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Security2026-06-25 · source-backed
The folk assumption has been that tabular FMs are low-privacy-risk because they don't train on your real rows. arXiv 2606.26021 (cs.CR) breaks that, showing attention layers expose privacy vulnerabilities and proposing protection for high-risk queries. As agents start calling tabular FMs over enterprise data, this is a governance problem you have to weigh before treating "synthetic-pretrained" as a free pass. If you're putting customer tables behind one of these models, the threat model isn't just the training set, it's what the live model coughs up at inference.
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Reported by the same outlet (arxiv.org); overlapping topics (agent, assumption, break); pushes against this story (against).
Reported by the same outlet (arxiv.org); overlapping topics (agent, calling, model); pushes against this story (against).
Reported by the same outlet (arxiv.org); overlapping topics (agent, data, model); pushes against this story (but).
Reported by the same outlet (arxiv.org); overlapping topics (agent, data, model); pushes against this story (against).
Reported by the same outlet (arxiv.org); overlapping topics (agent, even, model); pushes against this story (but).
Reported by the same outlet (arxiv.org); overlapping topics (been, behind, model); pushes against this story (but).
Reported by the same outlet (arxiv.org); overlapping topics (agent, data, enterprise); pushes against this story (but).
Same source domain / Shared topic
Reported by the same outlet (arxiv.org); overlapping topics (attention, data, layer, model).