Discussion
Amine Raji, PhD
sidrag22: > Low barrier to entry. This attack requires write access to the knowledge base,this is the entire premise that bothers me here. it requires a bad actor with critical access, it also requires that the final rag output doesn't provide a reference to the referenced result. Seems just like a flawed product at that point.
sandermvanvliet: If you think about this in the context of systems that ingest content from third party systems then this attack becomes more feasible.But then, if you’re inside the network you’ve already overcome many of the boundaries
robutsume: The "requires write access" framing undersells the risk. Most production RAG pipelines don't ingest from a single curated database — they crawl Confluence, shared drives, Slack exports, support tickets. In a typical enterprise, hundreds of people have write access to those sources without anyone thinking of it as "write access to the knowledge base."The PoisonedRAG paper showing 90% success at millions-of-documents scale is the scary part. The vocabulary engineering approach here is basically the embedding equivalent of SEO — you're just optimizing for cosine similarity instead of PageRank. And unlike SEO, there's no ecosystem of detection tools yet.I'd love to see someone test whether document-level provenance tracking (signing chunks with source metadata and surfacing that to the user) actually helps in practice, or if people just ignore it like they ignore certificate warnings.
alan_sass: I've seen these data poisoning attacks from multiple perspectives lately (mostly from): SEC data ingestion + public records across state/federal databases.I believe it is possible to reduce the data poisoning from these sources by applying a layered approach like the OP, but I believe it needs many more dimensions with scoring to model true adversaries with loops for autonomous quarantine->processing->ingesting->verification->research->continue to verification or quarantine->then start again for all data that gets added after the initial population.Also, for: "1. Map every write path into your knowledge base. You can probably name the human editors. Can you name all the automated pipelines — Confluence sync, Slack archiving, SharePoint connectors, documentation build scripts? Each is a potential injection path. If you can’t enumerate them, you can’t audit them."I recommend scoring for each source with different levels of escalation for all processes from official vs user-facing sources. That addresses issues starting from the core vs allowing more access from untrusted sources.
zenoprax: "bad actor" can now be "ignorant employee running AI agents on their laptop".Threats from incompetence or ignorance will be multiplied by 'X' over 'Y' years as AI proliferates. Unsupervised AI agents and context poisoning will spiral things out of control in any environment.I'm interested in the effect of this with respect to AI-generated/assisted documentation and the recycling of that alongside the source-code back into the models.
malfist: Almost like defense in depth is key to good security. GP is ignoring that a truffle defense is only good until the first person is tricked
ineedasername: Any document store where you haven’t meticulously vetted each document— forget about actual bad actors— runs this risk. A size org across many years generates a lot of things. Analysis that were correct at one point and not at another, things that were simply wrong at all times, contradictory, etc.You have to choose model suitably robust is capabilities and design prompts or various post training regimes that are tested against such, where the model will identify the different ones and either choose the correct one on surface both with an appropriately helpful and clear explanation.At minimum you have to start from a typical model risk perspective and test and backtest the way you would traditional ML.
