Datasets:

Quome
/

haarf-redteam

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
id: string
arm: string
kind: string
invariant: string
magnitude: double
fail_open: bool
max_degradation: double
control: string
seed_round: int64
layer: string
payload: string
built: bool
atlas: string
expected_block: bool
lineage: null
to
{'id': Value('string'), 'control': Value('string'), 'atlas': Value('string'), 'layer': Value('string'), 'built': Value('bool'), 'payload': Value('string'), 'expected_block': Value('bool'), 'seed_round': Value('int64'), 'lineage': Value('null')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              id: string
              arm: string
              kind: string
              invariant: string
              magnitude: double
              fail_open: bool
              max_degradation: double
              control: string
              seed_round: int64
              layer: string
              payload: string
              built: bool
              atlas: string
              expected_block: bool
              lineage: null
              to
              {'id': Value('string'), 'control': Value('string'), 'atlas': Value('string'), 'layer': Value('string'), 'built': Value('bool'), 'payload': Value('string'), 'expected_block': Value('bool'), 'seed_round': Value('int64'), 'lineage': Value('null')}
              because column names don't match

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HAARF-RedTeam

Continuous red-team replay, regression gating, incident runbooks, and chaos testing for clinical AI guardrails.

HAARF-RedTeam is the benchmark corpus for Defenses That Pass Once and Rot: Continuous Red-Teaming, Incident Response, and Resilience as a Meta-Layer for Clinical AI Guardrails, the qusiege module of the QUOKKAGUARD program. It ships with the qusiege repository, which contains the qfire gateway layer under test, the experiment harness, and the paper.

A self-contained synthetic corpus (JSONL files gitignored, regenerated from the tracked generator): corpus.jsonl with 62 AttackRecords (one canonical attack per vulnerability, 5 per built layer and 4 per unbuilt layer) spanning all 8 HAARF control families and 13 target layers tagged with 14 MITRE ATLAS techniques, expected-block flags, and built/unbuilt status; incidents.jsonl with 60 decision streams (half carrying an injected attack, half benign controls); and faults.jsonl with latency, dependency-loss, and partition scenarios in a production (fail-safe) and a validation (planted-broken) arm; seed 42, no PHI.

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
`README.md`	3 KB
`corpus.jsonl`	14 KB	62 rows
`faults.jsonl`	3 KB	18 rows
`incidents.jsonl`	191 KB	60 rows

Record schemas

corpus.jsonl: id, control, atlas, layer, built, payload, expected_block, seed_round, lineage
faults.jsonl: id, arm, kind, invariant, magnitude, fail_open, max_degradation
incidents.jsonl: stream_id, is_attack, events

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.

# from the qusiege repository root (deterministic seeds)
python3 scripts/015-redteam/gen_redteam.py --seed 42

Intended use

Evaluating the redteam enforcement layer of a clinical-agent security gateway (HAARF control C3.1/3.7/3.8): A guardrail evaluated once at submission and then deployed for months rots: attack classes evolve, configuration changes silently reopen closed vulnerabilities, live attacks need containment, and dependency failures must degrade safely.

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

git clone https://github.com/quome-cloud/qusiege
cd qusiege
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{schwoebel2026qusiege,
  author = {Schwoebel, Jim and {QUOKKAGUARD Contributors}},
  title  = {Defenses That Pass Once and Rot: Continuous Red-Teaming, Incident Response, and Resilience as a Meta-Layer for Clinical AI Guardrails},
  note   = {Preprint. Quome, QUOKKAGUARD program (qusiege module)},
  year   = {2026},
  url    = {https://github.com/quome-cloud/qusiege}
}

@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

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