pretty_name: JailFlowEval
language:

• en
  license: cc-by-4.0
  size_categories:
• n<1K
  task_categories:
• text-generation
  tags:
• datasets
• benchmark
• safety
• red-teaming
• jailbreak
• agent-security
• workflow-safety
• enterprise-workflows
  configs:
• config_name: default
  data_files:
  • split: train
    path: data/train.parquet

JailFlowEval

This repository contains the dataset subset for our paper "JailFlowEval: A Workflow-Safety Benchmark for LLM-Driven Enterprise Service Workflows".

Dataset Summary

• Rows: 744
• Unique source prompts: 373
• Split: train

Task distribution:

• plan=336
• exec=202
• review=103
• improve=103

Fields

Each row contains:

• example_id: unique row identifier in this packaged dataset
• source_id: prompt ID from the source table
• quality_tier: always high_quality
• task: one of plan, exec, review, or improve
• source: source dataset name
• cluster_label: semantic cluster label
• forbidden_prompt: original harmful or disallowed prompt
• jailbroken_prompt: rewritten situational jailbreak prompt
• response: target model response
• score: PAIR-based harmfulness score

Safety Notice

This dataset contains harmful prompts and harmful or policy-violating model outputs. It is intended for safety evaluation, red teaming research, and defense analysis. It should not be used to facilitate real-world harm.

Citation

If you use this dataset, please cite this dataset release. The formal manuscript citation can be added after publication.

@misc{liu2026jailfloweval_dataset,
  title        = {JailFlowEval: A Workflow-Safety Benchmark for LLM-Driven Enterprise Service Workflows},
  author       = {Zhichao Liu and Haining Yu and Wenbo Pan and Wenting Zhang and Bingxuan Wang and Xiang Li},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/yaojialzc/JailFlowEval}},
  note         = {Dataset release}
}