OWASP LLM Top 10: STRIDE

```python
# Import necessary libraries
import re
from typing import Any, Dict

def validate_prompt(prompt: str) -> bool:
    """Validate LLM01: Prompt Injection."""
    # Example: Simple check to avoid command-like inputs; customize as needed
    if re.search(r"sudo|rm -rf|:", prompt):
        return False
    return True

def validate_output(output: str) -> bool:
    """Validate LLM02: Insecure Output Handling."""
    # Example: Ensure no sensitive info leaks; customize based on expected output
    sensitive_keywords = ["password", "ssn"]
    return not any(keyword in output for keyword in sensitive_keywords)

def validate_training_data(data: Any) -> bool:
    """Validate LLM03: Training Data Poisoning."""
    # Example validation: Check for anomalies or unexpected patterns
    # This is highly model and data-specific
    return True  # Placeholder for actual validation logic

def check_for_dos(input_data: Any) -> bool:
    """Validate LLM04: Model Denial of Service."""
    # Example: Check for excessively large inputs or complex queries
    if len(str(input_data)) > 10000:  # Arbitrary limit
        return False
    return True

def validate_supply_chain(component: Dict[str, Any]) -> bool:
    """Validate LLM05: Supply-Chain Vulnerabilities."""
    # Example: Check component's integrity, e.g., via checksums or trusted sources
    return True  # Placeholder for actual validation logic

def check_sensitive_info(output: str) -> bool:
    """Validate LLM06: Sensitive Information Disclosure."""
    # Reuse LLM02's method as a starting point; refine as needed
    return validate_output(output)

def validate_plugin(plugin_config: Dict[str, Any]) -> bool:
    """Validate LLM07: Insecure Plugin Design."""
    # Example: Ensure only allowed plugins can be loaded
    allowed_plugins = ["safe_plugin", "trusted_analysis"]
    return plugin_config.get("name") in allowed_plugins

def check_excessive_agency(action: Dict[str, Any]) -> bool:
    """Validate LLM08: Excessive Agency."""
    # Example: Verify actions are within predefined limits or scopes
    return action.get("scope") in ["read", "write limited"]

def validate_overreliance(use_case: str) -> bool:
    """Validate LLM09: Overreliance."""
    # This validation is more about process and review than scriptable checks
    return True

def check_model_theft(model_details: Dict[str, Any]) -> bool:
    """Validate LLM10: Model Theft."""
    # Example: Check if the model access is from unauthorized sources
    return model_details.get("access") == "authorized"


### Recommended Automated Validation Tools

For each STRIDE category, there are tools and libraries that can help automate the validation process:

- **SAST (Static Application Security Testing)**: Tools like Bandit for Python can analyze code to find common security issues.
- **DAST (Dynamic Application Security Testing)**: ZAP can dynamically analyze running applications for vulnerabilities.
- **IAST (Interactive Application Security Testing)**: Tools like Contrast Security integrate with applications to detect vulnerabilities during runtime.
- **RASP (Runtime Application Self-Protection)**: RASP tools can protect applications from vulnerabilities in real-time, useful for mitigating risks like LLM04 and LLM07.
- **Input Validation Libraries**: Libraries like Cerberus or Marshmallow for Python can validate input data against a predefined schema, helpful for preventing issues like LLM01, LLM02, and LLM06.

This script and the tools mentioned are starting points. Actual implementations should be tailored to specific use cases, data types, and security requirements.