Query Obfuscation with Embedding-Optimized Noise (EON): A Representational Blindspot in Agentic Retrieval

In modern agentic and semantic retrieval systems, dense embedding models increasingly determine which intent, tool, document, memory, or image to retrieve for a query. We identify a representational failure mode in this pipeline: a meaningful query can be replaced by visibly nonsemantic content while preserving much of its retrieval outcome, even when the candidate bank is hidden from the optimizer. We introduce *Embedding-Optimized Noise (EON)*, constructed using only the natural query representation, without access to candidate embeddings, relevance labels, rankings, retrieval feedback, or guardrail decisions. Despite this corpus-blind objective, EONs frequently preserve intent and tool routing, benchmark document search, episodic agent-memory retrieval, and cross-modal search across multiple dense embedding families. Crucially, this disconnect carries serious security implications: for malicious queries, an EON can reliably evade input guardrails that block the natural query, executing the intended safety-sensitive retrieval undetected. Through the geometry of the embedding space, we then characterize how query-level representation matching can reliably maintain subsequent retrieval outcomes over candidates unseen during optimization. Together, these findings expose a fundamental phenomenon across dual encoders that we call BLINDSPOT, where systems remain operationally blind to nonsemantic artifacts while reliably retrieving real, consequential content.