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# <img src="./img/logo.png" width=50px/>PIG: Privacy Jailbreak Attack on LLMs via Gradient-based Iterative In-Context Optimization
# <img src="./img/logo.png" width=50px style="padding-top: 0px"/>PIG: Privacy Jailbreak Attack on LLMs via Gradient-based Iterative In-Context Optimization
> This repository contains the official code implementation of our paper: [![arXiv: paper](https://img.shields.io/badge/arXiv-paper-red.svg)](https://arxiv.org/abs/xxx)
> This repository contains the official code implementation of our paper: [![arXiv: paper](https://img.shields.io/badge/arXiv-paper-red.svg)](https://arxiv.org/abs/2505.6437443)
![PIG](./img/PIG.png)
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## Datasets
You can download the Enron Email dataset and TrustLLM dataset [here](https://drive.google.com/drive/folders/16Th72F_QcxRAryOIk9L2t0oIps1xnHGW) and place them under the `./data` directory.
You can download the Enron Email dataset and TrustLLM dataset [here](https://drive.google.com/drive/folders/16Th72F_QcxRAryOIk9L2t0oIps1xnHGW) and place them under the `./data`.
## Usage
You can run a privacy jailbreak attack using the following steps:
1. First, modify parameters such as `dataset`, `target_model_name`, `attack_model_name`, or `eval_model_name` in script `run.sh`.
2. Then, execute the privacy jailbreak attack by running `bash run.sh`. Use the `tail` command to monitor the `log` file in real time.
1. First, modify parameters such as `dataset`, `target_model` or `attack_model` in script `run.sh`.
2. Then, execute the privacy jailbreak attack by running `bash run.sh`.
3. Next, after the attack completes, the results will be available in the corresponding `output` directory.
4. Finally, evaluate the results using `python eval.py` to compute various metrics such as the Attack Success Rate (ASR).
4. Finally, evaluate the results using `python eval.py` to compute various metrics such as the ASR.
## Acknowledgements