Recent observations indicate that fabricated citations are increasingly passing through the peer review processes of leading AI conferences. This issue raises significant concerns regarding the integrity of academic research in the field. Notably, commercial large language models (LLMs) have demonstrated limitations in identifying these inaccuracies, which can undermine the credibility of published work. In response to this challenge, a new open-source tool named CiteAudit has emerged, claiming to detect errors that existing models like GPT, Gemini, and Claude fail to recognize. This development warrants careful consideration, as it highlights both the opportunities for improving research validation and the risks associated with reliance on automated systems. A comprehensive evaluation of CiteAudit's effectiveness and its broader implications for the academic community will be essential in the coming months.