Characterizing and Defending Utility-Hijacking in Instruction-Tuning Data Selection
Researchers demonstrate 'utility-hijacking' attacks on instruction-tuning data selectors, where adversaries manipulate which training records are chosen to covertly shift fine-tuned model behaviour.
Summary written by editorial AI · Source link below
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
Editorial Analysis
Enterprises fine-tuning LLMs with automated data selection face a new manipulation vector; poisoned selection criteria can silently degrade model alignment.
Audit instruction-data selection pipelines for manipulation resistance before using them in production fine-tuning workflows.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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