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tamperbench

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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
seq: int64
ts: string
kind: string
body: struct<action: string, decision: string, detail: string, event_id: string, format_version: int64, mo (... 50 chars omitted)
  child 0, action: string
  child 1, decision: string
  child 2, detail: string
  child 3, event_id: string
  child 4, format_version: int64
  child 5, mode: string
  child 6, pubkey: string
  child 7, qfire_version: string
prev_hash: string
this_hash: string
sig: string
merkle_root: string
to
{'seq': Value('int64'), 'merkle_root': Value('string'), 'ts': Value('string'), 'sig': 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
              seq: int64
              ts: string
              kind: string
              body: struct<action: string, decision: string, detail: string, event_id: string, format_version: int64, mo (... 50 chars omitted)
                child 0, action: string
                child 1, decision: string
                child 2, detail: string
                child 3, event_id: string
                child 4, format_version: int64
                child 5, mode: string
                child 6, pubkey: string
                child 7, qfire_version: string
              prev_hash: string
              this_hash: string
              sig: string
              merkle_root: string
              to
              {'seq': Value('int64'), 'merkle_root': Value('string'), 'ts': Value('string'), 'sig': Value('string')}
              because column names don't match

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TamperBench

A blake3-chained, ed25519-signed, externally anchored audit ledger for clinical AI agents.

TamperBench is the benchmark corpus for Tamper-Evident Action Provenance for Confidential Clinical AI Agents, the quledger module of the QUOKKAGUARD program. It ships with the quledger repository, which contains the qfire gateway layer under test, the experiment harness, and the paper.

Deterministic (seed 42), fully synthetic clinical action streams plus mutated chained audit logs spanning six audit-tampering classes (TA1 field edit, TA2 delete and truncate, TA3 reorder, TA4 forge, TA6 rollback) with labels.json ground truth; the E1 corpus is 26 tampered logs derived from a 200-entry signed and anchored fixture.

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
FORMAT.md 2 KB
fixture/anchors.jsonl 8 KB 30 rows
fixture/audit.jsonl 1 MB 3,001 rows
labels.json 3 KB
streams/stream-0.jsonl 202 KB 1,000 rows
streams/stream-1.jsonl 201 KB 1,000 rows
streams/stream-2.jsonl 202 KB 1,000 rows
streams/stream-3.jsonl 202 KB 1,000 rows
streams/stream-4.jsonl 201 KB 1,000 rows
streams/stream-5.jsonl 202 KB 1,000 rows
streams/stream-6.jsonl 202 KB 1,000 rows
streams/stream-7.jsonl 202 KB 1,000 rows
streams/stream-8.jsonl 202 KB 1,000 rows
streams/stream-9.jsonl 202 KB 1,000 rows
tampered/ta1_field_edit-0.jsonl 1 MB 3,001 rows
tampered/ta1_field_edit-1.jsonl 1 MB 3,001 rows
tampered/ta1_field_edit-2.jsonl 1 MB 3,001 rows
tampered/ta1_field_edit-3.jsonl 1 MB 3,001 rows
tampered/ta1_field_edit-4.jsonl 1 MB 3,001 rows
tampered/ta2_delete-0.jsonl 1 MB 3,000 rows
tampered/ta2_delete-1.jsonl 1 MB 3,000 rows
tampered/ta2_delete-2.jsonl 1 MB 3,000 rows
tampered/ta2_delete-3.jsonl 1 MB 3,000 rows
tampered/ta2_delete-4.jsonl 1 MB 3,000 rows
tampered/ta2_truncate-0.jsonl 163 KB 358 rows
tampered/ta2_truncate-1.jsonl 1 MB 2,420 rows
tampered/ta2_truncate-2.jsonl 788 KB 1,730 rows
tampered/ta2_truncate-3.jsonl 60 KB 132 rows
tampered/ta2_truncate-4.jsonl 56 KB 124 rows
tampered/ta3_reorder-0.jsonl 1 MB 3,001 rows
tampered/ta3_reorder-1.jsonl 1 MB 3,001 rows
tampered/ta3_reorder-2.jsonl 1 MB 3,001 rows
tampered/ta3_reorder-3.jsonl 1 MB 3,001 rows
tampered/ta3_reorder-4.jsonl 1 MB 3,001 rows
tampered/ta4_forge-0.jsonl 1 MB 3,002 rows
tampered/ta4_forge-1.jsonl 1 MB 3,002 rows
tampered/ta4_forge-2.jsonl 1 MB 3,002 rows
tampered/ta4_forge-3.jsonl 1 MB 3,002 rows
tampered/ta4_forge-4.jsonl 1 MB 3,002 rows
tampered/ta6_rollback-0.anchors.jsonl 8 KB 30 rows
tampered/ta6_rollback-0.jsonl 683 KB 1,500 rows

Record schemas

  • fixture/anchors.jsonl: seq, merkle_root, ts, sig
  • fixture/audit.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • streams/stream-0.jsonl: action, actor, decision, detail, event_id, patient_ref, stream, ts_epoch, unit
  • streams/stream-1.jsonl: action, actor, decision, detail, event_id, patient_ref, stream, ts_epoch, unit
  • streams/stream-2.jsonl: action, actor, decision, detail, event_id, patient_ref, stream, ts_epoch, unit
  • streams/stream-3.jsonl: action, actor, decision, detail, event_id, patient_ref, stream, ts_epoch, unit
  • streams/stream-4.jsonl: action, actor, decision, detail, event_id, patient_ref, stream, ts_epoch, unit
  • streams/stream-5.jsonl: action, actor, decision, detail, event_id, patient_ref, stream, ts_epoch, unit
  • streams/stream-6.jsonl: action, actor, decision, detail, event_id, patient_ref, stream, ts_epoch, unit
  • streams/stream-7.jsonl: action, actor, decision, detail, event_id, patient_ref, stream, ts_epoch, unit
  • streams/stream-8.jsonl: action, actor, decision, detail, event_id, patient_ref, stream, ts_epoch, unit
  • streams/stream-9.jsonl: action, actor, decision, detail, event_id, patient_ref, stream, ts_epoch, unit
  • tampered/ta1_field_edit-0.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta1_field_edit-1.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta1_field_edit-2.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta1_field_edit-3.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta1_field_edit-4.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta2_delete-0.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta2_delete-1.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta2_delete-2.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta2_delete-3.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta2_delete-4.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta2_truncate-0.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta2_truncate-1.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta2_truncate-2.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta2_truncate-3.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta2_truncate-4.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta3_reorder-0.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta3_reorder-1.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta3_reorder-2.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta3_reorder-3.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta3_reorder-4.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta4_forge-0.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta4_forge-1.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta4_forge-2.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta4_forge-3.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta4_forge-4.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig
  • tampered/ta6_rollback-0.anchors.jsonl: seq, merkle_root, ts, sig
  • tampered/ta6_rollback-0.jsonl: seq, ts, kind, body, prev_hash, this_hash, sig

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 quledger repository root (deterministic seeds)
cargo build --release && cargo test --release --test audit_tamper -- --ignored --exact export_fixture && python3 scripts/003-tamper-audit/tamper.py --log datasets/003-tamper-audit/fixture/audit.jsonl --anchors datasets/003-tamper-audit/fixture/anchors.jsonl --out datasets/003-tamper-audit/tampered --per-class 5 --seed 42 && python3 scripts/003-tamper-audit/gen.py --out datasets/003-tamper-audit/streams --streams 10 --events 1000 --seed 42

Intended use

Evaluating the audit enforcement layer of a clinical-agent security gateway (HAARF control C2): An adversarial agent, a compromised host process, or a privileged operator can silently rewrite, drop, reorder, or roll back a clinical agent's action log, defeating the traceability HAARF C2 requires.

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

git clone https://github.com/quome-cloud/quledger
cd quledger
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{schwoebel2026quledger,
  author = {Schwoebel, James},
  title  = {Tamper-Evident Action Provenance for Confidential Clinical AI Agents},
  note   = {Preprint. Quome, QUOKKAGUARD program (quledger module)},
  year   = {2026},
  url    = {https://github.com/quome-cloud/quledger}
}

@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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