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yimaoren/DT-test

published by @yimaoren

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
188
Last 30 days
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Likes
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Size
88.0 MB
Created on HF
2026-05-07
Age
5 months ago

Description

DecodingTrust-Agent Platform

This dataset contains agent evaluation tasks (both benign and malicious / red-teaming) spanning 14 domains: browser, code, crm, customer-service, finance, legal, macos, medical, os-filesystem, research, telecom, travel, windows, and workflow.

Each record represents a single task case.

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from datasets import load_dataset

ds_benign = load_dataset("DecodingTrust-Agent/DecodingTrust-Agent", "browser", split="benign")

ds_malicious =… See the full description on the dataset page: https://huggingface.co/datasets/yimaoren/DT-test.

Tags

task_categories:text-generationlanguage:enlicense:apache-2.0region:us

README current version from Hugging Face


license: apache-2.0
task_categories:

  • text-generation
    language:
  • en
    configs:
  • config_name: browser
    data_files:
    • split: benign
      path: data/browser/benign.json
    • split: malicious
      path: data/browser/malicious.json
  • config_name: code
    data_files:
    • split: benign
      path: data/code/benign.json
    • split: malicious
      path: data/code/malicious.json
  • config_name: crm
    data_files:
    • split: benign
      path: data/crm/benign.json
    • split: malicious
      path: data/crm/malicious.json
  • config_name: customer-service
    data_files:
    • split: benign
      path: data/customer-service/benign.json
    • split: malicious
      path: data/customer-service/malicious.json
  • config_name: finance
    data_files:
    • split: benign
      path: data/finance/benign.json
    • split: malicious
      path: data/finance/malicious.json
  • config_name: legal
    data_files:
    • split: benign
      path: data/legal/benign.json
    • split: malicious
      path: data/legal/malicious.json
  • config_name: macos
    data_files:
    • split: benign
      path: data/macos/benign.json
    • split: malicious
      path: data/macos/malicious.json
  • config_name: medical
    data_files:
    • split: benign
      path: data/medical/benign.json
    • split: malicious
      path: data/medical/malicious.json
  • config_name: os-filesystem
    data_files:
    • split: benign
      path: data/os-filesystem/benign.json
    • split: malicious
      path: data/os-filesystem/malicious.json
  • config_name: research
    data_files:
    • split: benign
      path: data/research/benign.json
    • split: malicious
      path: data/research/malicious.json
  • config_name: telecom
    data_files:
    • split: benign
      path: data/telecom/benign.json
    • split: malicious
      path: data/telecom/malicious.json
  • config_name: travel
    data_files:
    • split: benign
      path: data/travel/benign.json
    • split: malicious
      path: data/travel/malicious.json
  • config_name: windows
    data_files:
    • split: benign
      path: data/windows/benign.json
    • split: malicious
      path: data/windows/malicious.json
  • config_name: workflow
    data_files:
    • split: benign
      path: data/workflow/benign.json
    • split: malicious
      path: data/workflow/malicious.json

DecodingTrust-Agent Platform

This dataset contains agent evaluation tasks (both benign and malicious / red-teaming) spanning 14 domains: browser, code, crm, customer-service, finance, legal, macos, medical, os-filesystem, research, telecom, travel, windows, and workflow.

Each record represents a single task case.

Loading

from datasets import load_dataset

ds_benign = load_dataset("DecodingTrust-Agent/DecodingTrust-Agent", "browser", split="benign")
ds_malicious = load_dataset("DecodingTrust-Agent/DecodingTrust-Agent", "browser", split="malicious")

Splits

  • benign — standard task evaluation under benign conditions.
  • malicious — adversarial / red-teaming tasks, covering both direct and indirect threat models.

Schema (per record)

Field Type Description
task_id string Task identifier
domain string One of the 14 domains
split string benign or malicious
threat_model string | null direct / indirect (malicious only)
risk_category string | null e.g. browser-integrity (malicious only)
case_id string Identifier of the case directory within the split (e.g. 1 or file_type_conversion/3)
task_instruction string | null Task instruction text
system_prompt string | null Agent system prompt
mcp_servers list | null MCP server configuration
attack object | null Attack specification (malicious only)
redteaming_agent object | null Red-teaming agent specification (malicious only)
judge_py string | null Full raw contents of judge.py
setup_sh string | null Full raw contents of setup.sh
metadata_files object Inlined small metadata files, mapping each filename to its parsed JSON content or raw text
metadata_files_external object Pointers for metadata files that exceed the inline size threshold, mapping each filename to a relative path

See STATS.md for per-(domain, split) counts.

README history 8 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

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  5. 2026-05-07Upload 28 files87d542d0 B
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  8. 2026-05-07initial commit6eaae490 B
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Metadata

License
apache-2.0

Repository

Download the dataset

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Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("yimaoren/DT-test")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="yimaoren/DT-test", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/yimaoren/DT-test
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