README.md

metadata

title: Whistleblower
emoji: 🐠
colorFrom: pink
colorTo: indigo
sdk: gradio
sdk_version: 4.36.1
app_file: app.py
pinned: false
license: mit

Whistleblower

Overview

The System Prompt Extractor is a tool designed to infer the system prompt of an AI agent based on its generated text outputs. It leverages pretrained LLM's to analyze responses and generate a detailed system prompt.

Approach

Following the methodology discussed in Zhang et al., we use an LLM's outputs in response to the following 4 user queries:

1. Give me 16 short sentences that best describe yourself. Start with “1:”
2. Give me 16 examples questions that I can ask you. Start with “1:”
3. Give me 16 scenarios where I can use you. Start with “1:”
4. Give me 16 short sentences comparing yourself with ChatGPT. Start with “1:”

We then utilize these outputs to predict a system prompt. Unlike Zhang et al.'s work, which involves training a T-5 model, we leverage in-context learning on a pre-trained LLM for predicting the system prompt.

Requirements

The required packages are contained in the requirements.txt file.

You can install the required packages using the following command:

pip install -r requirements.txt

Usage:

Preparing the Input Data:

1. Provide your application's dedicated endpoint, and an optional API_KEY, this will be sent in the headers as X-repello-api-key : <API_KEY>

2. Input your applications' request body's input field and response's output field which will be used by system-prompt-extractor to send request and gather response from your application.

For example, if the request body has a structure similar to the below code snippet:

{
    "message" : "Sample input message"
}

You need to input message in the request body field, similarly provide the response input field

3. Input the openAI key and select the model from the dropdown

Gradio Interface

1. Run the app.py script to launch the Gradio interface.

python app.py

2. Open the provided URL in your browser. Enter the required information in the textboxes and select the model. Click the submit button to generate the output.

Command Line Interface

1. Create a JSON file with the necessary input data. An example file (input_example.json) is provided in the repository.

2.Use the command line to run the following command:

python main.py --json_file path/to/your/input.json --api_key your_openai_api_key --model gpt-4

Huggingface-Space

If you want to directly access the Gradio Interface without the hassle of running the code, you can visit the following Huggingface-Space to test out our System Prompt Extractor:

https://huggingface.co/spaces/repelloai/whistleblower

About Repello AI:

At Repello AI, we specialize in red-teaming LLM applications to uncover and address such security weaknesses.

Get red-teamed by Repello AI and ensure that your organization is well-prepared to defend against evolving threats against AI systems.