arXiv:2606. 27736v1 Announce Type: new Abstract: The rapid spread of fake news poses increasing threats to information ecosystems, especially as AI-generated misinformation under Generative Engine Optimization (GEO) poisoning allows adversarially crafted content to be systematically surfaced by retrieval systems, contaminating LLM reasoning.
By Zhaoqi Wang, Zijian Zhang, Kun Zheng, Zhen Li, Xin Li, Chunlei Li, Jiamou Liu
arXiv:2501. 14728v2 Announce Type: replace-cross Abstract: While generative artificial intelligence (GenAI) models have achieved significant success, their misuse for generating deceptive content raises growing concerns about online information security.
By Zehong Yan, Peng Qi, Wynne Hsu, Mong Li Lee
The paper introduces GEO Defender, a two‑stage defense system designed to protect generative search engines from malicious Generative Engine Optimization (GEO) attacks that rewrite web documents to manipulate generated answers. GEO Defender comprises a Shield Reranker, which learns a defensive residual to demote GEO‑rewritten documents while maintaining relevance, and a Training‑Free Shield Generation component that creates a natural‑language library guiding the target LLM’s source usage during inference. Experiments on both closed‑source and open‑source large language models show that GEO Defender dramatically lowers attack success rates from 50.32% to 6.20%, preserves over 94% of benign evidence usage, and maintains answer quality while generalizing to unseen attacks.
By Haozhang Li, Yangguang Shao, Xinjie Lin, Zhong Guan, Mi Zhou, Junzheng Shi
Counter‑GEO‑Bench is a new benchmark that evaluates how well defenses can stop large language models from producing misinformation when faced with generative engine‑optimized (GEO) content. It contains 247 human‑verified queries paired with both information‑preserving and information‑distorting GEO rewrites, and measures attack success rate, false positives, and answer quality across three victim LLMs. The study shows that existing off‑the‑shelf defenses reduce attack success by at most 5.7 %, while a lightweight baseline called C‑GEO Guard cuts success by 47.6 % with minimal loss of utility.
By Bing Zheng, Zongyao Zhao, Wenming Yang
Counter‑GEO‑Bench is a new defense benchmark that tests how well systems can resist misinformation generated by generative engine optimization (GEO). It contains 247 human‑verified queries paired with both information‑preserving and information‑distorting GEO rewrites, and evaluates defenses on attack success rate, false positives, and answer quality across three large language models. Existing off‑the‑shelf defenses reduce attack success by at most 5.7%, while a lightweight baseline, C‑GEO Guard, cuts it by 47.6% with minimal loss of utility.
arXiv:2609.06027v1 Announce Type: cross
Abstract: Search-augmented LLM agents are increasingly used for consumer decisions, making them vulnerable to Generative Engine Optimization (GEO) poisoning. E...
By Zhongan Bi, Qiwen Wang, Jianrong Jiang, Jigang Ding, Wenwen Xiong, Changhua Meng, Xuanang Gao, Kepeng Lin, Changjiang Jiang, Yiang Chen, Huan Yao, Wei Wang, Zhenyu Ma, Wenhui Dong