@inproceedings{zhang-etal-2026-leakdojo,
title = "{L}eak{D}ojo: Decoding the Leakage Threats of {RAG} Systems",
author = "Zhang, Maosen and
Dong, Jianshuo and
Boting, Lu and
Wenyue, Li and
Zhang, Xiaoping and
Zhang, Tianwei and
Qiu, Han",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.287/",
pages = "5790--5811",
ISBN = "979-8-89176-395-1",
abstract = "Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to leverage external knowledge, but also exposes valuable RAG databases to leakage attacks. As RAG systems grow more complex and LLMs exhibit stronger instruction-following capabilities, existing studies fall short of systematically assessing RAG leakage risks. We present LeakDojo, a configurable framework for controlled evaluation of RAG leakage. Using LeakDojo, we benchmark six existing attacks across fourteen LLMs, four datasets, and diverse RAG systems. Our study reveals that (1) query generation and adversarial instructions contribute independently to leakage, with overall leakage well approximated by their product; (2) stronger instruction-following capability correlates with higher leakage risk; and (3) improvements in RAG faithfulness can introduce increased leakage risk. These findings provide actionable insights for understanding and mitigating RAG leakage in practice. Our codebase is available at https://github.com/yeasen-z/LeakDojo."
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<abstract>Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to leverage external knowledge, but also exposes valuable RAG databases to leakage attacks. As RAG systems grow more complex and LLMs exhibit stronger instruction-following capabilities, existing studies fall short of systematically assessing RAG leakage risks. We present LeakDojo, a configurable framework for controlled evaluation of RAG leakage. Using LeakDojo, we benchmark six existing attacks across fourteen LLMs, four datasets, and diverse RAG systems. Our study reveals that (1) query generation and adversarial instructions contribute independently to leakage, with overall leakage well approximated by their product; (2) stronger instruction-following capability correlates with higher leakage risk; and (3) improvements in RAG faithfulness can introduce increased leakage risk. These findings provide actionable insights for understanding and mitigating RAG leakage in practice. Our codebase is available at https://github.com/yeasen-z/LeakDojo.</abstract>
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%0 Conference Proceedings
%T LeakDojo: Decoding the Leakage Threats of RAG Systems
%A Zhang, Maosen
%A Dong, Jianshuo
%A Boting, Lu
%A Wenyue, Li
%A Zhang, Xiaoping
%A Zhang, Tianwei
%A Qiu, Han
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhang-etal-2026-leakdojo
%X Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to leverage external knowledge, but also exposes valuable RAG databases to leakage attacks. As RAG systems grow more complex and LLMs exhibit stronger instruction-following capabilities, existing studies fall short of systematically assessing RAG leakage risks. We present LeakDojo, a configurable framework for controlled evaluation of RAG leakage. Using LeakDojo, we benchmark six existing attacks across fourteen LLMs, four datasets, and diverse RAG systems. Our study reveals that (1) query generation and adversarial instructions contribute independently to leakage, with overall leakage well approximated by their product; (2) stronger instruction-following capability correlates with higher leakage risk; and (3) improvements in RAG faithfulness can introduce increased leakage risk. These findings provide actionable insights for understanding and mitigating RAG leakage in practice. Our codebase is available at https://github.com/yeasen-z/LeakDojo.
%U https://aclanthology.org/2026.findings-acl.287/
%P 5790-5811
Markdown (Informal)
[LeakDojo: Decoding the Leakage Threats of RAG Systems](https://aclanthology.org/2026.findings-acl.287/) (Zhang et al., Findings 2026)
ACL
- Maosen Zhang, Jianshuo Dong, Lu Boting, Li Wenyue, Xiaoping Zhang, Tianwei Zhang, and Han Qiu. 2026. LeakDojo: Decoding the Leakage Threats of RAG Systems. In Findings of the Association for Computational Linguistics: ACL 2026, pages 5790–5811, San Diego, California, United States. Association for Computational Linguistics.