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Sharing model context also shares data risk
Internal representations do not make information anonymous. Define data boundaries before connecting multiple models.
The Dual-Cache briefing describes research into communication between models through context representations. For multi-agent systems, the warning is straightforward: sensitive information need not be readable text to cross a boundary.
Map the components receiving context and their purposes. Identify each input's origin, who controls access and what remains stored. Changing a format is not evidence of anonymization and does not remove the need to evaluate a destination.
For textual integration with Rota, apply the policy before inference. Process retrieved documents and tool responses as well. If the architecture shares caches or vectors through another path, assess that path separately; a text API does not automatically sanitize it.
Compare processed context usefulness and look for reidentification through combined information. The research does not demonstrate isolation between customers. Rota processes supported inputs without promising to remove personal data from every internal model representation.
Original source: Dual-Cache permite comunicação entre LLMs com mistura entre camadas