| title | One Polluted Page Is Enough: Evaluating Web Content Pollution in LLM Recommenders |
|---|---|
| authors | Minghao Luo, Liang Chen |
| date | 2026-08-24 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.13610 |
| upvotes | 5 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Search-augmented LLMs increasingly mediate everyday consumer recommendations by retrieving live web content. This creates a new risk: LLM recommenders may consume web content that Generative Engine Optimization (GEO) operators have polluted to mislead them. We ask: to what extent do they become unwitting promoters of fake products? We introduce FORGE (Fake Online Recommendations in Generative Environments), which locally rewrites real products in a frozen set of retrieved web pages into fake ones and measures how often the LLM recommends the fake product, across 225 real products in 15 categories and 5 consumer scenarios. Across 12 commercial and open-weights LLMs, all models are vulnerable: a single polluted page yields fooled rates of up to 27%, while the full top-3 replacement raises this to 73.8%. Vulnerability varies across categories, increasing when models lack stable prior knowledge of the products. Reasoning does not mitigate this vulnerability; instead, it often generates spurious social proof to justify false recommendations. None of the four defenses is adequate: the skepticism prompt can exacerbate vulnerability much like reasoning, the two consensus filters risk suppressing legitimate products, and credibility re-ranking helps every model but removes only a sixth of the fakes. We release the FORGE benchmark and the evaluation code at https://github.com/leoluolol/forge-benchmark. |
cite
@misc{luo2026one,
title={One Polluted Page Is Enough: Evaluating Web Content Pollution in LLM Recommenders},
author={Minghao Luo and Liang Chen},
year={2026},
eprint={2606.13610},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.13610}
}