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Sprint projectJun 21, 2026Florianopolis, Brazil

Vectox

Diana Chang, Ian Duhamel Hayes · Team OnçAI

Submitted to Global South AI Safety Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

vectox — Sleeper-Agent RAG Memory Poisoning (Latin America · Technical Safety)

RAG systems trust their vector store as memory, which makes that store a write-accessible attack surface. We built vectox, a reproducible sandbox showing that a handful of dormant, benign-looking documents seeded into a vector store act as an inference-time sleeper agent: invisible until a specific trigger query retrieves them, at which point the model treats the retrieved text as an instruction and its output is hijacked — no model weights touched. We pair a new NVIDIA garak rag_poisoning probe and detector with a live PostgreSQL pgvector(384)+HNSW victim, seeded with Latin-American-context poison derived structurally from the SESGO Spanish stereotype benchmark plus a Spanish-English code-switching variant. Across six bias dimensions and two languages, 24 poison documents among 224 achieve 100% attack success; adding an insertion-time BEFORE INSERT quarantine gate drops it to 0% with no change to the attack — demonstrating that auditing memory writes before they reach the retrieval hot path neutralizes the threat for Spanish/Portuguese systems.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. Really liked this paper because it has a very clear and practical security takeaway: RAG memory is not just storage, it is part of the trusted system. The sleeper-agent framing is effective, and the 100% to 0% result makes the risk and mitigation easy to understand.

    The strongest insight is that memory should be checked before it enters the retrieval path, not only after the model responds. I would like to see this tested on a larger, more realistic corpus with adaptive attackers, but overall this is a useful and well-scoped applied security contribution.

  2. The project has has chosen a great topic to be worked on, i.e; preventing poisoned documents in RAG from bypassing LLM or creating disruptions in the agent workflow. It proposes a solution to set all newly inserted documents to be in pending state. However, the paper doesn't discuss how the newly inserted documents would go to the approval state, and if manual intervention is necessary, then the whole implementation becomes redundant.

  3. I think the problem of poisoned retrieval sets is relevant. However, I think that the proposed solution lacks novelty. I would have liked more clarity on what attacks and documents look like, but the eval set seems to suspiciously favor the intervention, as performance goes from 0% to 100%.

Cite this project

@misc{chang2026vectox,
  title = {{Vectox}},
  author = {Diana Chang and Ian Duhamel Hayes},
  year = {2026},
  month = jun,
  note = {Submitted to Global South AI Safety Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/vectox-0h8z}},
  url = {https://apartresearch.com/sprints/projects/vectox-0h8z}
}

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026