paperJuly 2026Unreviewed

GPE: Evaluating Robust Evidence Aggregation for Fact Verification under Controllable GEO-Style Poisoning

Zhaoqi Wang, Zijian Zhang, Xiaomei Yuan, Pengtao Kou, Jiamou Liu, Zhen Li, Liehuang Zhu

Abstract

Large language models increasingly use search tools to retrieve up-to-date information, introducing a new attack surface in which retrieved documents can be manipulated. This risk is amplified by the development of generative engine optimization, which can make selected content more likely to be retrieved, cited, and adopted by models. Existing fact-verification benchmarks and evaluation frameworks do not provide the controlled evidence environments needed to assess robustness against GEO poison

Framework mappings

OWASP Top 10 for LLM Applications

LLM04Data and Model Poisoning

MITRE ATLAS

AML.T0020Poison Training Data

Suggested from the entry's categories.

Cite

@misc{wang2026gpe,
  title = {{GPE: Evaluating Robust Evidence Aggregation for Fact Verification under Controllable GEO-Style Poisoning}},
  author = {Zhaoqi Wang and Zijian Zhang and Xiaomei Yuan and Pengtao Kou and Jiamou Liu and Zhen Li and Liehuang Zhu},
  year = {2026},
  month = jul,
  eprint = {2607.20730},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2607.20730}
}