Is My Data in Your Retrieval Database? Membership Inference Attacks Against Retrieval Augmented Generation
Retrieval Augmented Generation (RAG) systems have shown great promise in natural language processing. However, their reliance on data stored in a retrieval database, which may contain proprietary or sensitive information, introduces new privacy concerns. Specifically, an attacker may be able to infer whether a certain text passage appears in the retrieval database by observing the outputs of the RAG system, an attack known as a Membership Inference Attack (MIA). Despite the significance of this threat, MIAs against RAG systems have yet remained under-explored. This study addresses this gap by introducing an efficient and easy-to-use method for conducting MIA against RAG systems. We demonstrate the effectiveness of our attack using two benchmark datasets and multiple generative models, showing that the membership of a document in the retrieval database can be efficiently determined through the creation of an appropriate prompt in both black-box and gray-box settings. Moreover, we introduce an initial defense strategy based on adding instructions to the RAG template, which shows high effectiveness for some datasets and models. Our findings highlight the importance of implementing security countermeasures in deployed RAG systems and developing more advanced defenses to protect the privacy and security of retrieval databases.
Code (0)
등록된 구현이 없습니다.
Tasks
Inference AttackMembership Inference AttackRAGRetrievalRetrieval-augmented GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is a state-of-the-art technique that mitigates issues such as hallucinations and knowledge staleness in Large Language Models (LLMs) by retrieving relevant knowledge from an external …
RAGRetrievalRetrieval-augmented GenerationSemantic Similarity+1Mask-based Membership Inference Attacks for Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) has been an effective approach to mitigate hallucinations in large language models (LLMs) by incorporating up-to-date and domain-specific knowledge. Recently, there has been a trend o…
RAGRetrievalRetrieval-augmented GenerationRiddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to generate grounded responses by leveraging external knowledge databases without altering model parameters. Although the absence of weight tuning…
Membership Inference AttackRAGRetrievalRetrieval-augmented GenerationIs My Vision-Language Data in Your AI? Membership Inference Test (MINT) Demo 2
We present the Membership Inference Test (MINT) Demo 2, a framework designed to improve transparency in machine learning training processes. MINT is a technique for experimentally determining whether specific data were u…
Face RecognitionMembership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?
Data privacy is an important issue for "machine learning as a service" providers. We focus on the problem of membership inference attacks: given a data sample and black-box access to a model's API, determine whether the …
Machine TranslationTranslationVideo Captioning