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OSU-NLP-Group / AmpleGCG

AmpleGCG: Learning a Universal and Transferable Generator of Adversarial Attacks on Both Open and Closed LLM
https://github.com/OSU-NLP-Group/AmpleGCG?tab=readme-ov-file
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adversarial-attacks gcg nlp safety

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AmpleGCG: Learning a Universal and Transferable Generator of Adversarial Attacks on Both Open and Closed LLM

This is the official repo of AmpleGCG (https://arxiv.org/abs/2404.07921) and AmpleGCG-plus (https://arxiv.org/abs/2410.22143). Please kindly 🌟star🌟 this repo and cite our papers 📜 if you find them useful!

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🚨Updates🚨

Reproducibility and Codes

This repository hosts the source code of Augmented GCG, which extends the capabilities of GCG by overgenerating samples alongside the GCG optimizations. Our work builds upon the foundational GCG work, and we express our deep appreciation for their open-source release.

Due to safety and ethical considerations, we have decided not to publicly release the trained AmpleGCG, our adversarial suffix generator in the wild. There exists a significant risk that, if used maliciously, AmpleGCG could rapidly compromise the safety of both open-source and proprietary models. Such a scenario could lead to widespread dissemination of harmful content, a risk we aim to mitigate by restricting access to the trained model.

However, one can apply for our trained AmpleGCG via 🤗 AmpleGCG-series models and generated adversarial suffixes via this Google Form for research purposes only. Once approval, we will release the suffixes generated by AmpleGCG on AdvBench and MaliciousIntruct. Access to the model and data is granted on a provisional basis and is subject to the sole discretion of the authors.

Licensing Information

The code under this repo is licensed under an OPEN RAIL-S License.

The data under this repo is licensed under an OPEN RAIL-D License.

The model weight and parameters under this repo are licensed under an OPEN RAIL-M License.

Introduction

TL;DR We further amplify the effectiveness of GCG, achieving increased ASR, more comprehensive identification of vulnerabilities, and improved efficiency across both open-source and closed-source models.

As large language models (LLMs) become increasingly prevalent and integrated into autonomous systems, ensuring their safety is imperative. Despite significant strides toward safety alignment, recent work GCG (Zou et al., 2023) successfully produces a single suffix for each query to jailbreak LLMs. In this work, we first identify the overlooked opportunities by solely picking the suffix with the lowest loss during GCG optimization, and consequently, uncover many other missed successful suffixes in the middle steps. Moreover, we utilize them as training data to learn a generator named AmpleGCG, which captures the distribution of adversarial suffixes given a harmful query. This generator facilitates the rapid generation of hundreds of suffixes for any harmful query in minutes. AmpleGCG achieves near 100% attack success rate (ASR) on two aligned LLMs (Llama-2-7B-Chat and Vicuna-7B), surpassing two strongest existing attack baselines. Interestingly, AmpleGCG also transfers effectively to attack different models, including closed-source LLMs, achieving a 99% ASR on the latest GPT-3.5. To summarize, our work amplifies the impact of GCG by training a generator of adversarial suffixes that is universal to any harmful query and is transferable from attacking open-source LLMs to closed-source LLMs. It can generate many adversarial suffixes for one harmful query within minutes (e.g., 200 suffixes in 6 mins with an ASR of 99% when attacking Llama-2-7B-Chat), rendering it more challenging to defend

Setup

conda create --name AmpleGCG python=3.11.4

conda activate AmpleGCG

pip install -r requirements.txt

Experiments

Augmented GCG

Augmented GCG simply extends GCG by overgenerating the suffix candidates during the optimizations. To obtain the suffixes with augmented GCG under either individual query or multi queries settings, please first:

cd llmattack/experiments/launch_scripts

We provide the scripts for four settings of augmented GCG.

  1. Individual Query

    1.1 Individual Model

    bash run_overgenerate_indiv_query_indiv_model_llama2-chat.sh

    1.2 Multiple Models

    bash run_overgenerate_indiv_query_multi_models_llama2-chat_vicuna.sh

  2. Multiple Queries

    2.1 Individual Model

    bash run_overgenerate_mutli_queries_indiv_model.sh

    2.2 Multiple Models

    bash run_overgenerate_mutli_queries_multi_models_vicuna7_13b_guanaco_7_13b.sh

    [!NOTE] Notice that for multiple queries settings, we only save the suffixes with the lowest loss at each step, which is different from the individual query setting of saving all available sampled candidates at each step.

For individual query and multiple queries settings, we save the potential suffixes with the key step_cands and controls respectively. Specifically, the suffixes within controls are the instances optimized over all training queries. For the suffixes under individual setting, we save them as the format

query:
    ...,

    step_N-1:[
        control: <suffix>,
        loss: <loss>
    ],

    step_N:[
        control: <suffix>,
        loss: <loss>
    ],

    ...

For more details on optimizing over different models and setups, please refer to the GCG repo

Evaluation

We provide a modularized and flexible pipeline to evaluate the different victim models.

Take the multiple queries settings for an example.

If you have gotten the results from the augmented GCG above, you need to first deduplicate the generated suffixes and place them under the myconfig/prompt_own_list.json with the key (e.g. llama2_lowest or llama2_lowest_at_each_step corresponding to default GCG (only the suffixes with lowest loss) and Overgenerate + X under multiple queries setting in the paper tables accordingly). Subsequently, you should replace the variable augmented_GCG in evaluate_augmentedGCG.sh with your defined keys and run

cd <project_workspace>
bash evaluate_augmentedGCG.sh

You can easily swap to other victim models and the generation configs of victim models under myconfig/target_lm by utilizing hydra.

After obtaining the content from victim models, you could detect the harmfulness of them by running:

bash add_reward.sh sequence

which would utilize Beavor-Cost to label the instances first and sequentially leverage HarmBench Classifier to only evaluate the instances that are deemed harmful by Beaver-Cost.

You could use a more advanced GPT4 evaluator by

bash add_reward.sh gpt4

AmpleGCG

Due to considered ethical issues, we don't publicly release the models in the wild. However, researchers could access to three different versions of AmpleGCG via 🤗 AmpleGCG-series models or train your own AmpleGCG-like adversarial suffixes generator based on the data collected from individual query settings. For more details of training, please refer to the paper about the overgenerate-then-filter pipeline for collecting training data of either individual model or multiple models and the figure below.

figure below

You could evaluate your trained generator in evaluate_augmentedGCG.sh as well once you obtain your own generator. You could further explore different settings of generation config for your generator in myconfig/generation_configs as we exemplified that different decoding approaches would affect the diversity and quality of the suffixes

Citation

@article{liao2024amplegcg,
  title={AmpleGCG: Learning a Universal and Transferable Generative Model of Adversarial Suffixes for Jailbreaking Both Open and Closed LLMs},
  author={Liao, Zeyi and Sun, Huan},
  journal={arXiv preprint arXiv:2404.07921},
  year={2024}
}

@article{kumar2024amplegcg,
  title={AmpleGCG-Plus: A Strong Generative Model of Adversarial Suffixes to Jailbreak LLMs with Higher Success Rates in Fewer Attempts},
  author={Kumar, Vishal and Liao, Zeyi and Jones, Jaylen and Sun, Huan},
  journal={arXiv preprint arXiv:2410.22143},
  year={2024}
}