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CleanBase method detects malicious documents in RAG knowledge databases

By PulseAugur Editorial · [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

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 →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

CleanBase method detects malicious documents in RAG knowledge databases

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · 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 English(EN) · 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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