Installation

Usage

Source Code

import argparse
import numpy as np
import torch
from scipy import stats


def simulate_adversarial_attack(model, input_data, epsilon=0.1):
    # Simulate adversarial attack
    perturbed_input = input_data + epsilon * np.sign(np.random.randn(*input_data.shape))
    return perturbed_input


def simulate_data_poisoning_attack(model, input_data, poison_ratio=0.1):
    # Simulate data poisoning attack
    num_poisoned_samples = int(poison_ratio * len(input_data))
    poisoned_indices = np.random.choice(len(input_data), num_poisoned_samples, replace=False)
    poisoned_input = input_data.copy()
    poisoned_input[poisoned_indices] += np.random.randn(*poisoned_input[poisoned_indices].shape)
    return poisoned_input


def evaluate_model_robustness(model, input_data, attack_type, attack_params):
    if attack_type == 'adversarial':
        perturbed_input = simulate_adversarial_attack(model, input_data, **attack_params)
    elif attack_type == 'data_poisoning':
        perturbed_input = simulate_data_poisoning_attack(model, input_data, **attack_params)
    else:
        raise ValueError('Unsupported attack type')
    # Evaluate model robustness
    model_output = model(torch.tensor(perturbed_input, dtype=torch.float32))
    return model_output.detach().numpy()

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='LLM Attack Simulator')
    parser.add_argument('--model', type=str, help='Path to LLM model file')
    parser.add_argument('--attack', type=str, help='Attack type (adversarial or data_poisoning)')
    parser.add_argument('--epsilon', type=float, default=0.1, help='Epsilon value for adversarial attack')
    parser.add_argument('--poison_ratio', type=float, default=0.1, help='Poison ratio for data poisoning attack')
    args = parser.parse_args()
    # Load model and input data
    model = torch.load(args.model)
    input_data = np.random.randn(10, 10)
    # Simulate attack and evaluate model robustness
    attack_params = {'epsilon': args.epsilon} if args.attack == 'adversarial' else {'poison_ratio': args.poison_ratio}
    model_output = evaluate_model_robustness(model, input_data, args.attack, attack_params)
    print(model_output)

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