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SmoothLLM

This is the official source code for "SmoothLLM: Defending LLMs Against Jailbreaking Attacks" by Alex Robey, Eric Wong, Hamed Hassani, and George J. Pappas. To learn more about our work, see our blog post.

Installation

Step 1: Create an empty virtual environment.

conda create -n smooth-llm python=3.10
conda activate smooth-llm

Step 2: Install the source code for "Universal and Transferable Adversarial Attacks on Aligned Language Models."

git clone https://github.com/llm-attacks/llm-attacks.git
cd llm-attacks
pip install -e .

Step 3: Download the weights for Vicuna and/or Llama2 from HuggingFace.

Step 4: Change the paths to the model and tokenizer in lib/model_configs.py depending on which set(s) of weights you downloaded in Step 3.

MODELS = {
    'llama2': {
        'model_path': '/shared_data0/arobey1/llama-2-7b-chat-hf',
        'tokenizer_path': '/shared_data0/arobey1/llama-2-7b-chat-hf',
        'conversation_template': 'llama-2'
    },
    'vicuna': {
        'model_path': '/shared_data0/arobey1/vicuna-13b-v1.5',
        'tokenizer_path': '/shared_data0/arobey1/vicuna-13b-v1.5',
        'conversation_template': 'vicuna'
    }
}

The conversation_template value is used to initialize a fastchat conversation template.

Experiments

We provide ten adversarial suffix generated by running GCG for Vicuna and Llama2 in the data/ directory. You can run SmoothLLM by running:

python main.py \
    --results_dir ./results \
    --target_model vicuna \
    --attack GCG \
    --attack_logfile data/GCG/vicuna_behaviors.json \
    --smoothllm_pert_type RandomSwapPerturbation \
    --smoothllm_pert_pct 10 \
    --smoothllm_num_copies 10

You can also change SmoothLLM's hyperparameters---the number of copies, the perturbation percentage, and the perturbation function---by changing the named arguments. At present, we support three kinds of perturbations: swaps, patches, and insertions. For more details, see Algorithm 2 in our paper. To use these functions, you can replace the --perturbation_type value with RandomSwapPerturbation, RandomPatchPerturbation, or RandomInsertPerturbation.

Reproducibility

The following codebases have reimplemented our results:

• https://gist.github.com/deadbits/4ab3f807441d72a2cf3105d0aea9de48

Citation

If you find this codebase useful in your research, please consider citing:

@article{robey2023smoothllm,
  title={SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks},
  author={Robey, Alexander and Wong, Eric and Hassani, Hamed and Pappas, George J},
  journal={arXiv preprint arXiv:2310.03684},
  year={2023}
}

License

smooth-llm is licensed under the terms of the MIT license. See LICENSE for more details.