabilities - Addressed through external tools; see comments

# LLM06: Sensitive Information Disclosure
def encrypt_sensitive_data(data: Any) -> str:
    """Encrypt sensitive information."""
    return encrypt(data)

def decrypt_sensitive_data(data: str) -> Any:
    """Decrypt sensitive information."""
    return decrypt(data)

# LLM07: Insecure Plugin Design - Addressed through secure development practices; see comments

# LLM08: Excessive Agency
def enforce_decision_boundaries(decision: Any) -> bool:
    """Enforce boundaries on decision-making capabilities."""
    # Implement logic to limit LLM decision-making capabilities
    return True

# LLM09: Overreliance - Mitigated through user education and awareness; not directly code-related

# LLM10: Model Theft
def protect_model_access():
    """Protect LLM models using access controls."""
    # This would interface with infrastructure to enforce access controls
    access_control()

# Note: For vulnerabilities like LLM05 and LLM07, integrating tools like Snyk, Dependabot for supply chain vulnerabilities, 
# and using static analysis tools for secure plugin design are recommended. For LLM09, developing comprehensive user training 
# programs on the limitations and responsible use of LLM technologies is essential.


### Implementation Notes:
- **LLM01 & LLM02:** Sanitization functions help prevent common injection vulnerabilities by removing or encoding potentially harmful characters.
- **LLM03:** A placeholder for data integrity checks ensures that training data has not been tampered with. Real implementations would need to compare hashes or use trusted data sources.
- **LLM04:** A rate limiter decorator can help mitigate DoS attacks by limiting the number of requests a user can make to an API within a given timeframe.
- **LLM05:** While code does not directly mitigate supply chain vulnerabilities, using automated tools like Snyk or Dependabot as part of your CI/CD pipeline can alert you to vulnerabilities in dependencies.
- **LLM06:** Encryption functions demonstrate the concept of protecting sensitive data in transit and at rest.
- **LLM07-LMM10:** These areas are more about policies, infrastructure, and external tools than direct coding practices. Comments and placeholders suggest directions for implementation.

Aligning these practices with ISO/IEC 20547-4:2020 ensures a comprehensive approach to security and privacy in the context of LLM technologies, focusing on robust data handling, secure processing environments, and awareness of the broader ecosystem's vulnerabilities.