Graph Foundation Models (GFMs) on text-attributed graphs (TAGs) align graph representations with language semantics to support transferable graph learning. Despite these advantages, the backdoor vulnerability of GFMs on TAGs remains insufficiently understood, especially under graph-language alignment, where graph and t...
Min-hua Lin, Zhi-Cheng Gao, Yilong Wang et al.· 0 citations
Retrieval-augmented generation (RAG) is widely used to mitigate hallucination issues in large language models (LLMs) and multimodal large language models (MLLMs). In particular, knowledge graph (KG)-based RAG leverages structured knowledge to provide (M)LLMs with high-quality external information. Building on these wor...
Zongyu Wu, Yilong Wang, Xiaochen Wang et al.· 0 citations
This survey presents a unified and pipeline-aware overview of RAG robustness, formalize threat models over the corpus, retriever, and generator, and organize attacks into three main objectives: accuracy, privacy, and fairness.
Minh Tran, Cuong Dang, Tuc Nguyen et al.· 0 citations
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