Datasets:

tavialiu
/

CAD-data

license: cc-by-4.0
language:
  - en
pretty_name: CAD Data
task_categories:
  - text-classification
tags:
  - prompt-injection
  - indirect-prompt-injection
  - agent-security
  - agentdojo
size_categories:
  - 1K<n<10K

CAD Data

Sentence-annotated indirect prompt injection data used to train and evaluate CAD, from Robust Context-Aware Detection of Malicious Instructions in Text (Liu, Ma, and Vorobeychik, 2026).

Code: https://github.com/tavia-liu/CAD · Models: https://huggingface.co/tavialiu/CAD-models

Each record pairs a user query with a tool-returned document into which an injection task has been inserted. Records are built from the AgentDojo banking, slack, and travel suites.

File	Description	Records
`training_data_original.tar.gz`	Training set	1,080
`testing_data_original.tar.gz`	Test set (AgentDojo v1.2.2)	389
`training_data_paraphrased.tar.gz`	Paraphrased injections of the training set, used for LLM-paraphrase adversarial training	3,376

Each archive extracts to a folder of the same name containing one JSON file per record.

Fields

Field	Description
`user`	User query given to the agent
`data`	Clean tool-returned document
`injected`	Document with the injection inserted (original train and test)
`injection`	Injected instruction text
`user_task_id`, `injection_task_id`	AgentDojo task identifiers
`sentence_segments`	The attacked document split into numbered sentence units (`[i] ...`)
`injection_span_unit`	Inclusive `start`/`end` indices of the injected units
`injection_span_char`	Character span of the injection (test set)
`total_units`	Number of sentence units
`suite_name`	Suite name (test set)
`style`	Paraphrase style (paraphrased set)
`source_file`	Original training record the paraphrase was derived from (paraphrased set)

Sentence-level labels: a unit i is malicious if start <= i <= end in injection_span_unit, and benign otherwise.

Paraphrase styles: authority_citation, code_comment, conditional_note, error_recovery, fake_dialogue, fluent_prose, step_by_step. Each paraphrased record keeps the user query and clean data of its source record and replaces only the injection text.

Usage

From the root of the CAD repository:

hf download tavialiu/CAD-data --repo-type dataset --local-dir data
for f in data/*.tar.gz; do tar -xzf "$f" -C data; done

Utility and attack success

Records are static. Task utility and attack success are measured by running the agent in AgentDojo, whose per-task utility and security checks serve as the oracles. The benchmark code used in the paper is included in the CAD repository under third_party/AutoDojo/.

License

Released under CC BY 4.0. Documents, user tasks, and injection tasks are derived from AgentDojo (MIT). Please also cite AgentDojo when using this data.

Citation

@article{liu2026cad,
  title   = {Robust Context-Aware Detection of Malicious Instructions in Text},
  author  = {Liu, Buzhao and Ma, Xinhang and Vorobeychik, Yevgeniy},
  journal = {arXiv preprint},
  year    = {2026}
}