OWASP LLM Top 10: CycloneDX SBOM

### Automated Validation Tools Suggestion

For robust validation and security checks, it's recommended to integrate automated validation tools within your development and deployment pipelines. Here are some tools that can be useful for addressing the vulnerabilities outlined:

1. **Bandit** - A tool designed to find common security issues in Python code. Useful for general code security checks, including some aspects related to LLM vulnerabilities.
   
2. **ZAP** (Zed Attack Proxy) - For web applications using LLMs, ZAP can help find vulnerabilities in web services and APIs.

3. **PyTorch/TensorFlow Data Validators** - For ML applications, using TensorFlow Data Validation (TFDV) or similar tools for PyTorch can help ensure the quality and integrity of training data (related to LLM03).

4. **Sonatype Nexus or OWASP Dependency-Check** - These tools can help identify known vulnerabilities in third-party dependencies (LLM05).

5. **Snyk** - A tool that can be integrated into the development workflow to monitor and protect against vulnerabilities in dependencies and containers, addressing supply-chain vulnerabilities.

These tools can be incorporated into CI/CD pipelines to automate the detection of security issues and vulnerabilities, reducing the risk of deploying compromised or insecure LLM applications.

```markdown
# Data Validation for LLM Applications

This Python script provides basic structures and checks for addressing the OWASP Top 10 vulnerabilities for Large Language Model (LLM) applications, as outlined in the CycloneDX mapping markdown.

```python
# Save this script as data_validation.py

import re
from typing import Any, Dict, List
import rate_limiting_decorator
import pandas as pd

# Example of a third-party library for rate limiting
# @rate_limiting_decorator.rate_limiter(calls=10, period=1)
def validate_input(input_text: str) -> bool:
    """
    Validate input to prevent prompt injection (LLM01).
    This function can be extended with more sophisticated checks as needed.
    """
    if re.match(r'^[\w\s]+$', input_text):
        return True
    else:
        return False

def sanitize_output(output_text: str) -> str:
    """
    Sanitize output to handle insecure output (LLM02).
    Replace this simple example with more comprehensive sanitization as needed.
    """
    safe_output = re.sub(r'[<>]', '', output_text)
    return safe_output

def verify_training_data(data_path: str) -> bool:
    """
    Check the integrity of training data to mitigate training data poisoning (LLM03).
    This is a placeholder function; implement specific checks based on your data.
    """
    # Placeholder check: verify data source, structure, or content integrity.
    return True

def rate_limiter(func):
    """
    Decorator for rate limiting to prevent model denial of service (LLM04).
    Use a more sophisticated rate limiting mechanism for production.
    """
    def wrapper(*args, **kwargs):
        # Implement rate limiting logic here
        print("Rate limiting check passed")
        return func(*args, **kwargs)
    return wrapper

@rate_limiter
def model_request_handler(request_data: Any):
    """
    Handle model requests with rate limiting.
    """
    # Process the request
    pass

def validate_third_party_components(components: List[Dict[str, Any]]) -> bool:
    """
    Validate third-party components to mitigate supply-chain vulnerabilities (LLM05).
    This function should check the security of components; placeholder logic here.
    """
    # Placeholder logic: verify components' sources and versions
    return True

def anonymize_data(data: Any) -> Any:
    """
    Anonymize data to prevent sensitive information disclosure (LLM06).
    Replace with actual data anonymization as appropriate.
    """
    # Placeholder for data anonymization logic
    return data

def validate_plugins(plugins: List[str]) -> bool:
    """
    Validate plugin security to address insecure plugin design (LLM07).
    Placeholder function; implement specific plugin security validations.
    """
    # Placeholder logic: check plugin sources, versions, and known vulnerabilities
    return True

def enforce_agency_limits(decision: Any) -> Any:
    """
    Enforce limits on model's agency to prevent excessive agency (LLM08).
    Placeholder logic; define and enforce decision-making boundaries.
    """
    # Placeholder for enforcing agency limits
    return decision

def enforce_human_oversight(decisions: List[Any]) -> List[Any]:
    """
    Ensure human oversight in decision-making to mitigate overreliance (LLM09).
    Placeholder function; implement oversight mechanisms.
    """
    # Placeholder for enforcing human oversight
    return decisions

def protect_model_access():
    """
    Implement protections against model theft (LLM10).
    This function is a placeholder; use appropriate access controls and encryption.
    """
    # Placeholder for model protection logic
    print("Model access protected")

# Example usage
if __name__ == "__main__":
    input_text = "Example input"
    if validate_input(input_text):
        print("Input validated")
    else:
        print("Invalid input")