@inproceedings{chen-etal-2026-much,
title = "How Much Can We Trust {LLM} Search Agents? Measuring Endorsement Vulnerability to Web Content Manipulation",
author = {Chen, Yimeng and
Ren, Zhe and
Laakom, Firas and
Li, Yu and
Guo, Dandan and
Schmidhuber, J{\"u}rgen},
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.2128/",
pages = "48390--48413",
ISBN = "979-8-89176-459-0",
abstract = "Large language model (LLM)-based search agents synthesize open-web content into actionable recommendations on behalf of users, creating a risk that attacker-published pages are transformed into endorsed claims. We introduce SearchGEO, a controlled evaluation framework for measuring endorsement corruption in LLM-based web-search agents, combining a web-evidence manipulation pipeline, a five-mode attack taxonomy, and multiple output-level metrics. We evaluate 13 LLM backends on 308 cases each. Results show that vulnerability patterns vary across backends: overall attack success rate (ASR) ranges from 0.0{\%} on Claude-Sonnet-4.6 to 31.4{\%} on Gemini-3-Flash, the strongest attack mode differs by model family, and the same deployment scaffold could amplify or decrease ASR on different backends. An auxiliary agent-skill probe, where endorsement becomes an install command, exposes a sharp split among otherwise robust backends: Claude over-rejects while GPT over-trusts. These findings argue for treating recommendation reliability under adversarial search content as a first-class dimension of backend safety evaluation."
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%0 Conference Proceedings
%T How Much Can We Trust LLM Search Agents? Measuring Endorsement Vulnerability to Web Content Manipulation
%A Chen, Yimeng
%A Ren, Zhe
%A Laakom, Firas
%A Li, Yu
%A Guo, Dandan
%A Schmidhuber, Jürgen
%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 chen-etal-2026-much
%X Large language model (LLM)-based search agents synthesize open-web content into actionable recommendations on behalf of users, creating a risk that attacker-published pages are transformed into endorsed claims. We introduce SearchGEO, a controlled evaluation framework for measuring endorsement corruption in LLM-based web-search agents, combining a web-evidence manipulation pipeline, a five-mode attack taxonomy, and multiple output-level metrics. We evaluate 13 LLM backends on 308 cases each. Results show that vulnerability patterns vary across backends: overall attack success rate (ASR) ranges from 0.0% on Claude-Sonnet-4.6 to 31.4% on Gemini-3-Flash, the strongest attack mode differs by model family, and the same deployment scaffold could amplify or decrease ASR on different backends. An auxiliary agent-skill probe, where endorsement becomes an install command, exposes a sharp split among otherwise robust backends: Claude over-rejects while GPT over-trusts. These findings argue for treating recommendation reliability under adversarial search content as a first-class dimension of backend safety evaluation.
%U https://aclanthology.org/2026.emnlp-main.2128/
%P 48390-48413
Markdown (Informal)
[How Much Can We Trust LLM Search Agents? Measuring Endorsement Vulnerability to Web Content Manipulation](https://aclanthology.org/2026.emnlp-main.2128/) (Chen et al., EMNLP 2026)
ACL