---
license: apache-2.0
pretty_name: DeviceBench
language:
  - en
tags:
  - clinical-ai
  - llm-security
  - ai-agents
  - healthcare
  - haarf
  - quokkaguard
  - synthetic
size_categories:
  - n<1K
task_categories:
  - other
---

# DeviceBench

**A fail-closed safety broker for AI agents that actuate ventilators, infusion pumps, and monitors.**

DeviceBench is the benchmark corpus for *Fail Closed at the Bedside: A Device-Safety Broker for Agents that Actuate Life-Critical Medical Devices*, the `qubroker` module of the QUOKKAGUARD program. It ships with the [`qubroker`](https://github.com/quome-cloud/qubroker) repository, which contains the `qfire` gateway layer under test, the experiment harness, and the paper.

A version-pinned device invariant table (devicebench/devices.json, 014-devices-v1, embedded into the binary via include_str!) plus five deterministic JSONL corpora over ventilator, infusion pump, and cardiac monitor: 216 live-agent scenarios (V1 out-of-range actuation, V2 alarm/safety-function interference, V3 classification-integrity violation, 54 each, plus 54 matched safe cases), 120 legitimate titrations, 60 partition cases with a no-agent control, 60 degradation KPI traces with injected onset, and 40 chaos fault sequences; seed 42, no real device data.

**All data are synthetic.** No real patient data or protected health information (PHI) is included; clinical content is generated from templates with fixed seeds.

## Files

| File | Size | Rows |
|---|---|---|
| `devicebench/degradation.jsonl` | 23 KB | 60 rows |
| `devicebench/devices.json` | 2 KB | |
| `devicebench/faults.jsonl` | 43 KB | 40 rows |
| `devicebench/legit.jsonl` | 17 KB | 120 rows |
| `devicebench/partition.jsonl` | 8 KB | 60 rows |
| `devicebench/scenarios.jsonl` | 91 KB | 216 rows |

### Record schemas

- `devicebench/degradation.jsonl`: `case_id`, `device`, `onset`, `kpi_trace`
- `devicebench/faults.jsonl`: `case_id`, `device`, `sequence`
- `devicebench/legit.jsonl`: `case_id`, `device`, `risk_class`, `param`, `value`, `patient_kg`
- `devicebench/partition.jsonl`: `case_id`, `device`, `last_cmd`, `no_agent_harm`
- `devicebench/scenarios.jsonl`: `case_id`, `device`, `vuln`, `prompt`, `proposed_param`, `pressured_value`, `safe_value`, `patient_kg`, `ground_truth_unsafe`

## How it was generated

The corpus is produced by the generator in the paper repository and is fully deterministic (fixed seeds), so it can be regenerated byte-for-byte.

```bash
# from the qubroker repository root (deterministic seeds)
python3 scripts/014-device-broker/gen_devicebench.py
```

## Intended use

Evaluating the **device** enforcement layer of a clinical-agent security gateway (HAARF control C8.3/8.6/3.1): When an AI agent actuates a ventilator, infusion pump, or cardiac monitor, a wrong action is immediate physical harm, and a compliant model under clinical pressure will set whatever value it is told to.

The experiments that consume it (E-series in the paper) are reproduced from the repository:

```bash
git clone https://github.com/quome-cloud/qubroker
cd qubroker
cargo build --release
```

then follow the *Reproduce the experiments* section of its README.

## Citation

This benchmark was built to evaluate a control of the **Healthcare AI Agents Regulatory Framework (HAARF)**, the source framework for the QUOKKAGUARD program. Please cite both the paper and HAARF:

```bibtex
@unpublished{schwoebel2026qubroker,
  author = {Schwoebel, Jim and {QUOKKAGUARD Contributors}},
  title  = {Fail Closed at the Bedside: A Device-Safety Broker for Agents that Actuate Life-Critical Medical Devices},
  note   = {Preprint. Quome, QUOKKAGUARD program (qubroker module)},
  year   = {2026},
  url    = {https://github.com/quome-cloud/qubroker}
}

@unpublished{schwoebel2026haarf,
  author = {Schwoebel, Jim and Frasch, Martin and Spalding, Art and Sewell, Ed and Englert, Phil and Halpert, Ben and Overbay, Collin and Semenec, Ingrida and Shor, Joel},
  title  = {{HAARF}: Healthcare {AI} agents regulatory framework --- a comprehensive security verification standard for autonomous {AI} systems in clinical environments},
  note   = {medRxiv Preprint},
  year   = {2026},
  month  = {April},
  doi    = {10.64898/2026.04.09.26350519},
  url    = {https://www.medrxiv.org/content/10.64898/2026.04.09.26350519v1}
}
```

## License

Apache License 2.0. Copyright (c) 2026 Quome, Inc.