Awesome
<p align=center> :fire: Protecting Your LLMs with Information Bottleneck</p>
[Arxiv Paper] [Slides] [δΈζη] [Website Page]
TL;DR: We propose IBProtector, the first LLM jailbreak defending method based on the Information Bottleneck principle. Our protector efficiently defends against adversarial prompts without losing key information.
π If you want to read the main code of IBProtector, check out the class VIBLLM in the file
./lib/defenses.py.
Dependent version
pip install -U datasets==2.14.5 torch==2.1.1 torchmetrics==1.2.0 bitsandbytes==0.43.0 openai==0.28.0 fschat==0.2.20
pip install -U transformers==4.40.1
please don't change the order, otherwise it will have comfict sicne this fschat verson is too old
Getting Started
model configuration
First set up the path and parameters of your LLMs configuration in the file lib/model_configs.py
data preparing
Datasets acquisition instuction in the file ./data/README.md, overall, you should get your jailbreaking data by GCG or PAIR in advance.
how to tune models
To finetune the IBProtector of Vicuna-13b, you can execute the following command
python test_finetuning.py
To finetune the IBProtector of Llama2-7b, you can execute the following command
python test_finetuning_llama.py
The baselines sft and unlearning are set in files test_sft.py and test_unlearning.py, respectively.
how to inference
You can execute the following command
python main.py --results_dir ./our_results --target_model vicuna --attack TriviaQA --method vib --cuda 0
The denfense method can be chosen: none, smooth, selfdefense, sft, unlearning, ra, semantic, and vib, Note that the vib, sft, and unlearning need to fine-tune the LLMs in advance, via the corresponding commands.
The attack method can be chosen: GCG and PAIR for the main experiment, EasyJailbreak for the transferability, and TriviaQA for testing benign answering rates.
You can also run the main results through the script:
bash script.sh
Evaluating the results
Please find the examples in ./eval/eval_asr.py, ./eval/eval_harm.py, ./eval/eval_gpt.py, ./eval/eval_friedman.py, and ./eval/eval_time.py to evaluate the results. The main you need is to change your result path by modifying file_path
For instance, the evaluating command is:
cd eval/
python eval_asr.py --file_path `YOUR_RESULTS_PATH`
Further Reading
For more information about theories and limitations of existing perturbation methods, please see THIS SLIDE.
The following are related works:
1, Explaining Time Series via Contrastive and Locally Sparse Perturbations, in ICLR 2024. [GitHub Repo]
2, TimeX++: Learning Time-Series Explanations with Information Bottleneck, in ICML 2024. [GitHub Repo]
Citing IBProtector
π If you find this resource helpful, please consider starting this repository and cite our research:
@inproceedings{liu2024protecting,
title={Protecting Your LLMs with Information Bottleneck},
author={Zichuan Liu and Zefan Wang and Linjie Xu and Jinyu Wang and Lei Song and Tianchun Wang and Chunlin Chen and Wei Cheng and Jiang Bian},
year={2024},
booktitle={Neural Information Processing Systems}
}
In case of any questions, bugs, suggestions, or improvements, please feel free to drop me at zichuanliu@smail.nju.edu.cn or open an issue.