NIST.md

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.