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arXiv Machine Learning By Xiaohua Zhan, Kazuki Egashira, Robin Staab, Mark Vero, Martin Vechev

All-in-One: A Composable Attack on LLM Transformations

Read the original on arXiv Machine Learning →

The paper introduces a unified attack framework that can simultaneously target multiple large language model (LLM) transformations such as quantization, pruning, and model merging. By treating each transformation as a weight perturbation, the authors jointly train a model to perform benignly before transformation and maliciously after any one of the transformations is applied. Experiments across various attack scenarios and model families show high success rates, illustrating that a single adversarial model can exploit diverse deployment-time transformations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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