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arXiv Machine Learning By Georgios Politis, Evangelos Pappas

LLM-Attack Suite: A Unified Red-Team Framework for Privacy Evaluation of Split and Distributed LLM Systems

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The paper introduces LLM‑Attack Suite, a unified framework for evaluating privacy risks in split and distributed large language models. It records attacker choices—such as message types, model weights, datasets, and recovery metrics—to enable consistent comparison and extension of privacy evaluations. Experiments on GPT‑2 demonstrate that providing gradients during training significantly boosts token and document recovery rates, illustrating how attacker access levels influence privacy outcomes.

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