ASVS.md

# Python Data Validation Script for LLM Vulnerabilities (LLM01 - LLM10)

import re
from cryptography.fernet import Fernet
import ratelimit
import requests

# Example key, generate your own using Fernet.generate_key()
key = b'your_encryption_key_here'
cipher_suite = Fernet(key)

# LLM01 & LLM02: Input Validation and Secure Output Handling
def validate_and_encode_input(input_data):
    """Validate and sanitize input data, then return encoded for secure handling."""
    # Simple validation example, adapt regex to your needs
    if re.match("^[a-zA-Z0-9 ]*$", input_data):
        # Securely encode output to prevent injection attacks
        encoded_data = cipher_suite.encrypt(input_data.encode('utf-8'))
        return encoded_data
    else:
        raise ValueError("Invalid input")

# LLM03: Secure Training Data
def encrypt_training_data(training_data):
    """Encrypt training data to ensure integrity."""
    encrypted_data = cipher_suite.encrypt(training_data.encode('utf-8'))
    return encrypted_data

# LLM04: Implement Rate Limiting to prevent DoS
@ratelimit.limits(calls=100, period=ratelimit.HOUR)
def process_request(request_data):
    """Process incoming request with rate limiting to prevent DoS attacks."""
    # Simulate request processing
    return "Request processed"

# LLM05, LLM06, LLM07, LLM08, LLM09, LLM10 are more conceptual and require organizational
# and architectural measures, including secure plugin design, API security,
# data protection methods, and more, which are beyond the scope of a simple script.
# These require comprehensive approaches involving multiple systems and practices.


### Automated Validation Tools

For automating validation and ensuring adherence to ASVS standards, consider integrating the following tools into your development and deployment pipelines:

- **OWASP ZAP (Zed Attack Proxy)**: For finding vulnerabilities in web applications.
- **SonarQube**: For continuous inspection of code quality to detect bugs, vulnerabilities, and code smells in your code.
- **OWASP Dependency-Check**: For detecting publicly disclosed vulnerabilities in project dependencies.
- **Bandit**: For finding common security issues in Python code.
- **cryptography**: Python library for encryption and decryption to secure data, as shown in the script.
- **Ratelimit**: Python library to implement rate limiting, as demonstrated in the script.

These tools can automate aspects of security validation and complement the script's functionalities, focusing on specific LLM vulnerabilities and general application security concerns outlined in the ASVS.