As large language models (LLMs) become deeply integrated into the workflows of research production and academic publishing—spanning manuscript editing, peer review, language polishing, translation, topic recommendation, and even writing assistance—the technical foundations of academic journal knowledge production are undergoing structural changes. This transformation has prompted a renewed examination of the risks that LLM adoption introduces in academic journals. Focusing on these application risks, this paper systematically analyzes practical challenges across content originality, academic integrity, and data security. Building on this, the paper conducts an in-depth analysis of the root causes at the technical, institutional, and ethical levels, proposing a three-dimensional governance framework that integrates "technical prevention and control, institutional design, and ethics and education." As artificial intelligence technologies, epitomized by LLMs, have rapidly permeated the entire workflow of academic journals—from topic planning and content creation to editorial review, peer assessment, and publication dissemination, they have significantly enhanced publishing efficiency and intelligence. However, while this "algorithmic intervention" boosts productivity, it also introduces hidden and systemic risks that pose severe challenges to the foundations of academic journals. Three major risks—weakened content originality, erosion of academic integrity, and the loss of data security—are deeply eroding the academic ecosystem built on authenticity, innovation, and trustworthiness. The core gap identified in prior scholarship is the absence of systematic analysis from the perspective of "academic journals as governance entities"—specifically, on how LLMs reshape their core functions. Specifically, existing studies fail to thoroughly address three questions: How do LLMs deconstruct journals' mechanisms for evaluating originality? How do they blur the chain of academic accountability? And how do they challenge journals' role as guardians of credible knowledge boundaries? In light of this, this paper adopts a holistic research approach of "risk identification, cause tracing, and governance strategy." By systematically analyzing the specific risk manifestations triggered by LLMs across journal publishing processes, the study traces their technical, institutional, and ethical causes, and explores a scientifically feasible governance framework. The aim is to provide practical, forward-looking support for journal institutions to safely and responsibly apply AI technologies while maintaining compliance. This study reveals three deep interconnections among core risks: copyright and originality disputes in generative content, technology-enabled distortions of academic misconduct, and the potential leakage and misuse of journal core data. The analysis demonstrates that no single approach is adequate to resolve these systemic challenges. Effective governance must therefore integrate technological safeguards, institutional development, and ethical guidance to establish a collaborative governance framework. LLMs are not merely tools for paper writing, but institutional forces reshaping the logic of academic knowledge production and journal governance. Governance should not restrict innovation but instead lay the foundation for responsible innovation, with the key being the achievement of a dynamic balance among technological efficiency, academic quality, and ethical security. Ultimately, the goal is to foster a new academic publishing ecosystem that prioritizes human-centered values while technology is oriented toward practical use.