E-MIA: Exam-Style Black-Box Membership Inference Attacks against RAG Systems
Retrieval-Augmented Generation (RAG) equips large language models (LLMs) with external evidence by retrieving documents at inference time, but it also turns the retrieval corpusinto a sensitive asset. Under a black-box setting, an adversary given a candidate document can infer whether it has been ingested into the RAG knowledge base (i.e., document-level membership inference) solely from query response interactions, thereby leaking corpus coverage and the existence of sensitive topics. Existing RAG MIA methods either rely on soft signals such as semantic similarity, which often yield overlapping member/non-member score distributions and unstable thresholds, or employ explicit confirmation...
| Authors | Zelin Guan, Shengda Zhuo, Zeyan Li et al. |
|---|---|
| Primary category | cs.CR |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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