paperJune 2026Unreviewed

When the Aggregator Cheats: Data-Free Backdoors in Federated LLM-based QA Systems

Chenqing Zhu, Yanbo Dai, Yulong Tian, Qingming Li, Songze Li

Abstract

Large Language Model (LLM)-based question-answering (QA) systems are increasingly deployed in sensitive domains such as healthcare, mental health counseling, and legal consultation. Federated learning (FL) enables collaborative training without sharing raw client data, for which locally trained models are aggregated at a central server (i.e., a cloud service provider) to obtain a global model. In this paper, we explore the potential vulnerability where a malicious aggregator, who may collude wit

Framework mappings

OWASP Top 10 for LLM Applications

LLM04Data and Model Poisoning

MITRE ATLAS

AML.T0020Poison Training Data

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Cite

@misc{zhu2026whena,
  title = {{When the Aggregator Cheats: Data-Free Backdoors in Federated LLM-based QA Systems}},
  author = {Chenqing Zhu and Yanbo Dai and Yulong Tian and Qingming Li and Songze Li},
  year = {2026},
  month = jun,
  eprint = {2606.27511},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.27511}
}