New attack vector targets graph foundation models via alignment layer

Researchers have identified a new attack vector targeting graph foundation models by exploiting their alignment layer, which maps inputs into a shared representation. This attack, performed at inference time without access to training data, can cause significant model collapse. The study found that the OpenGraph model is particularly vulnerable, collapsing at a much lower perturbation budget than other models, indicating a specific fragility in its spectral tokenizer. AI

IMPACT Identifies a novel vulnerability in graph foundation models, potentially impacting their security and reliability in real-world applications.

RANK_REASON The cluster contains a research paper detailing a new attack method on graph foundation models. [lever_c_demoted from research: ic=1 ai=1.0]

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• alignment layer
• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• graph foundation models
• Hugging Face
• Influence Flower
• ScienceCast

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