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arXiv AI By Wenda Li, Tongya Zheng, Shunyu Liu, Hanyang Yuan, Kaixuan Chen, Yu Wang, Yuchen Ying, Haoze Jiang, Huan Li, Gang Chen, Zujie Ren

S-GAP: Stealthy Graph Attack Primitives for Graph Large Language Models

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The paper introduces S‑GAP, a stealthy graph attack primitive that unifies textual and structural perturbations into a single coherent attack intent for Graph Large Language Models (GraphLLMs). By iteratively refining the semantic content of the GAP through a multi‑agent framework, S‑GAP achieves higher attack success rates and produces more relationally plausible perturbations compared to existing methods. Experiments on four datasets and three GraphLLMs demonstrate an average 7.02% improvement in attack success rate, revealing significant vulnerabilities in current GraphLLMs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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