CtrlRAG: Black-box Document Poisoning Attacks for Retrieval-Augmented Generation of Large Language Models

Retrieval-Augmented Generation (RAG) systems enhance response credibility and traceability by displaying reference contexts, but this transparency simultaneously introduces a novel black-box attack vector. Existing document poisoning attacks, where adversaries inject malicious documents into the knowledge base to manipulate RAG outputs, rely primarily on unrealistic white-box or gray-box assumptions, limiting their practical applicability. To address this gap, we propose CtrlRAG, a two-stage black-box attack that (1) constructs malicious documents containing misinformation or emotion-inducing content and injects them into the knowledge base, and (2) iteratively optimizes them using a localization algorithm and Masked Language Model (MLM) guided on reference context feedback, ensuring their retrieval priority while preserving linguistic naturalness. With only five malicious documents per target question injected into the million-document MS MARCO dataset, CtrlRAG achieves up to 90% attack success rates on commercial LLMs (e.g., GPT-4o), a 30% improvement over optimal baselines, in both *Emotion Manipulation* and *Hallucination Amplification* tasks. Furthermore, we show that existing defenses fail to balance security and performance. To mitigate this challenge, we introduce a dynamic *Knowledge Expansion* defense strategy based on *Parametric/Non-parametric Memory Confrontation*, blocking 78% of attacks while maintaining 95.5% system accuracy. Our findings reveal critical vulnerabilities in RAG systems and provide effective defense strategies.