OWASP LLM Top 10: ENISA

```python
import re
from typing import Any, Dict, List
import pandas as pd  # For handling CSV file operations

# Assuming a CSV file for LLM data inputs
DATA_CSV_FILE = 'path_to_your_data_file.csv'

# LLM01: Prompt Injection - Input validation function
def validate_prompt_input(input_text: str) -> bool:
    """Basic validation to prevent prompt injection."""
    # Define illegal characters or patterns here
    pattern = re.compile(r'[<>{}]')
    return not bool(pattern.search(input_text))

# LLM02: Insecure Output Handling - Output sanitization function
def sanitize_output(output_text: str) -> str:
    """Sanitize output to prevent data leaks."""
    # Implement sanitization logic here
    sanitized_text = output_text.replace('<script>', '').replace('</script>', '')
    return sanitized_text

# LLM03: Training Data Poisoning - Data integrity check
def check_data_integrity(data: Dict[str, Any]) -> bool:
    """Check integrity of the training data."""
    # Implement integrity check logic (e.g., checksum, hash comparison)
    return True

# LLM04: Model Denial of Service - Resource allocation check
def resource_allocation_check() -> bool:
    """Check if resource allocation for the model is within limits."""
    # Implement check for resource usage (e.g., CPU, memory limits)
    return True

# LLM05: Supply-Chain Vulnerabilities - Third-party library check
def third_party_library_check(library_name: str) -> bool:
    """Check security of third-party libraries."""
    # Implement checks against a known vulnerabilities database
    return True

# LLM06: Sensitive Information Disclosure - Data encryption check
def data_encryption_check(data: str) -> bool:
    """Check if sensitive data is encrypted."""
    # Implement encryption check logic here
    return True

# LLM07: Insecure Plugin Design - Plugin security check
def plugin_security_check(plugin_name: str) -> bool:
    """Check security of plugins used by the LLM."""
    # Implement security checks for plugins
    return True

# LLM08: Excessive Agency - Decision-making capability check
def decision_making_capability_check() -> bool:
    """Ensure LLM does not exceed intended agency."""
    # Implement checks to limit LLM's decision-making capabilities
    return True

# LLM09: Overreliance - User training verification
def user_training_verification(user_id: int) -> bool:
    """Verify if a user has been trained on LLM limitations."""
    # Implement verification logic here
    return True

# LLM10: Model Theft - Access control check
def access_control_check(user_id: int) -> bool:
    """Check if access controls are properly implemented."""
    # Implement access control checks here
    return True

# Load and validate data from a CSV file
def load_and_validate_data(csv_file: str) -> List[Dict[str, Any]]:
    data = pd.read_csv(csv_file)
    validated_data = []
    for index, row in data.iterrows():
        if validate_prompt_input(row['prompt']) and check_data_integrity(row.to_dict()):
            validated_data.append(row.to_dict())
    return validated_data

if __name__ == '__main__':
    validated_data = load_and_validate_data(DATA_CSV_FILE)
    print(f"Validated Data: {validated_data}")


### Recommended Automated Validation Tools

For the aspects of data validation, protection, compliance, and security assessments highlighted in the script, the following tools and libraries can be highly effective:

1. **Input Validation and Sanitization**: Use libraries like `cerberus`, `voluptuous`, or even Python's built-in `re` module for regex-based validations.
2. **Data Integrity Checks**: Utilize cryptographic hash libraries such as `hashlib` for generating and comparing checksums or hashes of data.
3. **Resource Allocation and Monitoring**: Tools like `psutil` can help monitor system resources to prevent DoS attacks due to resource exhaustion.
4. **Third-Party Library Security**: Use `Safety` and `Bandit` to check for known vulnerabilities and security issues in dependencies.
5. **Data Encryption**: For implementing encryption, the `cryptography` library offers both high-level and low-level cryptographic primitives.
6. ** illegal_patterns = ['<script>', 'SELECT * FROM']
    return not any(pattern in input_text for pattern in illegal_patterns)

# LLM02: Insecure Output Handling - Secure output handling function
def secure_output_handling(output_data: Any) -> str:
    """Apply encoding or other security measures to the output."""
    # Example: HTML encoding to prevent XSS in web applications
    return re.sub(r'([<>"\'/])', r'\\\1', str(output_data))

# LLM03: Training Data Poisoning - Validate training data source integrity
def validate_data_source(data_source: str) -> bool:
    """Check the integrity and trustworthiness of the data source."""
    # Example: Verify the data source against a list of trusted sources
    trusted_sources = ['source1', 'source2']
    return data_source in trusted_sources

# LLM04: Model Denial of Service - Implement rate limiting
# This is typically implemented at the infrastructure level, e.g., web server or API gateway

# LLM05: Supply-Chain Vulnerabilities - Validate third-party components
def validate_third_party_components(component_list: List[str]) -> bool:
    """Ensure third-party components are secure and trusted."""
    # Example: Check components against a vulnerability database or a list of approved components
    approved_components = ['component1', 'component2']
    return all(component in approved_components for component in component_list)

# LLM06: Sensitive Information Disclosure - Data protection function
def protect_sensitive_information(data: Dict[str, Any]) -> Dict[str, Any]:
    """Apply data protection measures such as encryption."""
    # Placeholder for encryption or other data protection mechanisms
    return data  # Implement encryption or access control here

# LLM07: Insecure Plugin Design - Security assessment for plugins
# This would be a manual process or using specific security scanning tools for plugins

# LLM08: Excessive Agency - Ensure ethical guidelines are followed
# This is more of a design and review process than a code implementation

# LLM09: Overreliance - Awareness and training
# Implementing awareness programs is beyond the scope of a script

# LLM10: Model Theft - IP protection mechanism
def protect_intellectual_property(model_data: Any) -> Any:
    """Apply measures to protect the intellectual property of the model."""
    # Placeholder for IP protection mechanisms
    return model_data  # Implement specific IP protection measures here

# Example usage
if __name__ == '__main__':
    # Load and validate data from CSV
    data = pd.read_csv(DATA_CSV_FILE)
    for index, row in data.iterrows():
        if not validate_prompt_input(row['prompt']):
            print(f"Invalid input found in row {index}")


### Recommended Automated Validation Tools

For automating some of the validations and checks mentioned in the script above, here are some Pytho