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arXiv · 2605.26754

Cordon-MAS: Defending RAG against Knowledge Poisoning via Information-Flow Control

Zhe Yu·Wenpeng Xing·Gaolei Li·Shuguang Xiong·Hongzhi Wang·Xuyang Teng·Meng Han
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Abstract

Retrieval-augmented generation (RAG) increasingly underpins high-stakes applications, yet remains vulnerable to Confundo-style poisoning where adversarially optimized documents manipulate generated outputs. Existing defenses assume that detecting poisoned evidence prevents harm. We show this assumption is incorrect: models exhibit a monitoring-control gap -- they can detect contradictions in retrieved evidence yet still act on poisoned claims. We introduce the Cordon Principle -- no agent capable of final synthesis may access untrusted natural-language evidence -- and realize it through CORDON-MAS, a compartmentalized framework that enforces this principle architecturally by separating evidence extraction, cross-source audit, and answer synthesis into agents with asymmetric memory privileges. Across five BEIR datasets, CORDON-MAS reduces attack success rate by 92.4\% relative to undefended RAG. This reframes RAG poisoning from a detection problem to an information-flow control problem.

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Zhe Yu, Wenpeng Xing, Gaolei Li, Shuguang Xiong, Hongzhi Wang, Xuyang Teng, Meng Han. 2026-05-26. Cordon-MAS: Defending RAG against Knowledge Poisoning via Information-Flow Control. https://arxiv.org/abs/2605.26754

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