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.
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
arXiv:2606. 11409v1 Announce Type: cross Abstract: Adversarial robustness evaluations of large language models (LLMs) typically report attack success rate (ASR) under fixed query budgets, implicitly treating all attacks as equally costly.
By Malikeh Ehghaghi, Bogl\'arka Ecsedi, Marsha Chechik, Colin Raffel
arXiv:2406.00083v3 Announce Type: replace-cross
Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant information from external knowledge bases t...
By Jiaqi Xue, Mengxin Zheng, Yebowen Hu, Fei Liu, Xun Chen, Qian Lou
arXiv:2603. 00801v2 Announce Type: replace Abstract: Language agents increasingly act as web-enabled systems that search, browse, and synthesize information from diverse sources.
By Shrey Shah, Levent Ozgur