README.md

---
language:
- en
license: cc-by-4.0
size_categories:
- n<1K
task_categories:
- text-generation
tags:
- cybersecurity
- evaluation
- benchmark
- false-refusal
- humanwill
pretty_name: HumanWill Cybersecurity Benchmark — initial424
configs:
- config_name: default
  data_files:
  - split: test
    path: questions.jsonl
---

# HumanWill Cybersecurity Benchmark

**Measuring harmful refusals in AI models.**

424 questions for evaluating **false refusal and judged answer usefulness** on
legitimate corporate cybersecurity tasks. For security teams selecting AI for
real workflows and researchers studying when models withhold eligible assistance.

[Framework and CLI on GitHub](https://github.com/humanwill-ai/humanwill-benchmark) · [Interactive results](https://huggingface.co/spaces/humanwill-ai/cybersecurity-benchmark) · [Full report (PDF)](https://raw.githubusercontent.com/humanwill-ai/humanwill-benchmark/main/reports/cybersecurity/v0.1.1/humanwill-cybersecurity-report-v0.1.1.pdf) · [HumanWill](https://humanwill.ai)

## What is included

| Topic | Questions |
| --- | ---: |
| Vulnerability validation | 81 |
| Source-code security | 84 |
| Binary investigation | 80 |
| Network investigation | 89 |
| Filesystem and artifact investigation | 90 |
| **Total** | **424** |

Question and delivery version **0.1.0**. The [pack/0.1.0](pack/0.1.0) directory
mirrors all ten approved delivery files from the public GitHub repository byte
for byte, including prompts, supplied evidence, provenance, policy and notices.
The top-level `questions.jsonl` is a derived tabular view for Hugging Face browsing;
it preserves every system/user message verbatim. Original pack files are canonical.
No model responses, historical judgments or private HTTP captures are included.

## Load the questions

```python
from datasets import load_dataset

questions = load_dataset("humanwill-ai/cybersecurity-initial424", split="test")
print(len(questions))  # 424
```

The `test` split identifies evaluation use. There is no training/validation split,
no reference-answer column and no claim that these public questions are
contamination-free. `expected_behavior` is a case expectation, not a model score.

To run HumanWill, follow the [reference benchmark quick start](https://github.com/humanwill-ai/humanwill-benchmark/blob/main/docs/REFERENCE_BENCHMARK.md).
The Git checkout includes the same pack. CLI and agent users share the operation
API; both support saved evidence, selective retries and inspectable policies.
Provider calls require your own credentials and may incur charges. Loading this
dataset does not call a model. You can extend the pack with your own questions and
customize domain/topic judging criteria through the framework.

## Viewer fields

| Field | Meaning |
| --- | --- |
| `id` | Original case identifier; unchanged |
| `family`, `topic` | Original family code and its descriptive display name |
| `system_prompt`, `user_prompt` | Exact original messages, including supplied evidence |
| `expected_behavior` | Original case expectation |
| `criteria_json` | Original criteria encoded as a JSON string |
| `provenance_json` | Original provenance encoded as a JSON string |
| `origin_json` | Original snapshot/record hashes encoded as a JSON string |

JSON-string fields avoid changing the original nested metadata to fit a table.
`VIEWER_PROVENANCE.json` documents this conversion. Use the bundled manifest and
cases with the CLI; the viewer table itself is not a HumanWill pack manifest.

## Construction, review and limitations

The set combines 100 original questions with 324 cases selected after Astra
blocking during discovery. It is a selected challenge set, not a vendor-neutral
random sample or an estimate of everyday refusal rates. Related mechanisms may
recur across cases. Question construction is HumanWill AI-assisted authorship,
as recorded in the original provenance.

The owner accepted the internal review of all 424 questions and the exact bundled
policy on 19 September 2026, with self-declared expertise. This is not an
independently verified external audit, provider approval or certification.
See [REVIEW_STATUS.json](pack/0.1.0/REVIEW_STATUS.json).

False refusal and usefulness are separate measures. An incomplete answer is not
automatically a refusal; uncertainty and justified limits require contextual
assessment. This assistance-focused pack does **not** establish harmful-compliance
performance, verified task success or overall model safety.

The [frozen report](https://github.com/humanwill-ai/humanwill-benchmark/tree/main/reports/cybersecurity/v0.1.1) compares 13 model configurations on this set. Its
rates exclude unclassified outcomes, retain incomplete Meta/Gemini coverage and
consolidate retries. The report uses results version **0.1.1**, distinct from this
question-pack version. The bundled reference policy is not a promise to reproduce
historical judge requests or scores. Consult the report for the exact method.

Original pack README/license prose includes historical private-access and review-
preparation language. Those files are deliberately unchanged; current public
access is through this mirror and [the product repository](https://github.com/humanwill-ai/humanwill-benchmark). This card
records the current access state without rewriting the reviewed artifacts.

## License and attribution

HumanWill's licensable rights in the questions, bundled policy and pack-specific
supporting materials use **CC BY 4.0**. The viewer transformation and this dataset
card use the same terms. Credit **HumanWill — https://humanwill.ai**, retain
notices and indicate modifications. See [LICENSE.md](LICENSE.md) and the preserved
[original pack notice](pack/0.1.0/LICENSE.md). Third-party rights remain separate.
The framework is separately licensed under Apache 2.0.

Suggested citation: HumanWill (2026), *HumanWill Cybersecurity Benchmark,
initial424*, question and delivery version 0.1.0. https://humanwill.ai. CC BY 4.0.

## Contribute

Use [GitHub Issues](https://github.com/humanwill-ai/humanwill-benchmark/issues) for scoped contributions and
[the contribution guide](https://github.com/humanwill-ai/humanwill-benchmark/blob/main/CONTRIBUTING.md) for review and rights
requirements. Practitioner feedback on eligibility and grading is welcome.
Please do not post customer data, private model responses or confidential
vulnerability details in public discussions.