OWASP LLM Top 10: SAMM

```python
# Data Validation Script for LLMs based on OWASP Top 10 SAMM Mapping

import re
from typing import List

# LLM01: Prompt Injection
def validate_prompt(prompt: str) -> bool:
    """
    Validate input prompt to prevent injection attacks.
    """
    # Simple validation to check for malicious patterns; expand as needed
    if re.search(r"[^\w\s]", prompt):
        return False
    return True

# LLM02: Insecure Output Handling
def encode_output(output: str) -> str:
    """
    Encode output to ensure safe handling.
    """
    # Example: HTML encode to prevent XSS; adjust encoding as needed for your use case
    return output.replace("<", "&lt;").replace(">", "&gt;")

# LLM03: Training Data Poisoning
# Automated tool recommendation: Use data validation tools or custom scripts to verify data source integrity

# LLM04: Model Denial of Service (DoS)
# Automated tool recommendation: Implement monitoring and alerting using tools like Prometheus or Datadog

# LLM05: Supply-Chain Vulnerabilities
# Automated tool recommendation: Use dependency check tools like OWASP Dependency-Check

# LLM06: Sensitive Information Disclosure
def classify_and_protect_data(data: str) -> str:
    """
    Classify data and apply protection mechanisms like encryption.
    """
    # Placeholder for data classification and encryption logic
    return data  # This should be encrypted based on classification

# LLM07: Insecure Plugin Design
# Automated tool recommendation: Static code analysis tools like SonarQube for secure plugin development

# LLM08: Excessive Agency
# Note: This is more about design and governance, less about direct code validation

# LLM09: Overreliance
# Note: Addressed through education and proper use guidance, not directly through validation scripts

# LLM10: Model Theft
# Automated tool recommendation: Implement robust access controls and use tools for auditing like AWS CloudTrail

# Example usage
prompt = "Select * from users where user='admin';"
print("Prompt validation:", validate_prompt(prompt))
print("Encoded output:", encode_output("<script>alert('XSS');</script>"))


### Automated Validation Tools Recommendation

For each vulnerability area, certain tools can enhance the automation and effectiveness of your validation processes:

- **LLM01: Prompt Injection**: Input validation libraries or frameworks specific to your programming environment.
- **LLM02: Insecure Output Handling**: Libraries that automatically encode or sanitize output, such as OWASP's Java Encoder for Java applications.
- **LLM03: Training Data Poisoning**: Data validation tools, and machine learning datasets integrity verification tools.
- **LLM04: Model Denial of Service (DoS)**: Monitoring and alerting tools like Prometheus, Datadog, or CloudWatch.
- **LLM05: Supply-Chain Vulnerabilities**: OWASP Dependency-Check, Snyk, or Dependabot for identifying vulnerable dependencies.
- **LLM06: Sensitive Information Disclosure**: Data classification and encryption tools, such as AWS KMS or Azure Key Vault.
- **LLM07: Insecure Plugin Design**: Static code analysis tools like SonarQube, Coverity, or CodeQL to identify security flaws.
- **LLM08: Excessive Agency**: Not directly applicable for automated tools but requires design reviews and ethical guidelines.
- **LLM09: Overreliance**: Not directly applicable for automated tools; addressed through user education and system design.
- **LLM10: Model Theft**: Access control mechanisms and auditing tools like AWS CloudTrail or Google Cloud Audit Logs.

This script and the tools listed provide a starting point. Tailoring to your specific LLM's operational and security requirements is essential for effective vulnerability management.