38 citations · 64 across the 23 of their papers we have counts for
3 papers · 2 filters
Pay Attention to the Triggers: Constructing Backdoors That Survive Distillation
Giovanni De Muri, Mark Vero, Robin Staab +1
LLMs are often used by downstream users as teacher models for knowledge distillation, compressing their capabilities into memory-efficient models. However, as these teacher models…
Fewer Weights, More Problems: A Practical Attack on LLM Pruning
Kazuki Egashira, Robin Staab, Thibaud Gloaguen +2
Model pruning, i.e., removing a subset of model weights, has become a prominent approach to reducing the memory footprint of large language models (LLMs) during inference. Notably,…
Watch your steps: Dormant Adversarial Behaviors that Activate upon LLM Finetuning
Thibaud Gloaguen, Mark Vero, Robin Staab +1
Finetuning open-weight Large Language Models (LLMs) is standard practice for achieving task-specific performance improvements. Until now, finetuning has been regarded as a controll…