标题： RAGRank：使用PageRank对抗CTI大语言模型流水线中的投毒攻击 显示英文标题

标题： RAGRank: Using PageRank to Counter Poisoning in CTI LLM Pipelines

摘要： 检索增强生成（RAG）已成为在网络安全威胁情报（CTI）系统中实现大型语言模型（LLM）使用的主导架构模式。 然而，这种设计容易受到中毒攻击，之前提出的防御措施在CTI上下文中可能失效，因为网络威胁信息对于新兴攻击来说通常是全新的，而且复杂的威胁行为者可以模仿合法的格式、术语和风格惯例。 为了解决这个问题，我们提出通过在语料库上应用源可信度算法来加速现代RAG防御的鲁棒性，以PageRank为例。 在我们的实验中，我们定量地证明了我们的算法对恶意文档应用较低的权威分数，同时促进可信内容，使用标准化的MS MARCO数据集。 我们还在CTI文档和馈送中展示了我们算法的概念验证性能。 显示英文摘要

摘要： Retrieval-Augmented Generation (RAG) has emerged as the dominant architectural pattern to operationalize Large Language Model (LLM) usage in Cyber Threat Intelligence (CTI) systems. However, this design is susceptible to poisoning attacks, and previously proposed defenses can fail for CTI contexts as cyber threat information is often completely new for emerging attacks, and sophisticated threat actors can mimic legitimate formats, terminology, and stylistic conventions. To address this issue, we propose that the robustness of modern RAG defenses can be accelerated by applying source credibility algorithms on corpora, using PageRank as an example. In our experiments, we demonstrate quantitatively that our algorithm applies a lower authority score to malicious documents while promoting trusted content, using the standardized MS MARCO dataset. We also demonstrate proof-of-concept performance of our algorithm on CTI documents and feeds.