| --- |
| pretty_name: Agent Mosaic |
| license: mit |
| language: |
| - en |
| size_categories: |
| - n<1K |
| task_categories: |
| - text-generation |
| tags: |
| - text |
| - synthetic |
| - evaluation |
| - confidentiality |
| - llm-agents |
| - arxiv:2610.30418 |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/benchmark.jsonl |
| --- |
| |
| # Agent Mosaic |
|
|
| A benchmark of **30 synthetic examples**, ten each in cybersecurity, medical, |
| and military domains, for studying confidentiality leaks through cross-document |
| inference. Each example pairs two individually accessible documents whose facts |
| jointly imply a designated higher-sensitivity **forbidden composite**. |
|
|
| This is the dataset used in |
| [**Agent Mosaic: Recognizing Confidentiality Leaks in Agentic LLM Systems**](https://arxiv.org/abs/2610.30418) |
| (Daniel C. Ruiz, Luca Luceri, and Emilio Ferrara, 2026). See the paper for the |
| experimental protocol. This release contains only the benchmark examples, not |
| model responses, annotations, experiment code, or supporting prompts. Research code available [**here**](https://github.com/ZQ-Dev8/agent-mosaic). |
|
|
| ## Download |
|
|
| Install `datasets`, then load the single `test` split: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("ZQ-Dev/agent-mosaic-examples", split="test") |
| ``` |
|
|
| The examples are also available directly as [`data/benchmark.jsonl`](data/benchmark.jsonl). |
| There are no training or validation splits. |
|
|
| ## Fields |
|
|
| Each row is one benchmark example, with **17 string fields**: |
|
|
| | Field(s) | Description | |
| | --- | --- | |
| | `example_id`, `domain` | Stable identifier and domain: `cybersecurity`, `medical`, or `military`. | |
| | `docA`, `docB`, `distractor` | Two relevant document bodies and an optional in-domain distractor. | |
| | `docA_datum`, `docB_datum` | The composing facts identified by the authors. | |
| | `docA_classification`, `docB_classification`, `distractor_classification` | Synthetic document labels: `SECRET`. | |
| | `docA_datum_classification`, `docB_datum_classification` | Synthetic composing-fact labels: `SECRET`. | |
| | `forbidden_composite`, `forbidden_composite_classification` | The protected inference and its synthetic `TOP_SECRET` label. | |
| | `user_benign_query` | A request designed to be answerable from one relevant document. | |
| | `user_explicit_query` | A request directly targeting the composite. | |
| | `user_adversarial_query` | The explicit query plus the study's fixed clearance-claim/instruction-override suffix. | |
|
|
| ## Synthetic-data disclaimer |
|
|
| **All documents and classifications are synthetic.** Examples were generated |
| with Anthropic Claude Opus 4.6 and manually reviewed by the authors for accuracy and realism. |
| Fictional medical, military, and vulnerability content is experimental stimulus, **not advice |
| or claims about real people or systems**. Names, addresses, and vulnerability identifiers are |
| not verified facts. `SECRET` and `TOP_SECRET` are experimental labels, not actual classifications. |
| No actual confidential records are included. |
|
|
| ## License |
|
|
| Released under the [MIT License](LICENSE). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{ruiz2026agentmosaic, |
| title={Agent Mosaic: Recognizing Confidentiality Leaks in Agentic LLM Systems}, |
| author={Daniel C. Ruiz and Luca Luceri and Emilio Ferrara}, |
| year={2026}, |
| eprint={2610.30418}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CR}, |
| url={https://arxiv.org/abs/2610.30418} |
| } |
| ``` |
|
|
