AdversariaLLM: A Unified and Modular Toolbox for LLM Robustness Research

2025-11-06

AI Summary

Current LLM safety research suffers from fragmented implementations, inconsistent datasets and evaluation protocols, and poor reproducibility. To address these challenges, we introduce the first open-source, unified toolbox dedicated to LLM safety assessment. It features a modular architecture integrating 12 adversarial attack algorithms and 7 benchmark datasets, supports plug-and-play inference with Hugging Face models, and enables automated evaluation via JudgeZoo. The toolbox incorporates distributed evaluation, deterministic execution, computational resource monitoring, and multi-dimensional robustness assessment—including harmfulness, over-refusal, and utility—thereby substantially improving experimental reproducibility and cross-study comparability. Its core innovation lies in establishing a standardized, extensible, end-to-end security evaluation framework, providing transparent, reliable, and verifiable infrastructure for rigorous LLM safety research.

Technology Category

• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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. ame 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.

Problem

• Addressing fragmented LLM safety research ecosystem
• Ensuring reproducibility and comparability across studies
• Providing unified toolbox for jailbreak robustness evaluation

Innovation

• Unified modular toolbox for LLM robustness research
• Implements twelve adversarial attack algorithms
• Integrates seven benchmark datasets and open-weight LLMs