The “silent hallucination” loop: how our autonomous data pipeline poisoned its own vector store

An autonomous data pipeline at a company poisoned its own vector store by ingesting hallucinated outputs from a large language model, creating a feedback loop that degraded retrieval accuracy without triggering any monitoring alerts. The incident highlights a critical failure mode in AI-driven data systems where self-referential data ingestion can silently corrupt knowledge bases.