Fewer Weights, More Problems: A Practical Attack on LLM Pruning

Fuente: arXiv

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Bibliographic Details
Main Authors:	Egashira, Kazuki, Staab, Robin, Gloaguen, Thibaud, Vero, Mark, Vechev, Martin
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Cryptography and Security
Online Access:	Acceder al recurso
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Internet

https://arxiv.org/abs/2510.07985