Datasets:

ZQ-Dev
/

agent-mosaic-examples

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 (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.

Download

Install datasets, then load the single test split:

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. 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.

Citation

@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}
}