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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

Japanese

RAG(Retrieval-Augmented Generation)は、外部の知識ベースへの依存が新たな攻撃表面を導入するが、外部の知識ベースに依存する大規模な言語モデルが外部の知識にアクセスできるようにします。 RAGシステムに対する既存のデータ抽出攻撃は、プロンプトインジェクションに大きく依存し、ジェネレーターの指示に従う能力に大きく依存し、またはリクエストされた文書を再現またはパラフラッシュするバックドアに依存しますが、抽出忠誠心と隠蔽性をバランスをとるために苦労します。この論文では、RAGLeak(RAGLeak)は、サプライチェーンプライバシーバックドア攻撃で、直接のテキスト曝露

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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