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Implement of Implicit Knowledge Extraction Attack.

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# SILENT LEAKS: Implicit Knowledge Extraction Attack on RAG Systems Through Benign Queries

[![ICLR 2026](https://img.shields.io/badge/ICLR-2026-blue)](https://iclr.cc/)
[![Paper](https://img.shields.io/badge/Paper-OpenReview-red)](https://openreview.net/pdf?id=zfVICPB5Sv)
[![Website](https://img.shields.io/badge/Website-Ikea-Green)](https://wangyuhao06.github.io/IKEA-web/)
[![GitHub](https://img.shields.io/badge/GitHub-Code-black)](https://github.com/Wangyuhao06/IKEA)

📦 **Setup Instructions**

Follow the steps below to set up and run the pipeline.

🔐 **1. Environment Configuration**

Create a `.env` file in the root directory to store your API keys. This is required for accessing OpenAI and DeepSeek models.

Add the following lines to the .env file:
```bash
OPENAI_KEY=your_openai_api_key
DEEPSEEK_KEY=your_deepseek_api_key
```
📁 **2. Dataset Preparation**

Download one or more of the following datasets from Hugging Face and place them into the datasets/ directory:
- [ChatDoctor-HealthCareMagic-100k](https://huggingface.co/datasets/lavita/)
- [HarryPotterQA](https://huggingface.co/datasets/vapit/HarryPotterQA)
- [PokemonQA](https://huggingface.co/datasets/tungdop2/pokemon)

Example folder structure:

datasets/

├── ChatDoctor-HealthCareMagic-100k.json

├── HarryPotterQA.json

├── PokemonQA.json

**Note:** Ensure each dataset is saved in a valid JSON format, i.e., a list of objects with {"input": ..., "output": ...} structure.

▶️ **3. Run the Pipeline**

Execute the main feedback mutation pipeline script:
```bash
python feedback_mutation_pipeline.py
```

📌 **Note:**
- API usage may incur costs depending on the number of queries issued
- Output logs and extracted results will be saved in the outputs/ directory

## Citation

```bibtex
@inproceedings{
  wang2026silent,
  title={Silent Leaks: Implicit Knowledge Extraction Attack on {RAG} Systems},
  author={Yuhao Wang and Wenjie Qu and Shengfang Zhai and Yanze Jiang and Liu Zichen and Yue Liu and Yinpeng Dong and Jiaheng Zhang},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
  url={https://openreview.net/forum?id=zfVICPB5Sv}
}
```

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