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arXiv · 2605.22041

RADAR: Defending RAG Dynamically against Retrieval Corruption

Ziyuan Chen·Yueming Lyu·Yi Liu·Weixiang Han·Jing Dong·Caifeng Shan·Tieniu Tan
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Abstract

While RAG systems are increasingly deployed in dynamic web search, temporal volatility amplifies their vulnerability to adversarial attacks. Existing static-oriented defenses struggle to handle evolving threats and incur prohibitive storage costs in dynamic settings. We propose RADAR, a framework that models reliable context selection as a graph-based energy minimization problem, solved exactly via Max-Flow Min-Cut. By incorporating a Bayesian memory node, RADAR recursively updates a belief state instead of archiving raw historical documents, effectively balancing stability against attacks with adaptability to genuine knowledge shifts. Experiments on a novel dynamic dataset show that RADAR achieves superior robustness and response quality with minimal storage overhead compared to the baselines.

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Ziyuan Chen, Yueming Lyu, Yi Liu, Weixiang Han, Jing Dong, Caifeng Shan, Tieniu Tan. 2026-05-21. RADAR: Defending RAG Dynamically against Retrieval Corruption. https://arxiv.org/abs/2605.22041

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