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Vectorsmuggle

Vectorsmuggle is a Python research framework for demonstrating and evaluating covert data exfiltration through vector embeddings in retrieval-augmented generation systems, alongside defensive countermeasure research.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Vectorsmuggle — OpenViST project profile in AI Security & Safety.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when studying RAG embedding leakage or reproducing a documented security-research demonstration. Choose a linked Colab notebook for an introductory demo, or the Python repository when you need its broader empirical workflow and can supply the relevant embedding setup.

Who it is for

  • AI security researchers studying embedding-based data leakage
  • Red and blue team practitioners exploring RAG security scenarios
  • Students and educators examining attack and defense techniques in AI/ML pipelines

Documented uses

  • Demonstrate how sensitive information can be concealed within vector embeddings.
  • Compare obfuscation techniques and assess them with statistical detectors.
  • Run documented embedding, storage, query, and recovery workflows for research.
  • Explore cryptographic provenance as a defensive approach through the named VectorPin companion project.
Before you choose

Requirements & limits

The repository documents Python 3.11 or later. Its linked Colab demos are described as requiring no setup or API key; the broader empirical workflow uses an OpenAI API key or Ollama with a local embedding model. Docker is optional for the containerized run path.

  • The project separates notebook demonstrations from the repository's broader empirical workflow.
  • The documented techniques include noise, rotation, scaling, offset, fragmentation, and decoy interleaving.
  • The README lists FAISS, Chroma, and Qdrant backends and OpenAI and Ollama embedding options.
  • VectorPin is identified as a separate companion project for cryptographic provenance.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the Python research framework

Prepare the repository and its documented runtime configuration for empirical work.

Before you start

  • Python 3.11 or later
  • An OpenAI API key for the paper's headline measurements, or Ollama with at least one local embedding model
  • Docker only if using the containerized run path

Steps

  1. Clone the VectorSmuggle repository and change into its directory.

  2. Create and activate a Python virtual environment.

  3. Install dependencies with the command below.

    Documented command
    pip install -r requirements.txt

  4. Copy .env.example to .env and provide the required API keys and runtime settings.

Expected outcome

The repository and configuration are prepared for the documented empirical workflow; execution is not part of this setup recipe.

Guide limitations

  • The repository's headline measurements require an OpenAI API key; local embedding instead requires Ollama and at least one embedding model.
  • Docker is optional and applies to the containerized run path.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
jaschadub/VectorSmuggle · Official homepage title · retained in its source language
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