The robustness of recent Large Language Models (LLMs) has become increasingly crucial as their applicability expands across various domains and real-world applications. Retrieval-Augmented Generation (RAG) is a promising solution for addressing the limitations of LLMs, yet existing studies on the robustness of RAG often overlook the interconnected relationships between RAG components or the potential threats prevalent in real-world databases, such as minor textual errors. In this work, we investigate two underexplored aspects when assessing the robustness of RAG: 1) vulnerability to noisy documents through low-level perturbations and 2) a holistic evaluation of RAG robustness. Furthermore, we introduce a novel attack method, the Genetic Attack on RAG (\textit{GARAG}), which targets these aspects. Specifically, GARAG is designed to reveal vulnerabilities within each component and test the overall system functionality against noisy documents. We validate RAG robustness by applying our \textit{GARAG} to standard QA datasets, incorporating diverse retrievers and LLMs. The experimental results show that GARAG consistently achieves high attack success rates. Also, it significantly devastates the performance of each component and their synergy, highlighting the substantial risk that minor textual inaccuracies pose in disrupting RAG systems in the real world.

通过寻求大规模语言模型与检索生成的完美结合来解决其局限性，本论文研究了在评估检索生成鲁棒性时常被忽视的两个方面：对噪声文档的脆弱性和对检索生成系统的整体评估。同时，论文引入一种新的攻击方法——遗传攻击，用于揭示组成部分中的漏洞并测试系统对噪声文档的功能性。实验结果表明，遗传攻击在不同的数据库和语言模型上均能取得较高的攻击成功率，同时显著破坏了每个组成部分以及它们之间的协同作用，凸显了微小的文本不准确性对破坏现实世界中的检索生成系统所带来的巨大风险。

低级扰动模拟野外文档对 RAG 管道的遗传攻击