RAG-Pull: Turning Retrieval into a Code-Injection Channel via Invisible Unicode Perturbations
Enterprise RAG implementations face a novel supply chain risk where malicious actors could inject invisible Unicode characters into knowledge bases to trigger code execution.
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arXiv:2510.11195v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) increases the reliability and trustworthiness of the LLM response and reduces hallucination by eliminating the need for model retraining. It does so by adding external data into the LLM's context. We develop a new class of black-box attack, RAG-Pull, that inserts hidden UTF characters into queries or external code repositories, redirecting retrieval toward malicious code, thereby breaking the models' safety
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