OWASP LLM Top 10: NIST

import re
from pydantic import BaseModel, ValidationError, validator
from cryptography.fernet import Fernet
import logging
import numpy as np
from ratelimit import limits, sleep_and_retry
import hashlib
import os

# Basic setup for logging to monitor behaviors and potential attacks
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# LLM01: Prompt Injection - Enhanced input validation with Pydantic
class SafePrompt(BaseModel):
    prompt: str

    @validator('prompt')
    def check_for_injection(cls, v):
        # Example of blocking suspicious patterns
        if re.search(r"[{}]", v):  # Basic check for code injection patterns
            raise ValueError("Potential code injection detected.")
        return v

# LLM02: Insecure Output Handling - Enhanced output encoding
def secure_output_html(output):
    # This function would be used to escape HTML entities to prevent XSS attacks
    return re.sub(r'([<>"\'&])', lambda x: f"&#{ord(x.group(0))};", output)

# LLM03: Training Data Poisoning - Anomaly Detection
def detect_anomalies_in_data(data):
    # Placeholder for a more complex anomaly detection mechanism, possibly using statistical models or ML
    logging.info("Anomaly detection placeholder - implement specific logic here.")
    return True  # Assuming anomaly detection passed for demonstration

# LLM04: Model Denial of Service (DoS) - Implementing Rate Limiting
@sleep_and_retry
@limits(calls=100, period=60)  # Example: Limit to 100 calls per minute
def process_input(input_data):
    # Process input data with rate limiting to prevent DoS
    logging.info(f"Processing input: {input_data}")
    return True

# LLM05: Supply-Chain Vulnerabilities - Basic Integrity Checks
def verify_third_party_integrity():
    # Placeholder for supply-chain validation logic
    # Example: Check hashes of third-party libraries against known secure hashes
    logging.info("Supply-chain integrity check placeholder - implement specific validation here.")

# LLM06: Sensitive Information Disclosure - Implement Encryption for Data at Rest
def encrypt_data(data, key):
    f = Fernet(key)
    encrypted_data = f.encrypt(data.encode())
    return encrypted_data

def decrypt_data(encrypted_data, key):
    f = Fernet(key)
    decrypted_data = f.decrypt(encrypted_data).decode()
    return decrypted_data

# Generate a new encryption key (Do this once and store securely)
# key = Fernet.generate_key()

# LLM07: Insecure Plugin Design - Plugin Security Review
def plugin_security_review(plugin):
    # Placeholder for a security review process for plugins
    # This could involve static analysis, dependency checking, etc.
    logging.info("Plugin security review placeholder - implement specific checks here.")

# LLM08: Excessive Agency - Monitoring and Limiting Actions
def limit_agency(actions):
    # Placeholder for limiting the agency of LLMs based on predefined rules
    logging.info("Limit agency placeholder - implement action limitation logic here.")

# LLM09: Overreliance - Checks and Balances
def check_overreliance():
    # Implement logic to detect and warn about overreliance on automated systems
    logging.warning("Check for overreliance on LLMs.")

# LLM10: Model Theft - Model Access Controls
def secure_model_access():
    # Placeholder for securing access to models, potentially using access control lists or encryption
    logging.info("Model access control placeholder - implement specific security measures here.")

if __name__ == "__main__":
    # Example usage of some functions
    try:
        safe_prompt = SafePrompt(prompt="Hello, world! {malicious_code}")
    except ValidationError as e:
        logging.warning(f"Validation error: {e}")

    encrypted_message = encrypt_data("Secret Message", Fernet.generate_key())
    logging.info(f"Encrypted Message: {encrypted_message}")

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.