An arXiv paper titled "How Much Can We Trust LLM Search Agents? Measuring Endorsement Vulnerability to Web Content Manipulation" (submitted 15 Jun 2026) introduces SearchGEO, a controlled evaluation framework for measuring endorsement corruption in LLM-based web-search agents. The authors, led by Yimeng Chen and five co-authors, evaluate 13 LLM backends on 308 cases each. The paper reports overall attack success rate (ASR) varying across backends, from 0.0% on Claude-Sonnet-4.6 to 31.4% on Gemini-3-Flash. The authors describe a five-mode attack taxonomy, a web-evidence manipulation pipeline, multiple output-level metrics, and an auxiliary agent-skill probe that, the paper reports, shows a sharp split: Claude "over-rejects" while GPT "over-trusts." The paper argues recommendation reliability under adversarial search content should be a first-class safety evaluation dimension.