SafetyFlow: An Agent-Flow System for Automated LLM Safety Benchmarking

ICLR 2026 Conference Submission239 Authors

Keywords: LLM Safety, Benchmark, Agent

Abstract: The rapid proliferation of large language models (LLMs) has intensified the requirement for reliable safety evaluation to uncover model vulnerabilities. To this end, numerous LLM safety evaluation benchmarks are proposed. However, existing benchmarks generally rely on labor-intensive manual curation, exhibit significant redundancy and limited difficulty, and struggle with scalability and efficiency. To alleviate these problems, we introduce SafetyFlow, the first agent-flow system designed to automate the construction of LLM safety benchmarks. SafetyFlow can automatically build a comprehensive safety benchmark in only four days without any human intervention by orchestrating seven specialized agents, significantly reducing time and resource cost. Equipped with versatile tools, the agents of SafetyFlow ensure process and cost controllability while integrating human expertise into the automatic pipeline. The final produced dataset, SafetyFlowBench, contains 23,446 queries with strong safety discrimination and low redundancy. SafetyFlow's contributions include the first fully automated benchmarking pipeline and a comprehensive safety benchmark. We evaluate the safety of 49 advanced LLMs on our dataset and conduct extensive experiments to validate our efficacy and efficiency.

Primary Area: datasets and benchmarks

Submission Number: 239