| --- |
| 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. |
|
|
| ## Contents |
|
|
| | 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: |
|
|
| ```bash |
| 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](https://github.com/ethz-spylab/agentdojo) (MIT). Please also cite AgentDojo when |
| using this data. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
