Embedding Inversion Simulation This script demonstrates how a vector embedding, which may seem anonymous, can be reverse-engineered to reconstruct the original sensitive data it represents. It uses a real sentence-transformer model to generate embeddings and a text generation model (GPT-2) to simulate the reconstruction attack.
Requirements Python 3.7+
pip (Python package installer)
Installation Clone or download the repository/script.
Navigate to the script's directory in your terminal.
Install the required Python libraries using the provided requirements.txt file. Run the following command:
pip install -r requirements.txt
This will install all necessary packages, including numpy, torch, sentence-transformers, transformers, and scipy.
Running the Simulation Once the installation is complete, you can run the simulation script directly from your terminal:
python3 simulation_embedding.py
The script will then execute the simulation, printing the original secret, the generated "anonymous" vector, the discovered semantic keywords, and the final reconstructed text to your console.
###########################################################
Second script demonstrate Data Poisoning the AI's knowledge base to skew its output or inject bias. Uploading malicious documents or compromising data feeds before they are vectorized.
python3 rag_poisoning.py