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    <abstract>Federated Retrieval-Augmented Generation (FedRAG) is attractive for privacy-sensitive applications because full local corpora remain on clients. As a result, routing must rely on client-provided semantic profiles, creating a new opportunity for manipulation. We introduce Routing Hijacking, a routing-stage attack in which a malicious client forges its profile to attract target queries despite having irrelevant underlying data. We show that this vulnerability is severe. Across three representative FedRAG routing architectures, Routing Hijacking consistently misroutes target queries and leads to downstream disruptions and failures, including missing evidence, poisoning, incorrect answers, and hallucinations. In a controlled MedQA-USMLE stress test, we further show that poisoned retrieved evidence can mislead models across scales, leading to incorrect answers, hallucinations, and sycophantic failures. Existing defenses do not close this gap: encrypted routing preserves the exploited ranking, and Byzantine-robust Federated Learning (FL) rules transfer poorly to heterogeneous routing profiles. To address this gap, we propose a trust-aware post-routing framework that reweights clients using returned-evidence feedback, including retrieval relevance, profile consistency, and cross-client agreement; online experiments show that it suppresses persistent hijacking over recurring queries and transfers to a learned neural router. Our findings establish routing integrity as a security challenge in FedRAG and highlight the need for stronger defenses for secure federated retrieval.</abstract>
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%0 Conference Proceedings
%T A Wolf in Sheep’s Clothing: Targeted Routing Hijacking in Federated RAG
%A Mu, Junjie
%A Li, Qiongxiu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F mu-li-2026-wolf
%X Federated Retrieval-Augmented Generation (FedRAG) is attractive for privacy-sensitive applications because full local corpora remain on clients. As a result, routing must rely on client-provided semantic profiles, creating a new opportunity for manipulation. We introduce Routing Hijacking, a routing-stage attack in which a malicious client forges its profile to attract target queries despite having irrelevant underlying data. We show that this vulnerability is severe. Across three representative FedRAG routing architectures, Routing Hijacking consistently misroutes target queries and leads to downstream disruptions and failures, including missing evidence, poisoning, incorrect answers, and hallucinations. In a controlled MedQA-USMLE stress test, we further show that poisoned retrieved evidence can mislead models across scales, leading to incorrect answers, hallucinations, and sycophantic failures. Existing defenses do not close this gap: encrypted routing preserves the exploited ranking, and Byzantine-robust Federated Learning (FL) rules transfer poorly to heterogeneous routing profiles. To address this gap, we propose a trust-aware post-routing framework that reweights clients using returned-evidence feedback, including retrieval relevance, profile consistency, and cross-client agreement; online experiments show that it suppresses persistent hijacking over recurring queries and transfers to a learned neural router. Our findings establish routing integrity as a security challenge in FedRAG and highlight the need for stronger defenses for secure federated retrieval.
%U https://aclanthology.org/2026.emnlp-main.668/
%P 15388-15411

Markdown (Informal)

[A Wolf in Sheep’s Clothing: Targeted Routing Hijacking in Federated RAG](https://aclanthology.org/2026.emnlp-main.668/) (Mu & Li, EMNLP 2026)

• A Wolf in Sheep’s Clothing: Targeted Routing Hijacking in Federated RAG (Mu & Li, EMNLP 2026)

ACL

• Junjie Mu and Qiongxiu Li. 2026. A Wolf in Sheep’s Clothing: Targeted Routing Hijacking in Federated RAG. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 15388–15411, Budapest, Hungary. Association for Computational Linguistics.