5 papers
One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems
Zhiyuan Chang, Mingyang Li, Xiaojun Jia +5
Large Language Models (LLMs) enhanced with Retrieval-Augmented Generation (RAG) have shown improved performance in generating accurate responses. However, the dependence on externa…
Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems
Haowei Wang, Rupeng Zhang, Junjie Wang +4
Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by retrieving relevant documents from external corpora before generating responses. This approach…
What External Knowledge is Preferred by LLMs? Characterizing and Exploring Chain of Evidence in Imperfect Context for Multi-Hop QA
Zhiyuan Chang, Mingyang Li, Xiaojun Jia +5
Incorporating external knowledge has emerged as a promising way to mitigate outdated knowledge and hallucinations in LLM. However, external knowledge is often imperfect, encompassi…
Mimicking the Familiar: Dynamic Command Generation for Information Theft Attacks in LLM Tool-Learning System
Ziyou Jiang, Mingyang Li, Guowei Yang +4
Information theft attacks pose a significant risk to Large Language Model (LLM) tool-learning systems. Adversaries can inject malicious commands through compromised tools, manipula…
From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial Injection
Haowei Wang, Rupeng Zhang, Junjie Wang +4
Tool-calling has changed Large Language Model (LLM) applications by integrating external tools, significantly enhancing their functionality across diverse tasks. However, this inte…