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Inf. Syst."],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>\n                    Retrieval-Augmented Generation (RAG) improves the performance of Large Language Models (LLMs) by retrieving and integrating relevant information from external knowledge bases, which helps generate more accurate responses. However, RAG is vulnerable to\n                    <jats:italic toggle=\"yes\">retrieval poisoning attacks<\/jats:italic>\n                    , where attackers can induce LLM to produce inaccurate responses by injecting malicious documents into the retrieval process. In this article, we propose\n                    <jats:italic toggle=\"yes\">ShieldRAG<\/jats:italic>\n                    , a novel defense framework designed to counteract retrieval poisoning attacks by reshaping the retrieval embedding space. ShieldRAG leverages a dual-strategy effect realized via a majority-consensus mechanism: \u2460\n                    <jats:italic toggle=\"yes\">Push<\/jats:italic>\n                    : Implicitly forces the embedding of a user query away from malicious documents by filtering out their minority signals, reducing their influence. \u2461\n                    <jats:italic toggle=\"yes\">Pull<\/jats:italic>\n                    : Aligns the embedding of a user query closer to that of benign documents, reinforcing accurate retrieval. These strategies work synergistically to preserve retrieval integrity and enhance the quality of LLM-generated responses. Specifically, ShieldRAG operates through three key steps:\n                    <jats:italic toggle=\"yes\">Sliding Retrieval Explanation Generation<\/jats:italic>\n                    ,\n                    <jats:italic toggle=\"yes\">Keyword Aggregation<\/jats:italic>\n                    , and\n                    <jats:italic toggle=\"yes\">Query Targeting Optimization<\/jats:italic>\n                    . These three steps collectively ensure the effective integration of information from benign sources while filtering out malicious interference, thereby significantly enhancing the robustness of RAG systems against retrieval poisoning attacks. We evaluate ShieldRAG on four open-domain Question Answering (QA) datasets: Natural Questions, MS-MARCO, HotpotQA, and 2WikiMultiHopQA, using seven representative LLMs. Extensive experiments demonstrate that ShieldRAG significantly improves response accuracy while mitigating adversarial effects, showcasing strong generalization across multiple datasets and LLM architectures.\n                  <\/jats:p>","DOI":"10.1145\/3800948","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T14:03:54Z","timestamp":1772805834000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["<i>Push and Pull<\/i>\n                    : Defending against Retrieval Poisoning Attacks via Embedding Space Reshaping"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7842-6605","authenticated-orcid":false,"given":"Longzhu","family":"He","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4506-3927","authenticated-orcid":false,"given":"Xiyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1609-7089","authenticated-orcid":false,"given":"Quan","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8179-7503","authenticated-orcid":false,"given":"Chaozhuo","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7765-8466","authenticated-orcid":false,"given":"Zheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Academy of Artificial Intelligence, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5359-1540","authenticated-orcid":false,"given":"Pengpeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"China Academy of Civil Aviation Science and Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4266-7527","authenticated-orcid":false,"given":"Sen","family":"Su","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China and Chongqing University of Posts and Telecommunications, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et al. 2023. 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