Benchmarking Poisoning Attacks against Retrieval-Augmented Generation
This work addresses a critical security problem for RAG systems in AI, highlighting vulnerabilities across various architectures, but it is incremental as it builds on prior poisoning strategies.
The paper tackled the security vulnerability of Retrieval-Augmented Generation (RAG) systems to poisoning attacks by proposing the first comprehensive benchmark framework, evaluating 13 attack methods and 7 defenses across 5 QA datasets and 10 expanded variants, finding that attacks are less effective on expanded datasets and current defenses are inadequate.
Retrieval-Augmented Generation (RAG) has proven effective in mitigating hallucinations in large language models by incorporating external knowledge during inference. However, this integration introduces new security vulnerabilities, particularly to poisoning attacks. Although prior work has explored various poisoning strategies, a thorough assessment of their practical threat to RAG systems remains missing. To address this gap, we propose the first comprehensive benchmark framework for evaluating poisoning attacks on RAG. Our benchmark covers 5 standard question answering (QA) datasets and 10 expanded variants, along with 13 poisoning attack methods and 7 defense mechanisms, representing a broad spectrum of existing techniques. Using this benchmark, we conduct a comprehensive evaluation of all included attacks and defenses across the full dataset spectrum. Our findings show that while existing attacks perform well on standard QA datasets, their effectiveness drops significantly on the expanded versions. Moreover, our results demonstrate that various advanced RAG architectures, such as sequential, branching, conditional, and loop RAG, as well as multi-turn conversational RAG, multimodal RAG systems, and RAG-based LLM agent systems, remain susceptible to poisoning attacks. Notably, current defense techniques fail to provide robust protection, underscoring the pressing need for more resilient and generalizable defense strategies.