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CleanBase method detects malicious documents in RAG knowledge databases

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed CleanBase, a novel method to identify malicious documents within retrieval-augmented generation (RAG) knowledge databases. The system leverages the high semantic similarity often found among malicious documents crafted for prompt injection attacks. CleanBase constructs a similarity graph where documents forming cliques are flagged as malicious, thereby enhancing the security and integrity of RAG systems. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Enhances RAG system security by detecting and mitigating prompt injection attacks through malicious document identification.

RANK_REASON This is a research paper detailing a new method for detecting malicious documents in RAG systems.

Read on arXiv cs.LG →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Weifei Jin, Xilong Wang, Wei Zou, Jinyuan Jia, Neil Gong ·

    CleanBase: Detecting Malicious Documents in RAG Knowledge Databases

    arXiv:2605.00460v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is vulnerable to prompt injection attacks, in which an adversary inserts malicious documents containing carefully crafted injected prompts into the knowledge database. When a user issues a ques…

  2. arXiv cs.LG TIER_1 · Neil Gong ·

    CleanBase: Detecting Malicious Documents in RAG Knowledge Databases

    Retrieval-augmented generation (RAG) is vulnerable to prompt injection attacks, in which an adversary inserts malicious documents containing carefully crafted injected prompts into the knowledge database. When a user issues a question targeted by the attack, the RAG system may re…

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