FGSM Text Attack Implementation

This project implements the Fast Gradient Sign Method (FGSM) attack for text models using PyTorch and Hugging Face Transformers. The implementation demonstrates how to generate adversarial examples that can perturb language model outputs.

Overview

The FGSM attack works by:

Computing the gradient of the loss with respect to the input
Taking the sign of this gradient
Adding a small perturbation in the direction of the sign to create an adversarial example

Requirements

Python 3.8+
PyTorch
Transformers
Datasets

Install the required packages using:

pip install transformers torch datasets

Project Structure

.
├── fgsm_attack.ipynb    # Jupyter notebook implementation
└── README.md           # This file

Usage

The implementation is provided as a Jupyter notebook with the following main components:

load_model(): Loads the pre-trained model and tokenizer
generate_loss(): Computes the loss for given input text
apply_fgsm_attack(): Implements the FGSM attack
compare_outputs(): Compares original and perturbed outputs

Example Usage

model, tokenizer = load_model()
original_text = "The weather today is great."
epsilon = 0.1  # Attack strength parameter

loss, inputs = generate_loss(model, tokenizer, original_text)
perturbed_input = apply_fgsm_attack(inputs, loss, epsilon)
compare_outputs(tokenizer, original_text, perturbed_input)

Parameters

model_name: Name of the pre-trained model (default: "gpt2")
epsilon: Attack strength parameter (default: 0.1)
text: Input text to be perturbed

Notes

The implementation uses GPT-2 by default but can be adapted for other language models
The epsilon parameter controls the strength of the perturbation
Larger epsilon values result in more noticeable changes but may produce less coherent text

Limitations

The attack operates on token IDs, which may not always result in semantically meaningful perturbations
The effectiveness of the attack may vary depending on the model and input text
Token ID clamping is used to ensure valid outputs, which may limit attack effectiveness

References

StacklokLabs/FGSM_LLM_Attack

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