This paper investigates the presence and impact of questionable, AI-generated academic papers on widely used preprint repositories, with a focus on their role in citation manipulation. Motivated by suspicious patterns observed in publications related to our ongoing research on GenAI-enhanced cybersecurity, we identify clusters of questionable papers and profiles. These papers frequently exhibit minimal technical content, repetitive structure, unverifiable authorship, and mutually reinforcing citation patterns among a recurring set of authors. To assess the feasibility and implications of such practices, we conduct a controlled experiment: generating a fake paper using GenAI, embedding citations to suspected questionable publications, and uploading it to one such repository (ResearchGate). Our findings demonstrate that such papers can bypass platform checks, remain publicly accessible, and contribute to inflating citation metrics like the H-index and i10-index. We present a detailed analysis of the mechanisms involved, highlight systemic weaknesses in content moderation, and offer recommendations for improving platform accountability and preserving academic integrity in the age of GenAI.

翻译：本文研究了可疑的、由人工智能生成的学术论文在广泛使用的预印本存储库中的存在及其影响，重点关注其在引用操纵中的作用。受我们正在进行的关于生成式人工智能增强网络安全研究中观察到的可疑模式启发，我们识别出可疑论文和作者群体的集群。这些论文通常表现出技术内容极少、结构重复、作者身份无法验证，以及在一组反复出现的作者之间存在相互强化的引用模式。为评估此类做法的可行性和影响，我们进行了一项对照实验：使用生成式人工智能生成一篇虚假论文，嵌入对疑似可疑出版物的引用，并将其上传至此类存储库之一（ResearchGate）。我们的研究结果表明，此类论文能够绕过平台检查，保持公开可访问状态，并有助于夸大H指数和i10指数等引用指标。我们详细分析了所涉及的机制，指出了内容审核中的系统性缺陷，并为提升平台问责制和在生成式人工智能时代维护学术诚信提供了建议。