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supplychainbench

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Tags:
clinical-ai
llm-security
ai-agents
healthcare
haarf
quokkaguard
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
file: string
class: string
where: struct<component: string, version: string>
  child 0, component: string
  child 1, version: string
metadata: struct<component: struct<type: string, name: string>, properties: list<item: struct<name: string, va (... 14 chars omitted)
  child 0, component: struct<type: string, name: string>
      child 0, type: string
      child 1, name: string
  child 1, properties: list<item: struct<name: string, value: string>>
      child 0, item: struct<name: string, value: string>
          child 0, name: string
          child 1, value: string
bomFormat: string
specVersion: string
components: list<item: struct<type: string, name: string, version: string, properties: list<item: struct<name: s (... 82 chars omitted)
  child 0, item: struct<type: string, name: string, version: string, properties: list<item: struct<name: string, valu (... 70 chars omitted)
      child 0, type: string
      child 1, name: string
      child 2, version: string
      child 3, properties: list<item: struct<name: string, value: string>>
          child 0, item: struct<name: string, value: string>
              child 0, name: string
              child 1, value: string
      child 4, hashes: list<item: struct<alg: string, content: string>>
          child 0, item: struct<alg: string, content: string>
              child 0, alg: string
              child 1, content: string
to
{'bomFormat': Value('string'), 'specVersion': Value('string'), 'metadata': {'component': {'type': Value('string'), 'name': Value('string')}, 'properties': List({'name': Value('string'), 'value': Value('string')})}, 'components': List({'type': Value('string'), 'name': Value('string'), 'version': Value('string'), 'properties': List({'name': Value('string'), 'value': Value('string')}), 'hashes': List({'alg': Value('string'), 'content': Value('string')})})}
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
              file: string
              class: string
              where: struct<component: string, version: string>
                child 0, component: string
                child 1, version: string
              metadata: struct<component: struct<type: string, name: string>, properties: list<item: struct<name: string, va (... 14 chars omitted)
                child 0, component: struct<type: string, name: string>
                    child 0, type: string
                    child 1, name: string
                child 1, properties: list<item: struct<name: string, value: string>>
                    child 0, item: struct<name: string, value: string>
                        child 0, name: string
                        child 1, value: string
              bomFormat: string
              specVersion: string
              components: list<item: struct<type: string, name: string, version: string, properties: list<item: struct<name: s (... 82 chars omitted)
                child 0, item: struct<type: string, name: string, version: string, properties: list<item: struct<name: string, valu (... 70 chars omitted)
                    child 0, type: string
                    child 1, name: string
                    child 2, version: string
                    child 3, properties: list<item: struct<name: string, value: string>>
                        child 0, item: struct<name: string, value: string>
                            child 0, name: string
                            child 1, value: string
                    child 4, hashes: list<item: struct<alg: string, content: string>>
                        child 0, item: struct<alg: string, content: string>
                            child 0, alg: string
                            child 1, content: string
              to
              {'bomFormat': Value('string'), 'specVersion': Value('string'), 'metadata': {'component': {'type': Value('string'), 'name': Value('string')}, 'properties': List({'name': Value('string'), 'value': Value('string')})}, 'components': List({'type': Value('string'), 'name': Value('string'), 'version': Value('string'), 'properties': List({'name': Value('string'), 'value': Value('string')}), 'hashes': List({'alg': Value('string'), 'content': Value('string')})})}
              because column names don't match

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SupplyChainBench

A signed CycloneDX AI bill of materials and fail-closed admission gate for clinical AI agents.

SupplyChainBench is the benchmark corpus for AI Supply-Chain Integrity for Confidential Clinical Agents: AIBOM Attestation and the Provenance Gap, the qubom module of the QUOKKAGUARD program. It ships with the qubom repository, which contains the qfire gateway layer under test, the experiment harness, and the paper.

A clean CycloneDX 1.5 clinical-agent-profile AIBOM plus 15 labeled tampered copies spanning five supply-chain attack classes (S1 weight tamper, S2 known-vulnerable dependency, S3 typosquat, S4 stripped provenance, S5 data tamper) with labels.json ground truth; fully synthetic, seed 42, paired with the pinned OSV snapshot in scripts/004-supply-chain/osv_snapshot.json.

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
PROFILE.md 1 KB
attacks/s1_weight_tamper-0.json 7 KB
attacks/s1_weight_tamper-1.json 7 KB
attacks/s1_weight_tamper-2.json 7 KB
attacks/s2_dependency_cve-0.json 7 KB
attacks/s2_dependency_cve-1.json 7 KB
attacks/s2_dependency_cve-2.json 7 KB
attacks/s3_typosquat-0.json 7 KB
attacks/s3_typosquat-1.json 7 KB
attacks/s3_typosquat-2.json 7 KB
attacks/s4_provenance_gap-0.json 6 KB
attacks/s4_provenance_gap-1.json 6 KB
attacks/s4_provenance_gap-2.json 6 KB
attacks/s5_data_tamper-0.json 7 KB
attacks/s5_data_tamper-1.json 7 KB
attacks/s5_data_tamper-2.json 7 KB
labels.json 2 KB
manifests/clean.json 7 KB

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 qubom repository root (deterministic seeds)
python3 scripts/004-supply-chain/gen.py --out datasets/004-supply-chain --per-class 3 --seed 42

Intended use

Evaluating the admission enforcement layer of a clinical-agent security gateway (HAARF control C3): A clinical AI agent is assembled from base weights, fine-tunes, prompts, rules, detector models, hundreds of library dependencies, tool servers, and reference data, any of which can be backdoored, typosquatted, or left with no verifiable origin before the agent ever runs; weight attestation and the runtime firewall check none of this.

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

git clone https://github.com/quome-cloud/qubom
cd qubom
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{schwoebel2026qubom,
  author = {Schwoebel, James},
  title  = {AI Supply-Chain Integrity for Confidential Clinical Agents: AIBOM Attestation and the Provenance Gap},
  note   = {Preprint. Quome, QUOKKAGUARD program (qubom module)},
  year   = {2026},
  url    = {https://github.com/quome-cloud/qubom}
}

@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.
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