AMLD SQL Injection Demo

Introduction

Welcome to the AMLD SQL Injection Demo by Effixis for AMLD EPFL 2024! This project showcases the risks of SQL injections in web applications, particularly when using Large Language Models (LLMs). The repository includes two demonstrations: Basic SQL Injections and LLM Safeguard.

Features

Basic SQL Injections (Basic_SQL_Injections.py): Demonstrates the risks of direct SQL query generation by LLMs, leading to potential SQL injections.
LLM Safeguard (pages/LLM_safeguard.py): Illustrates an advanced setup where an LLM Safeguard is employed to detect and filter out malicious SQL queries.
Chinook Database Integration: Uses the Chinook sample database, representing a digital media store.
Interactive Web Interface: Built with Streamlit, offering a user-friendly interface for interacting with both demonstrations.
Database Reset Functionality: Allows users to reset the database to its original state for repeated tests.

Installation

Clone the repository:

git clone https://github.com/effixis/shared-amld-sql-injection-demo.git

Navigate to the cloned directory:
```
cd shared-amld-sql-injection-demo
```
Install the required packages:

Activate your preferred Python environment and install the required packages using the provided requirements.txt file. For example, using Conda:
```
conda create -n amld-sql-injection-demo
conda activate amld-sql-injection-demo
pip install -r requirements.txt
```
Create a .env file in the root directory and set the OpenAI API key:
```
echo "OPENAI_API_KEY=enter_your_api_key_here" > .env
```
You can find your API key on the OpenAI dashboard.

Usage

Run the Streamlit application:

streamlit run Introduction.py

Follow the instructions on the web interface to interact with the application.

Disclaimer

This demo is for educational purposes to showcase the risk of SQL injections using LLMs. It should not be used for malicious purposes. Users are responsible for any misuse of the tools and information provided.