Micro-Collaborative Poisoning: A Distributed Attack on RAG Systems
Researchers present Micro-Collaborative Poisoning, which splits a false claim across documents to poison RAG systems.
The paper introduces Micro-Collaborative Poisoning, which divides a false target claim across several locally plausible documents instead of one malicious passage. The authors test 108 RAG configurations, varying dataset, retriever, retrieval depth, database mix, number of poisoned databases, and generator model. Success comes from accumulating weak adversarial signals, and it rises with larger top-k or several poisoned databases. Clean-database diversity and stronger retrievers reduce the effect, while isolated document checks leave a weaker signature than direct poisoning.
- False claims are split across multiple locally plausible documents.
- Evaluated across 108 RAG configurations and generator models.
- Larger top-k and multiple poisoned databases raise success.
- Isolated inspection shows a weaker signature than direct poisoning.
From the original article
1 min read at arxiv.orgRetrieval-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.
By Pedro Pereira · arxiv.org
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