SafetyFlow：用于自动化 LLM 安全基准测试的智能体流系统

Computer Science > Computation and Language arXiv:2508.15526 (cs) [Submitted on 21 Aug 2025 (v1), last revised 22 Sep 2026 (this version, v2)] Title:SafetyFlow: An Agent-Flow System for Automated LLM Safety Benchmarking Authors:Xiangyang Zhu, Yuan Tian, Chunyi Li, Kaiwei Zhang, Wei Sun, Guangtao Zhai View a PDF of the paper titled SafetyFlow: An Agent-Flow System for Automated LLM Safety Benchmarking, by Xiangyang Zhu and 5 other authors View PDF HTML (experimental) 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, which causes excessive time and resource consumption. They also exhibit significant redundancy and limited difficulty. 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 constructed dataset, SafetyFlowBench, contains 23,446 queries with low redundancy and strong discriminative power. Our contribution includes 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. Comments: Subjects: Computation and Language (cs.CL) Cite as: arXiv:2508.15526 [cs.CL] (or arXiv:2508.15526v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2508.15526 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xiangyang Zhu [view email] [v1] Thu, 21 Aug 2025 13:00:53 UTC (3,989 KB) [v2] Tue, 22 Sep 2026 09:14:59 UTC (4,016 KB) Full-text links: Access Paper: View a PDF of the paper titled SafetyFlow: An Agent-Flow System for Automated LLM Safety Benchmarking, by Xiangyang Zhu and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2025-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)