CITATION.cff

cff-version: 1.2.0
message: "If you use VectorPin in your work, please cite both the software and the companion preprint."
type: software
title: "VectorPin: Verifiable integrity for AI embedding stores"
authors:
  - family-names: Wanger
    given-names: Jascha
    affiliation: "ThirdKey / Tarnover, LLC"
    email: jascha@thirdkey.ai
abstract: >-
  VectorPin is a cryptographic provenance protocol for embeddings stored in
  vector databases. Each embedding is bound to its source content and producing
  model via an Ed25519 signature over a canonical byte representation, and any
  post-embedding modification breaks signature verification on read. Reference
  implementations in Python, Rust, and TypeScript are byte-for-byte compatible,
  locked together by shared test vectors. Part of the ThirdKey Trust Stack.
version: "0.1.1"
date-released: 2026-05-07
keywords:
  - vector database
  - embedding store
  - retrieval-augmented generation
  - cryptographic provenance
  - Ed25519
  - integrity
  - tamper evidence
  - AI security
license: Apache-2.0
repository-code: "https://github.com/ThirdKeyAI/VectorPin"
url: "https://thirdkey.ai"
references:
  - type: article
    title: "VectorSmuggle: Steganographic Exfiltration in Embedding Stores and a Cryptographic Provenance Defense"
    authors:
      - family-names: Wanger
        given-names: Jascha
    year: 2026
    doi: "10.5281/zenodo.20058256"
    url: "https://doi.org/10.5281/zenodo.20058256"
    notes: "Companion preprint documenting the threat model and the empirical evaluation that motivates VectorPin."