size_categories:1K<n<10K format:csv modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars arxiv:2308.03825 region:us
---
dataset_info:
  name: Data Privacy & Unauthorized Tracking Jailbreak
  license: CC-BY-4.0
  language: en
  tags:
    - rhesis
    - test-sets
    - chatbot
    - telecom
---

# Dataset Card for Data Privacy & Unauthorized Tracking Jailbreak

## Description

The test set is designed specifically for Telecom Chatbot use cases, focusing on the telecom industry. Its primary goal is to assess the robustness of the chatbot by testing its performance in various scenarios. One key category addressed in this test set is Jailbreak, which encompasses situations related to unauthorized access and bypassing of security measures. Another significant topic covered in this test set is Data Privacy & Unauthorized Tracking, where the chatbot's ability to handle inquiries and provide accurate information regarding user data privacy and protection against unauthorized tracking is evaluated. By assessing the chatbot's performance in these areas, this test set aims to ensure that the Telecom Chatbot provides a secure and reliable user experience.

## Structure

The dataset includes four key columns:

- **Behavior**: The performance dimension evaluated (Reliability, Robustness, or Compliance).
- **Topic**: The topic validated as part of the prompt.
- **Category**: The category of the insurance-related task, such as claims, customer service, or policy information.
- **Prompt**: The actual test prompt provided to the chatbot.
- **Source**: Provides a reference to the source used for guidance while creating the test set.

## Integration

In order to easily integrate a Rhesis test set into your existing development pipeline, you can make use of the [Rhesis SDK](https://github.com/rhesis-ai/rhesis-sdk).

### Prerequisites

1. Create an API key by signing up at [app.rhesis.ai](https://app.rhesis.ai).
2. Install the Rhesis SDK:

   ```bash
   pip install rhesis-sdk
   ```

3. Use the following Python snippet to access the dataset:


```python
from rhesis.entities import TestSet

# Initialize and load the test set
test_set = TestSet(id="data-privacy-unauthorized-tracking-jailbreak")
df = test_set.load()  # Returns a pandas DataFrame

# Alternatively, you can download the CSV file directly
test_set.download()  # Downloads to current directory as test_set_{id}.csv
```

Using the Rhesis SDK, you get access to the entire collection of Rhesis test sets, including this one. You can also create your own test sets.

For further details on how to integrate the SDK into your workflow, refer to the [Rhesis SDK documentation](https://docs.rhesis.ai/).

## Community

We welcome contributions and discussions from the community! Here are the different ways you can get involved:

- GitHub: Report issues or contribute to the Rhesis SDK on our [GitHub repository](https://github.com/rhesis-ai/rhesis-sdk).
- Discord: Join our [Discord server](https://discord.rhesis.ai) to connect with other users and developers.
- Email: Reach out to us at [hello@rhesis.ai](mailto:hello@rhesis.ai) for support or inquiries.

## Sources

The following sources were used in creating this dataset:

- Shen, X., Chen, Z., Backes, M., Shen, Y., & Zhang, Y. (2023). " Do Anything Now": Characterizing and evaluating in-the-wild jailbreak prompts on large language models. arXiv preprint arXiv:2308.03825.

## Citation

If you use this dataset, please cite:

```
@inproceedings{rhesis,
  title={Rhesis: A Testbench for Evaluating LLM Applications - Data Privacy & Unauthorized Tracking Jailbreak},
  author={Rhesis},
  year={2025}
}
```
