AdversariaLLM: A Unified and Modular Toolbox for LLM Robustness Research

• URL: http://arxiv.org/abs/2511.04316v1
• Date: Thu, 06 Nov 2025 12:38:09 GMT
• Title: AdversariaLLM: A Unified and Modular Toolbox for LLM Robustness Research
• Authors: Tim Beyer, Jonas Dornbusch, Jakob Steimle, Moritz Ladenburger, Leo Schwinn, Stephan Günnemann,
• Abstract summary: AdversariaLLM is a toolbox for conducting jailbreak robustness research.<n>It implements twelve adversarial attack algorithms, integrates seven benchmark datasets spanning harmfulness, over-refusal, and utility evaluation, and provides access to a wide range of open-weight LLMs via Hugging Face.<n>name also integrates judging through the companion package JudgeZoo, which can also be used independently.
• Score: 42.296088824743684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid expansion of research on Large Language Model (LLM) safety and robustness has produced a fragmented and oftentimes buggy ecosystem of implementations, datasets, and evaluation methods. This fragmentation makes reproducibility and comparability across studies challenging, hindering meaningful progress. To address these issues, we introduce AdversariaLLM, a toolbox for conducting LLM jailbreak robustness research. Its design centers on reproducibility, correctness, and extensibility. The framework implements twelve adversarial attack algorithms, integrates seven benchmark datasets spanning harmfulness, over-refusal, and utility evaluation, and provides access to a wide range of open-weight LLMs via Hugging Face. The implementation includes advanced features for comparability and reproducibility such as compute-resource tracking, deterministic results, and distributional evaluation techniques. \name also integrates judging through the companion package JudgeZoo, which can also be used independently. Together, these components aim to establish a robust foundation for transparent, comparable, and reproducible research in LLM safety.

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