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

# ExfilBench

**Provenance taint-tracking that stops encoded, split, and trickled PHI exfiltration where content DLP sees nothing.**

ExfilBench is the benchmark corpus for *PHI Egress Control and Covert-Channel Defense for Clinical AI Agents*, the `qudam` module of the QUOKKAGUARD program. It ships with the [`qudam`](https://github.com/quome-cloud/qudam) repository, which contains the `qfire` gateway layer under test, the experiment harness, and the paper.

200 deterministic (seed 42), fully synthetic clinical sessions with labeled PHI provenance: 140 exfiltration sessions across X1 direct (20), X2 encoded (60), X3 covert/split (20), X4 trickle (20), X5 aggregation (20), plus 60 legitimate sessions.

**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 |
|---|---|---|
| `exfilbench/sessions.jsonl` | 125 KB | 200 rows |

### Record schemas

- `exfilbench/sessions.jsonl`: `id`, `attack_type`, `inbound`, `outbound`, `calls`, `leaked_provenance`, `encoding`, `legit`

## How it was generated

The corpus is produced by the generator in the paper repository.

## Intended use

Evaluating the **egress** enforcement layer of a clinical-agent security gateway (HAARF control C3.6): A policy layer decides which tools an agent may call, not what data leaves through them, so a compromised agent can smuggle PHI into outbound tool arguments verbatim, base64/hex/rot13-encoded, split across fields, trickled below per-call thresholds, or recombined to re-identify.

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

```bash
git clone https://github.com/quome-cloud/qudam
cd qudam
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@unpublished{schwoebel2026qudam,
  author = {Schwoebel, James},
  title  = {PHI Egress Control and Covert-Channel Defense for Clinical AI Agents},
  note   = {Preprint. Quome, QUOKKAGUARD program (qudam module)},
  year   = {2026},
  url    = {https://github.com/quome-cloud/qudam}
}

@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.