Assessing Attack Surfaces in Generative Search Engines through Publisher Attributes: A Case Study in Political Domains

Abstract

We characterize the attack surface of generative search engines (GSEs) against poisoning attacks in the political domain, from the perspectives of citation selection and personalization. GSEs integrate web search and answer generation with user preferences and backgrounds using large language models (LLMs). They play a crucial role in how users access information on the web. Because anyone can publish content on the web, GSEs are vulnerable to poisoning attacks that manipulate citations to under

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@misc{mochizuki2026assessing,
  title = {{Assessing Attack Surfaces in Generative Search Engines through Publisher Attributes: A Case Study in Political Domains}},
  author = {R. Mochizuki and Shusuke Komatsu and Souta Noguchi and Kazuto Ataka},
  year = {2026},
  month = aug,
  eprint = {2608.15814},
  archivePrefix = {arXiv},
  url = {https://www.semanticscholar.org/paper/2ad14c8c5cf1f454c49512afc25a9e55fd2efa88}
}