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This paper demonstrates that websites can identify which large language model powers a browsing agent by analyzing its behavioral patterns and timing data, achieving up to 96% F1 score across 14 frontier LLMs. It formalizes this attack surface and shows that random timing delays are insufficient to prevent identification.