Characterizing and Defending Utility-Hijacking in Instruction-Tuning Data Selection↗ (원문, 새 창)

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arXiv:2610.29886v1 Announce Type: new Abstract: Instruction-data selectors choose which instruction-following records are used for fine-tuning, improving alignment quality, raising training efficiency, and filtering out harmful records. A selector's choices thus directly affect the fine-tuned model's safety, yet despite their widespread use, the security of these selectors remains under-studied. We conduct the first systematic study of adaptive supplier-side attacks against utility-oriented ins