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arXiv AI By Yizhu Wen, Nan Zhang, Haohan Yuan, Xun Chen, Haopeng Zhang, Hanqing Guo

Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots

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arXiv:2606. 12439v1 Announce Type: cross Abstract: Large language model (LLM) answer engines are increasingly used for information seeking, shifting visibility from ranked lists to synthesized answers.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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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.

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