RedBench: A Universal Dataset for Comprehensive Red Teaming of Large Language Models
This work addresses the need for systematic and consistent evaluation of LLM vulnerabilities for researchers and developers, though it is incremental as it aggregates existing datasets rather than proposing a new method.
The paper tackles the problem of inconsistent and limited datasets for red teaming large language models by introducing RedBench, a universal dataset aggregating 37 benchmarks with 29,362 samples across standardized risk categories and domains, enabling comprehensive vulnerability assessments.
As large language models (LLMs) become integral to safety-critical applications, ensuring their robustness against adversarial prompts is paramount. However, existing red teaming datasets suffer from inconsistent risk categorizations, limited domain coverage, and outdated evaluations, hindering systematic vulnerability assessments. To address these challenges, we introduce RedBench, a universal dataset aggregating 37 benchmark datasets from leading conferences and repositories, comprising 29,362 samples across attack and refusal prompts. RedBench employs a standardized taxonomy with 22 risk categories and 19 domains, enabling consistent and comprehensive evaluations of LLM vulnerabilities. We provide a detailed analysis of existing datasets, establish baselines for modern LLMs, and open-source the dataset and evaluation code. Our contributions facilitate robust comparisons, foster future research, and promote the development of secure and reliable LLMs for real-world deployment. Code: https://github.com/knoveleng/redeval