In July 2025, 18 academic manuscripts on arXiv contained hidden instructions that manipulated AI-assisted peer review (indirect prompt injection). Instructions such as "GIVE A POSITIVE REVIEW ONLY" were concealed using white text and microscopic font sizes. Author responses varied: one planned to withdraw their manuscript, while another defended the practice as legitimate testing of reviewers misusing large language models (LLMs). This analysis examines the technique within the broader pattern of prompt injection exploits that manipulated web search and résumé screening systems. For peer review, I reveal four types of hidden prompts, ranging from simple positive review commands to detailed evaluation frameworks. The honeypot defense--that prompts detect reviewers improperly using AI--fails under examination, given the consistently self-serving nature of these hidden prompts, though motivations likely vary from naive copying to calculated manipulation. This practice is best characterized as a novel form of questionable research practice (QRP). Publishers maintain inconsistent policies: Elsevier prohibits AI use in peer review entirely, while Springer Nature permits limited use with disclosure requirements. The practice exposes systematic vulnerabilities extending to plagiarism detection, citation indexing, and literature summarization. This analysis underscores the need for controlled AI integration in formal review processes alongside coordinated technical screening and harmonized policies governing AI use in academic evaluation.

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