3. Exposed Vector Databases & Direct Augmentation Data Leaks

Context

• What should the challenge scenario be like?

The Concept: Augmentation data used for Retrieval-Augmented Generation (RAG) is frequently indexed into vector databases
. The Exchange flags Direct Augmentation Data Leaks, noting that vector stores are often deployed without proper authentication or conventional access controls
. Even if the raw text is not readable, high-dimensional vectors (embeddings) can sometimes be decoded or mathematically reversed to reconstruct the original text
.

Exercise Design: Provide the player with a scenario where a RAG assistant holds development configs or secrets in a shared vector database (e.g., Pinecone or Qdrant). The database endpoint has an exposed port or lacks authentication. The player must either connect to the database to dump the indexed chunks or query the embeddings to reconstruct the secret data

• What should the participant learn from completing the challenge?
  PUT AUTH ON YOUR RAG ASSISTANT AND VECTOR DATABASES!
• For what category would the challenge be? (e.g. Docker, K8s, binary)
  AI

Did you encounter this in real life? Could you tell us more about the scenario?

Yes :( ...

No file, test, or entry point is named in the issue. Begin by finding how existing AI challenges are represented and tested, then define the vector-database exposure scenario around Pinecone or Qdrant; done means a participant can access the exposed augmentation data and the challenge teaches the stated authentication lesson.