Micro-Collaborative Poisoning: A Distributed Attack on RAG Systems

原文：Micro-Collaborative Poisoning: A Distributed Attack on RAG Systems

作者：Pedro Pereira, Eva Maia, Isabel Praça

来源：arXiv cs.AI（人工智能）

正文

Computer Science > Cryptography and Security

arXiv:2609.21573v1 (cs)

[Submitted on 18 Sep 2026]

Title:Micro-Collaborative Poisoning: A Distributed Attack on RAG Systems

Authors:Pedro Pereira, Eva Maia, Isabel Praça

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Abstract:Retrieval-Augmented Generation (RAG) improves large language models by grounding outputs in external knowledge sources, but this dependency also creates a surface for poisoning attacks. This paper introduces Micro-Collaborative Poisoning, a distributed attack in which a false target claim is divided across multiple locally plausible documents instead of being concentrated in a single malicious passage. We evaluate the attack across 108 RAG configurations by varying dataset, retriever architecture, retrieval depth, database composition, number of poisoned databases, and generator model. The results indicate that Micro-Collaborative Poisoning is not driven by a single dominant poisoned passage, but by the accumulation of weak adversarial signals across retrieved sources. Increasing top-$k$ and poisoning multiple databases make it more likely that these signals will appear together in the retrieved context, while clean database diversity and stronger retrievers can reduce their influence. The document-level poisoning visibility analysis further shows that this threat is difficult to expose through isolated document inspection, since Micro-Collaborative Poisoning achieves downstream influence while leaving a weaker explicit poisoning signature than direct poisoning.

Comments:

19 pages, 3 images, 4 tables, conference: 31st European Symposium on Research in Computer Security (ESORICS) 2026, Workshop: 2nd Workshop on the Use of Large Language Models for Cybersecurity

Subjects:

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

Cite as:

arXiv:2609.21573 [cs.CR]

(or

arXiv:2609.21573v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2609.21573

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arXiv-issued DOI via DataCite (pending registration)

主题

安全 · 人工智能

由「前沿雷达」于 2026-09-18 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。