@inproceedings{zhang-etal-2026-lazy,
title = "Lazy Grounding: Attacking Search Agents with Factual Evidence",
author = "Zhang, Yulin and
Huang, Yukun and
Chen, Sanxing and
Lin, Tianyi and
Yang, Ziang and
Yin, Xunjian and
Dhingra, Bhuwan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2600/",
pages = "59063--59077",
ISBN = "979-8-89176-459-0",
abstract = "Search agents mitigate hallucination by grounding their answers in retrieved web results. However, retrieval-based approaches also introduce an attack surface: agents may cite misinformation from poisoned search corpora containing false or malicious documents. We demonstrate that, in some cases, search agents' reasoning and responses may be steered by completely factual but distracting information. We refer to this failure as lazy grounding. We expose lazy grounding by injecting nearby evidence from answer-changing rewrites of benchmark questions into the search corpora. Each document contains factual evidence that supports a neighboring rewritten question but is retrieved for the original question. Across 12 model{--}benchmark pairs, the attack causes the accuracy of search agents' responses to drop by 5.9 points on average and by up to 17.3 points, while inducing nearby-answer adoption in every setting. The effect is even stronger when nearby evidence appears later or is more answer-shaped. Our results show that robust search agents must defend against not only misinformation but also the misapplication of factual evidence. The code is publicly available at https://github.com/frankyzha/lazy-grounding."
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<abstract>Search agents mitigate hallucination by grounding their answers in retrieved web results. However, retrieval-based approaches also introduce an attack surface: agents may cite misinformation from poisoned search corpora containing false or malicious documents. We demonstrate that, in some cases, search agents’ reasoning and responses may be steered by completely factual but distracting information. We refer to this failure as lazy grounding. We expose lazy grounding by injecting nearby evidence from answer-changing rewrites of benchmark questions into the search corpora. Each document contains factual evidence that supports a neighboring rewritten question but is retrieved for the original question. Across 12 model–benchmark pairs, the attack causes the accuracy of search agents’ responses to drop by 5.9 points on average and by up to 17.3 points, while inducing nearby-answer adoption in every setting. The effect is even stronger when nearby evidence appears later or is more answer-shaped. Our results show that robust search agents must defend against not only misinformation but also the misapplication of factual evidence. The code is publicly available at https://github.com/frankyzha/lazy-grounding.</abstract>
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%0 Conference Proceedings
%T Lazy Grounding: Attacking Search Agents with Factual Evidence
%A Zhang, Yulin
%A Huang, Yukun
%A Chen, Sanxing
%A Lin, Tianyi
%A Yang, Ziang
%A Yin, Xunjian
%A Dhingra, Bhuwan
%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 zhang-etal-2026-lazy
%X Search agents mitigate hallucination by grounding their answers in retrieved web results. However, retrieval-based approaches also introduce an attack surface: agents may cite misinformation from poisoned search corpora containing false or malicious documents. We demonstrate that, in some cases, search agents’ reasoning and responses may be steered by completely factual but distracting information. We refer to this failure as lazy grounding. We expose lazy grounding by injecting nearby evidence from answer-changing rewrites of benchmark questions into the search corpora. Each document contains factual evidence that supports a neighboring rewritten question but is retrieved for the original question. Across 12 model–benchmark pairs, the attack causes the accuracy of search agents’ responses to drop by 5.9 points on average and by up to 17.3 points, while inducing nearby-answer adoption in every setting. The effect is even stronger when nearby evidence appears later or is more answer-shaped. Our results show that robust search agents must defend against not only misinformation but also the misapplication of factual evidence. The code is publicly available at https://github.com/frankyzha/lazy-grounding.
%U https://aclanthology.org/2026.emnlp-main.2600/
%P 59063-59077
Markdown (Informal)
[Lazy Grounding: Attacking Search Agents with Factual Evidence](https://aclanthology.org/2026.emnlp-main.2600/) (Zhang et al., EMNLP 2026)
ACL
- Yulin Zhang, Yukun Huang, Sanxing Chen, Tianyi Lin, Ziang Yang, Xunjian Yin, and Bhuwan Dhingra. 2026. Lazy Grounding: Attacking Search Agents with Factual Evidence. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 59063–59077, Budapest, Hungary. Association for Computational Linguistics.