SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agents
Generative Engine Optimization (GEO) lets content owners rewrite web content to increase their visibility in generative systems. In recommendation agents, this creates a risk that sources controlled by sellers make flawed products appear better supported than they are. We study this risk at the generation stage by asking whether recommendation agents continue to make decisions that align with user utility when these sources are rewritten for GEO. To make this question measurable, we construct SafeGEO, an evaluation suite with 22 GEO attack variants across 600 recommendation cases. We empirically show that GEO attacks can promote flawed target products: they increase the rate at which such...
| Authors | Qianfeng Wen, Yifan Simon Liu, Xin Liu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 8 June 2026 |
| Last updated at source | 4 September 2026 |
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