CAREATTACK adapts closed-form parameter editing with graph-based conflict resolution and lightweight anchor repair to promote malicious passages in RAG retrieval while limiting side effects on non-target queries.
CPA-RAG: Covert poisoning attacks on retrieval- augmented generation in large language models
5 Pith papers cite this work. Polarity classification is still indexing.
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cs.CR 5years
2026 5representative citing papers
Existing corpus poisoning attacks degrade after reranking in chunked RAG pipelines due to granularity mismatch, while the proposed CRCP framework achieves higher attack success by jointly optimizing retrieval, reranker consistency, and chunk robustness.
CleanBase identifies malicious documents in RAG databases by detecting cliques in a semantic similarity graph constructed using embedding models and a statistical threshold.
RAGShield detects all numerical manipulations in government RAG systems via pattern-based value extraction and cross-source verification, achieving 0% attack success rate on 430 real IRS-derived attacks where embedding defenses miss 79-90%.
RefineRAG achieves 90% attack success on NQ by generating toxic seeds then optimizing them via retriever-in-the-loop word refinement, outperforming prior methods on effectiveness and naturalness.
citing papers explorer
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Conflict-Aware Retriever Editing for Knowledge Injection Attacks on LLM-Based RAG Systems
CAREATTACK adapts closed-form parameter editing with graph-based conflict resolution and lightweight anchor repair to promote malicious passages in RAG retrieval while limiting side effects on non-target queries.
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When Poison Fails After Retrieval: Revisiting Corpus Poisoning under Chunking and Reranking Pipelines
Existing corpus poisoning attacks degrade after reranking in chunked RAG pipelines due to granularity mismatch, while the proposed CRCP framework achieves higher attack success by jointly optimizing retrieval, reranker consistency, and chunk robustness.
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CleanBase: Detecting Malicious Documents in RAG Knowledge Databases
CleanBase identifies malicious documents in RAG databases by detecting cliques in a semantic similarity graph constructed using embedding models and a statistical threshold.
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RAGShield: Detecting Numerical Claim Manipulation in Government RAG Systems
RAGShield detects all numerical manipulations in government RAG systems via pattern-based value extraction and cross-source verification, achieving 0% attack success rate on 430 real IRS-derived attacks where embedding defenses miss 79-90%.
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RefineRAG: Word-Level Poisoning Attacks via Retriever-Guided Text Refinement
RefineRAG achieves 90% attack success on NQ by generating toxic seeds then optimizing them via retriever-in-the-loop word refinement, outperforming prior methods on effectiveness and naturalness.