Counter-GEO-Bench: Evaluating Defenses Against Information-Distorting Generative Engine Optimization
Generative engine optimization (GEO) enables content producers to increase the visibility of their web pages in generative search engines, but the same techniques can deliver targeted misinformation when adversaries publish ordinary-looking GEO-optimized documents that victim large language models (LLMs) retrieve and synthesize into distorted answers. No existing benchmark evaluates defenses against this threat under controlled conditions. Therefore, we present Counter-GEO-Bench, a defense benchmark that pairs 247 human-verified, quality-gated queries with information-preserving and information-distorting GEO rewrites, and evaluates defenses on attack success rate (ASR), false positive ra...
| Authors | Bing Zheng, Zongyao Zhao, Wenming Yang |
|---|---|
| Primary category | cs.IR |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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