Tim Beyer, Jonas Dornbusch, Jakob Steimle, Moritz Ladenburger, Leo Schwinn, Stephan Günnemann
AdversariaLLM is a comprehensive toolbox designed to improve the reproducibility and comparability of research on the robustness of Large Language Models (LLMs).
Research on the safety and robustness of Large Language Models (LLMs) has been growing rapidly, but the field is currently fragmented with inconsistent methods and tools. This makes it difficult for researchers to reproduce and compare results across studies. AdversariaLLM is a new toolbox that addresses these issues by providing a unified platform for conducting research on LLM robustness. It includes a variety of attack algorithms, benchmark datasets, and access to different LLMs, ensuring that research can be conducted in a consistent and reproducible manner.
