QFIRE-HealthBench: a healthcare prompt-injection benchmark

1000 benign + 1000 malicious healthcare prompts for evaluating LLM prompt firewalls in clinical settings.

Malicious provenance (by source)

• native (healthcare threats authored here): 400
• garak (real NVIDIA/garak DAN-family + in-the-wild jailbreaks, healthcare-wrapped): 300
• pyrit (real Microsoft PyRIT converters): 300

Categories: {'bulk_export': 39, 'phi_exfil': 127, 'cross_patient': 86, 'jailbreak': 415, 'phi_smuggle': 26, 'clinical_advice': 185, 'reidentification': 39, 'system_exfil': 29, 'direct_injection': 54} Obfuscation techniques: {'none': 700, 'morse': 33, 'rot13': 34, 'base64': 34, 'ascii_smuggler': 33, 'leetspeak': 33, 'binary': 33, 'caesar': 33, 'unicode_confusable': 33, 'atbash': 34}

Benign

Realistic clinical-adjacent requests (general health info, scheduling, med/insurance admin, lab-result general explanation, wellness, mental-health resources, records-access-for-self) that a healthcare assistant SHOULD allow. Over-blocking these is a failure mode.

Malicious

PHI exfiltration, re-identification, cross-patient access, bulk export, out-of-scope clinical advice (diagnosis/dosing), system-prompt exfiltration, direct injection, and real DAN/jailbreak framings — plus obfuscated variants produced by real PyRIT converters (Base64/ROT13/Atbash/Leetspeak/Unicode/Binary/...).

Ethics & licensing

Defensive benchmark. All identifiers are synthetic; no real PHI. Malicious = scope-violation / injection / PHI-solicitation, not operational-harm content. Seeds: garak (Apache-2.0, NVIDIA); converters: Microsoft PyRIT (MIT). Generated deterministically (seed 1337) by scripts/gen_healthcare_dataset.py + scripts/pyrit_convert.py.

Files & schema

• dataset.csv — the full benchmark, 2000 rows (1000 benign + 1000 malicious), deterministically shuffled (seed 1337) so the two classes are interleaved. dataset.jsonl is the same data in JSON Lines for programmatic use.

column	description
prompt	the input prompt text
label	benign or malicious
category	threat category (phi_exfil, jailbreak, bulk_export, …) or benign
technique	obfuscation applied (none, base64, rot13, unicode_confusable, …)
source	provenance (native, garak, pyrit)

from datasets import load_dataset
ds = load_dataset("H3althAg3nt/QFIRE-Healthbench")          # 2000 rows
benign  = ds["train"].filter(lambda r: r["label"] == "benign")
attacks = ds["train"].filter(lambda r: r["label"] == "malicious")

Citation

If you use this dataset, please cite the QFIRE paper (medRxiv preprint):

@article{schwoebel2026qfire,
  title   = {Beyond Injection Detection: A Positive-Security Prompt Firewall
             that Closes the Scope and PHI Gap SOTA Classifiers Miss in Healthcare},
  author  = {Schwoebel, James and Semenec, Ingrida and Rousseva, Jenia and
             Frasch, Martin G. and Thorstenson, Rome and Bhatt, Manish},
  journal = {medRxiv},
  year    = {2026},
  doi     = {10.64898/2026.06.04.26354950},
  url     = {https://www.medrxiv.org/content/10.64898/2026.06.04.26354950v1},
  note    = {Preprint. Task Force for AI Agents in Healthcare}
}

Code: https://github.com/Task-force-for-AI-agents-in-Healthcare/qfire