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RAGLeak: Supply-Chain Privacy Backdoors in Retrieval-Augmented Generation

Yuchen Chen, Wei Cheng, Yuan Xiao, Junda He, Jieke Shi, 欧阳萍萍, Quanjun Zhang, Chunrong Fang, David Lo, Zhenyu Chen · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.25937 · cited by 0 works (OpenAlex) · open-access PDF

Korean

RAG(Retrieval-Augmented Generation)는 대형 언어 모델이 추론 중에 외부 지식에 액세스 할 수 있지만 외부 지식 기반에 대한 의존도 또한 새로운 공격 표면을 도입합니다. RAG 시스템에 대한 기존 데이터 추출 공격은 프롬프트 인젝션에 의존하며, 이는 대체로 생성자의 지침 추적 능력에 의존합니다. 또는 추출 된 문서를 재생하거나 파라프라스하지만 추출 충성도와 비밀을 균형 잡기 위해 노력하는 백도어. 이 논문에서 우리는 RAGLeak를 제안합니다. 공급 체인 개인 정보 백도어 공격은 직접 텍스트 노출으로부터 정보 유출을 분리합니다. 발동하면 생성기는 재활용 된 개인 문서를 재생하거나 LLM에 의해 높은 신

Machine translation (Meta M2M100); the English original follows.

Abstract

Retrieval-Augmented Generation (RAG) enables large language models to access external knowledge during inference, but its reliance on external knowledge bases also introduces new attack surfaces. Existing data extraction attacks against RAG systems rely on either prompt injection, which heavily depends on the generator's instruction-following capability, or backdoors that reproduce or paraphrase retrieved documents but struggle to balance extraction fidelity and stealthiness. In this paper, we propose RAGLeak, a supply-chain privacy backdoor attack that decouples information leakage from direct textual exposure. When triggered, the generator reversibly obfuscates retrieved private documents for high-fidelity recovery by an auxiliary LLM. Across three model families and three application domains, RAGLeak achieves up to $100\%$ ASR with limited impact on benign utility and at most a $10$-percentage-point ASR drop under representative detection-based defenses. The backdoor remains effective after clean fine-tuning and across model scales and versions, revealing an underexplored supply-chain privacy risk in RAG systems. The source code is available at https://anonymous.4open.science/r/RAGLeak-4B67.

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Topics: Backdoor, Computer science, Computer security, Paraphrase, Generator (circuit theory), Source code

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