Guardrail Reinforcer

What It Does

Installation

pip install transformers gym torch

Usage

python guardrail_reinforcer.py --log bypass_attempts.json --model pretrained_model --output updated_model

Source Code

import argparse
import json
import os
import gym
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

def load_model(model_path):
    """Load the pre-trained AI model."""
    try:
        model = AutoModelForSequenceClassification.from_pretrained(model_path)
        tokenizer = AutoTokenizer.from_pretrained(model_path)
        return model, tokenizer
    except Exception as e:
        raise RuntimeError(f"Error loading model: {e}")

def load_log(log_path):
    """Load the bypass attempt log file."""
    if not os.path.exists(log_path):
        raise FileNotFoundError(f"Log file not found: {log_path}")
    try:
        with open(log_path, 'r') as f:
            return json.load(f)
    except json.JSONDecodeError:
        raise ValueError("Log file is not a valid JSON.")

def apply_reinforcement_learning(model, tokenizer, log_data):
    """Apply reinforcement learning to improve the model based on bypass attempts."""
    env = gym.make('CartPole-v1')  # Placeholder environment for RL

    for attempt in log_data.get('bypass_attempts', []):
        input_text = attempt.get('input', '')
        expected_output = attempt.get('expected_output', '')

        if not input_text or not expected_output:
            continue

        inputs = tokenizer(input_text, return_tensors="pt")
        outputs = model(**inputs)

        # Placeholder logic for RL adjustment
        reward = 1 if outputs.logits.argmax().item() == expected_output else -1
        env.step(reward)

    return model

def save_updated_model(model, output_path):
    """Save the updated model."""
    try:
        model.save_pretrained(output_path)
        print(f"Updated model saved to {output_path}")
    except Exception as e:
        raise RuntimeError(f"Error saving model: {e}")

def main():
    parser = argparse.ArgumentParser(description="Guardrail Reinforcer")
    parser.add_argument('--log', required=True, help="Path to the bypass attempt log file")
    parser.add_argument('--model', required=True, help="Path to the pre-trained AI model")
    parser.add_argument('--output', required=True, help="Path to save the updated model")

    args = parser.parse_args()

    try:
        model, tokenizer = load_model(args.model)
        log_data = load_log(args.log)
        updated_model = apply_reinforcement_learning(model, tokenizer, log_data)
        save_updated_model(updated_model, args.output)
    except Exception as e:
        print(f"Error: {e}")

if __name__ == "__main__":
    main()