OWASP LLM Top 10: CIS CONTROLS

```python
import re
from typing import List

# Mock functions to represent the validation checks for each LLM vulnerability
# These functions should be implemented with actual validation logic based on the system's architecture

def validate_prompt_injection(input_data: str) -> bool:
    """
    Validates input data to protect against prompt injection.
    Implement custom validation logic based on the system's requirements.
    """
    # Example: reject input containing scripting elements or unexpected operators
    return not bool(re.search(r'[<>{}();]', input_data))

def validate_output_handling(output_data: str) -> bool:
    """
    Checks for insecure output handling.
    Implement checks to ensure output data does not contain sensitive information.
    """
    # Example: ensure output does not contain API keys or personal data
    return "API_KEY" not in output_data and "personal_info" not in output_data

def validate_training_data(data: List[str]) -> bool:
    """
    Ensures the integrity of training data to protect against data poisoning.
    This could involve checksum verification, source validation, etc.
    """
    # Placeholder for actual validation logic
    return True

def validate_dos_protection(system_config: dict) -> bool:
    """
    Validates system configuration to minimize the risk of DoS attacks.
    This could involve checking network configurations, rate limiting settings, etc.
    """
    # Placeholder for actual validation logic
    return True

# Additional validation functions should be implemented for LLM04 to LLM10

# Example of using the validation functions
if __name__ == "__main__":
    input_data = "<input data>"
    output_data = "<output data>"
    training_data = ["data1", "data2"]
    system_config = {"config1": "value1"}

    # Perform the validations
    if not validate_prompt_injection(input_data):
        print("Prompt injection vulnerability detected.")
    if not validate_output_handling(output_data):
        print("Insecure output handling detected.")
    if not validate_training_data(training_data):
        print("Training data poisoning vulnerability detected.")
    if not validate_dos_protection(system_config):
        print("Model Denial of Service vulnerability detected.")

    # Add additional validation checks as necessary


### Automated Validation Tools

For the automated validation of these controls and vulnerabilities, you can leverage several tools, depending on the nature of the system and its architecture:

1. **Static Code Analysis Tools**: Tools like Bandit (for Python), FindBugs (for Java), and others can automatically detect common security issues in code.

2. **Dynamic Analysis Tools (DAST)**: Tools like ZAP or Burp Suite can test running applications for vulnerabilities such as injection attacks, insecure server configurations, and more.

3. **Dependency Checkers**: Tools like OWASP Dependency-Check can analyze project dependencies for known vulnerabilities, particularly useful for LLM05.

4. **Security Linters**: Linters like ESLint with security plugin for JavaScript, or Brakeman for Ruby on Rails, can detect insecure coding patterns before they go into production.

5. **Data Validation Libraries**: For Python, libraries like Pydantic or Cerberus can help in validating input data formats and types, assisting in preventing issues like LLM01.

Each of these tools can be integrated into your CI/CD pipeline to automate the validation process. Ensure you configure each tool according to the specific needs and architecture of your LLM system to maximize their effectiveness.