Datasets:

Quome
/

exfilbench

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 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:

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:

@unpublished{schwoebel2026qudam,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@unpublished{schwoebel2026haarf,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}
}