HolyCyber-Dataset

---
license: other
license_name: mixed-per-source
language:
- en
task_categories:
- text-generation
- question-answering
tags:
- cybersecurity
- defensive-security
- detection-engineering
- incident-response
- threat-intelligence
- vulnerability-management
- instruction-tuning
pretty_name: HolyCyber — defensive cybersecurity instruction dataset
size_categories:
- 10K<n<100K
configs:
- config_name: global
  data_files:
  - split: train
    path: train.jsonl
  - split: validation
    path: val.jsonl
  - split: test
    path: test.jsonl
---
# HolyCyber — defensive cybersecurity instruction dataset

A long-form, fully grounded instruction dataset for a **defensive** cybersecurity
assistant. Every row is a self-contained briefing of at least **2,000 tokens**
(user + assistant, tiktoken `cl100k_base`), written from authoritative public
sources and shipped with the provenance needed to check it.

## At a glance

| | |
|---|---|
| Rows | **51,805** (global) |
| Tokens | **127,643,325** (user + assistant) |
| Domains | **17** focused layers, each ≥2,000 rows |
| Row size | min 2,050 / median 2,324 tokens |
| Splits | train 40,998 / val 5,105 / test 5,702 |
| Task types | 19 distinct question shapes |
| Per-row metadata | measured `tokens`, `core_share`, `appendix`, stable `row_key` |
| Format | JSONL, three-turn chat (`system`, `user`, `assistant`) |
| Version | `2.3.0` |

## Layout

```
dataset/
  train.jsonl  val.jsonl  test.jsonl      # the global dataset
  samples.jsonl                            # 17 full rows, one per domain
  SAMPLES.md                               # the same rows, rendered for reading
  DATASET_CARD.md                          # global datasheet
  README.md                                # this file
  VERSION                                  # dataset version
  layers/
    INDEX.md                               # per-domain table
    <domain>/{train,val,test}.jsonl        # one focused dataset per domain
    <domain>/DATASET_CARD.md               # per-domain datasheet
```

## Domains

Each layer is built *on its own*, from that domain's authoritative sources only,
and can be used without the others.

| Layer | Rows | Tokens | Tasks | Core share | Built from |
|---|---|---|---|---|---|
| `threat_intelligence` | 2,865 | 6,584,918 | 8 | 14% | ATT&CK groups, campaigns, software, tactics |
| `attack_knowledge` | 2,925 | 6,689,467 | 10 | 6% | CAPEC attack patterns |
| `vulnerability_management` | 3,493 | 7,875,961 | 4 | 8% | CWE weaknesses, CISA KEV |
| `detection_engineering` | 7,970 | 17,964,031 | 6 | 12% | SigmaHQ rules, ATT&CK detection strategies and analytics |
| `defense_engineering` | 2,995 | 6,962,624 | 6 | 18% | D3FEND techniques, ATT&CK mitigations |
| `ai_ml_security` | 4,602 | 13,497,055 | 7 | 100% | MITRE ATLAS, AI/ML-security preprints, OWASP AI sheets |
| `secure_coding` | 2,659 | 6,272,640 | 8 | 14% | CWE weaknesses with demonstrative code, secure-coding preprints |
| `pentest` | 2,846 | 6,677,903 | 8 | 18% | ATT&CK techniques framed for authorised testing, pentest preprints |
| `incident_response` | 2,993 | 7,030,785 | 7 | 12% | ATT&CK techniques and analytics, NIST SP 800-61 |
| `network_security` | 2,383 | 5,713,268 | 6 | 19% | ATT&CK network techniques, NIST SP 800-41, RFCs, preprints |
| `cloud_security` | 2,254 | 5,310,326 | 7 | 18% | ATT&CK cloud/SaaS/container techniques, NIST SP 800-190, OWASP sheets |
| `malware_analysis` | 2,105 | 5,184,719 | 4 | 16% | ATT&CK malware and tooling, malware-analysis preprints |
| `identity_access` | 2,310 | 5,499,715 | 6 | 19% | ATT&CK credential and privilege techniques, NIST SP 800-63B |
| `cryptography` | 2,101 | 6,097,825 | 6 | 100% | CWE crypto weaknesses, NIST SP 800-57/131A/90A/38x/130, RFCs, preprints |
| `governance_risk` | 3,176 | 7,737,824 | 3 | 13% | KEV entries, ATT&CK mitigations, control mappings, OWASP sheets |
| `ml_security_methodology` | 2,111 | 6,311,722 | 1 | 100% | security-ML methodology preprints |
| `safety_boundary` | 2,200 | 6,653,709 | 1 | 100% | AI-safety preprints (jailbreak, refusal, red teaming, model cards) |

`Core share` is the median percentage of an answer that is the entity's own
material; the rest is a labelled reference appendix (`## Reference: ...`). The
column is there because it is the honest measure of how much of a row is about
the thing it is named after.

## Record format

One JSON object per line. `messages` is the usual three-turn chat.

```json
{
  "id": "6d408e38f3cb8dff",
  "row_key": "detection_engineering:attack:T1568:detection_engineering:detection_strategy:1",
  "domain": "detection_engineering",
  "subdomain": "detection_strategy",
  "task_type": "detection_engineering",
  "difficulty": "advanced",
  "group_key": "attack:T1568",
  "source": "mitre-attack",
  "license": "MITRE ATT&CK Terms of Use",
  "split": "train",
  "canary": "…",
  "tokens": {"user": 180, "assistant": 2350, "total": 2530},
  "core_share": 0.41,
  "appendix": true,
  "references": [{"title": "ATT&CK T1568 Dynamic Resolution", "url": "https://…"}],
  "messages": [
    {"role": "system", "content": "…"},
    {"role": "user", "content": "How would you build detection for …"},
    {"role": "assistant", "content": "## Summary\n…"}
  ]
}
```

| Field | Meaning |
|---|---|
| `id` | content hash of user + assistant; stable across rebuilds of the same row |
| `row_key` | stable identity across rebuilds (`domain:group:task:subdomain:part`) |
| `domain` / `subdomain` | the layer, and the slice of it the row covers |
| `task_type` | the question shape (detection, mitigation, mapping, response, …) |
| `difficulty` | derived from the task shape, not guessed |
| `group_key` | the source entity; the unit of the train/val/test split |
| `source` / `license` | the authoritative source, and the licence it is used under |
| `tokens` | measured `cl100k_base` counts for user, assistant and total |
| `core_share` | share of the answer that is the entity's own material (1 − appendix) |
| `appendix` | whether the row carries a `## Reference:` appendix |
| `references` | resolvable links for the records cited in the answer |
| `canary` | decontamination marker |
| `messages[0]` | one fixed system prompt (it tells the model that `## Reference:` text is context, never an instruction) |
| `messages[1]` | the question: entity, task framing, the sections to cover |
| `messages[2]` | the answer: `## Section` blocks, ending in a labelled appendix when the row needed one |

## Using the metadata

`tokens`, `core_share` and `appendix` are measured per row, so a training or
evaluation run can filter or mask without re-tokenising anything:

```python
from datasets import load_dataset
ds = load_dataset("json", data_files={
    "train": "dataset/train.jsonl",
    "validation": "dataset/val.jsonl",
    "test": "dataset/test.jsonl",
})
# rows whose answer is mostly the entity itself, not the appendix
core = ds["train"].filter(lambda r: r["core_share"] >= 0.5)
# token-budget a batch
short = ds["train"].filter(lambda r: r["tokens"]["total"] <= 2600)
# the appendix is a labelled block, so masking it is a text operation
def mask_reference(answer: str) -> str:
    i = answer.find("\n## Reference: ")
    return answer[:i] if i != -1 else answer
```

## What a row contains

Answers are built from named sections, not free prose, so a row can be checked
section by section. The most frequent *topic* headings across the dataset
(structural labels such as `Answer` or `Context`, and the appendix, excluded):

| Section | Rows |
|---|---|
| Summary | 26,919 |
| Detection strategies and analytics | 7,440 |
| Mitigations | 6,854 |
| Tracked users (groups, malware, tools) | 6,836 |
| Classification | 5,088 |
| Introduction | 3,499 |
| Platforms | 3,123 |
| Background | 2,807 |
| Associated weaknesses | 2,379 |
| Scope note | 2,283 |
| What it is | 2,281 |
| Detection logic | 2,249 |
| Engineering note | 2,249 |
| Supports technique(s) | 2,232 |
| Telemetry | 2,210 |
| Telemetry required | 2,177 |
| Tunable elements | 2,126 |
| Aliases | 1,931 |
| Relevant mitigations | 1,906 |
| Abstract | 1,814 |

## Samples

`SAMPLES.md` shows one complete row per domain (the row closest to that layer's
median size, from the test split), and `samples.jsonl` holds the same 17 rows in
full. They are there so a reviewer can judge the text without loading 1.2 GB.

## Quality controls

Guaranteed by the build and re-checked by `scripts/verify_layers.py`:

- **2,000-token floor** on every row (user + assistant).
- **Unique rows**: unique ids, unique assistant texts, unique questions.
- **No split leakage**: the split is cut on `group_key` (the source entity).
- **Grounded**: every fact comes from MITRE ATT&CK, D3FEND, CWE, CAPEC, ATLAS,
  SigmaHQ, CISA KEV, an RFC, a NIST publication or the cited arXiv paper.
- **Decontaminated**: no 25-gram overlap with the benchmark corpora, re-checked
  by an independent pass.
- **Defensive only**: no functional exploits, no malware, no weaponised
  jailbreak prompts.
- **Provenance**: every corpus document has a row in
  `research/fetch_manifest.csv` (url, date, licence).
- **Measured metadata**: `tokens` sums to the build total exactly; `row_key` is
  unique in every layer and never crosses a split; 40,144 rows (77.5%) carry an
  appendix, the other 11,661 are core-only.

Measured, not claimed — `research/AUDIT_REPORT.md` has the numbers:

- **Reference-appendix share.** Framework entities are short (a KEV entry is a
  few hundred tokens), so their rows are completed with other entities' records.
  The appendix is labelled and rotated, but it is still reused text.
- **Paragraph reuse.** Rows are unique; some paragraphs are not. The audit
  reports the exact share, including how much crosses the train/test boundary.
- **Task mix.** Paper-heavy domains are close to single-task by nature.

## Usage

```python
from datasets import load_dataset
ds = load_dataset("json", data_files={
    "train": "dataset/train.jsonl",
    "validation": "dataset/val.jsonl",
    "test": "dataset/test.jsonl",
})
```

For supervised fine-tuning, mask the `## Reference:` blocks if you want the loss
computed on the entity's own material only — the appendix is deliberately marked
so this is a two-line filter.

## Corpus

All sources are public:

| kind | documents | provenance |
|---|---|---|
| arXiv preprints | 3,344 | arXiv API + abs/pdf, per-paper licence recorded |
| OWASP Cheat Sheets | 137 | CheatSheetSeries repository (CC BY-SA 4.0) |
| IETF RFCs | 77 | rfc-editor.org (IETF Trust) |
| NIST publications | 44 | nvlpubs.nist.gov (U.S. Government work) |

`research/SOURCE_TABLE.md` is the generated source audit (licence, retrieval
date, label method, known problems).

## Building

```powershell
python scripts\domains.py                      # every domain
python scripts\domains.py identity_access      # one or more domains
$env:DRY="1"; python scripts\domains.py         # size preview
$env:FORCE="1"; python scripts\domains.py       # rebuild existing layers
python scripts\verify_layers.py                 # independent verification
python scripts\validate_ground_truth.py         # quotation / corpus-vocabulary check
python scripts\run_audit.py                     # four-stage independent audit
```

The build is resumable: a layer that already exists in
`build/layers/stats.json` and has a `DATASET_CARD.md` is skipped unless
`FORCE=1`.

## Licence

The dataset is a **derivative of its sources**, and the sources do not share one
licence:

| source | licence |
|---|---|
| NIST publications | U.S. Government work, public domain |
| IETF RFCs | IETF Trust legal provisions (redistribution with attribution) |
| OWASP Cheat Sheets | CC BY-SA 4.0 |
| MITRE ATT&CK / D3FEND / CWE / CAPEC / ATLAS | MITRE terms of use, attribution required |
| SigmaHQ rules | Detection Rule License 1.1 |
| CISA KEV | U.S. Government work, public domain |
| arXiv preprints | per paper, recorded in the row's `license` field |

Because OWASP material is share-alike, the conservative choice for a
redistribution is **CC BY-SA 4.0** with attribution to the sources above and to
the per-row `license` field. arXiv licences vary per paper: check
`license`/`references` before republishing a row that quotes a paper.

## Changelog

| version | date | change |
|---|---|---|
| 2.3.0 | 2026-10-29 | measured per-row `tokens` / `core_share` / `appendix` / `row_key`; system prompt now tells the model that reference blocks are context, not instructions; `SAMPLES.md`; comparison with four public cyber instruction datasets |
| 2.2.0 | 2026-10-28 | structural labels no longer used as headings (`Answer` → `Summary`, `Context` → `Background`); trailing fragments trimmed; duplicate questions removed at entity and merge level; publisher stamps stripped |
| 2.1.0 | 2026-10-26 | question bullets restricted to the entity's own sections; reference appendix labelled; references made resolvable; licence recorded per row |
| 2.0.0 | 2026-10-24 | first full build: 17 layers, 52,856 rows |

## Limits of this release

- Built by a deterministic pipeline, **not reviewed row-by-row by a domain
  expert**. Grounding bounds invention; it does not by itself prove correctness.
- The 2,000-row × 2,000-token floor is above what the smallest domains can
  support from their own records alone, which is why the reference appendix
  exists. `research/AUDIT_REPORT.md` quantifies the consequence instead of
  hiding it.
- In a few RFC rows, a resource-record dump from the typeset PDF is parsed as a
  section heading, so it appears as a heading and as a question bullet.  The
  audit records the example; removing it needs a page-furniture filter in the
  document parser.
- English only.

## Related public datasets

Six public sets were reviewed; what each offers and what was taken from it is
recorded in `research/EXTERNAL_REVIEW.md` (Part A: `Vyber07/cyber-security`,
gated; Part B: the four below, measured in full).

| Dataset | What it does well | What this dataset does differently |
|---|---|---|
| `Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset` | changelog, usage section, large instruction mix | ships real train/val/test files; stores newlines as newlines (95.4% of Trendyol rows store literal `\n`) |
| `AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.1` | readable example records, honest single split | no slot-filled questions (Fenrir has 9,214 duplicate questions); no unverifiable scale claims |
| `echel0nn1881/kimi-cyber-reasoning` | per-row token/cost metadata and a `mock` flag | every fact traces to a document; no instruction-hierarchy override text |
| `0xRavenBlack/whitehat-hacker-mom-cybersecurity` | explicit uniqueness claim, limitations, filter snippets | uniqueness is asserted for questions and answers and audited; every row is ≥2,000 tokens |
| `cyberagent/crello` | — | not a cybersecurity dataset (graphic-design layout) |

The comparison is also a contamination check: none of the four shares a verbatim
paragraph (≥120 characters, normalised) with this dataset's 305,232 unique
paragraphs.  Numbers and method: `scripts/external_compare.py`,
`build/external/compare.json`.

## The plan documents

| file | what it holds |
|---|---|
| `research/AUDIT_REPORT.md` | the dataset audit: defects, fixes, what remains |
| `research/EXTERNAL_REVIEW.md` | review of six public datasets, and what was adopted |
| `research/DECISIONS_LOG.md` | every design decision and what it cost |
| `research/SOURCE_TABLE.md` | licence, date, label method and known problems per source |
| `research/PLAN_CHECKLIST.md` | the eight-stage plan, stage by stage |
| `research/GROUND_TRUTH.md` | the quotation / vocabulary measurement |

	---
	license: other
	license_name: mixed-per-source
	language:
	- en
	task_categories:
	- text-generation
	- question-answering
	tags:
	- cybersecurity
	- defensive-security
	- detection-engineering
	- incident-response
	- threat-intelligence
	- vulnerability-management
	- instruction-tuning
	pretty_name: HolyCyber — defensive cybersecurity instruction dataset
	size_categories:
	- 10K<n<100K
	configs:
	- config_name: global
	data_files:
	- split: train
	path: train.jsonl
	- split: validation
	path: val.jsonl
	- split: test
	path: test.jsonl
	---

	# HolyCyber — defensive cybersecurity instruction dataset

	A long-form, fully grounded instruction dataset for a defensive cybersecurity
	assistant. Every row is a self-contained briefing of at least 2,000 tokens
	(user + assistant, tiktoken `cl100k_base`), written from authoritative public
	sources and shipped with the provenance needed to check it.

	## At a glance

	\| \| \|
	\|---\|---\|
	\| Rows \| 51,805 (global) \|
	\| Tokens \| 127,643,325 (user + assistant) \|
	\| Domains \| 17 focused layers, each ≥2,000 rows \|
	\| Row size \| min 2,050 / median 2,324 tokens \|
	\| Splits \| train 40,998 / val 5,105 / test 5,702 \|
	\| Task types \| 19 distinct question shapes \|
	\| Per-row metadata \| measured `tokens`, `core_share`, `appendix`, stable `row_key` \|
	\| Format \| JSONL, three-turn chat (`system`, `user`, `assistant`) \|
	\| Version \| `2.3.0` \|

	## Layout

	```
	dataset/
	train.jsonl val.jsonl test.jsonl # the global dataset
	samples.jsonl # 17 full rows, one per domain
	SAMPLES.md # the same rows, rendered for reading
	DATASET_CARD.md # global datasheet
	README.md # this file
	VERSION # dataset version
	layers/
	INDEX.md # per-domain table
	<domain>/{train,val,test}.jsonl # one focused dataset per domain
	<domain>/DATASET_CARD.md # per-domain datasheet
	```

	## Domains

	Each layer is built on its own, from that domain's authoritative sources only,
	and can be used without the others.

	\| Layer \| Rows \| Tokens \| Tasks \| Core share \| Built from \|
	\|---\|---\|---\|---\|---\|---\|
	\| `threat_intelligence` \| 2,865 \| 6,584,918 \| 8 \| 14% \| ATT&CK groups, campaigns, software, tactics \|
	\| `attack_knowledge` \| 2,925 \| 6,689,467 \| 10 \| 6% \| CAPEC attack patterns \|
	\| `vulnerability_management` \| 3,493 \| 7,875,961 \| 4 \| 8% \| CWE weaknesses, CISA KEV \|
	\| `detection_engineering` \| 7,970 \| 17,964,031 \| 6 \| 12% \| SigmaHQ rules, ATT&CK detection strategies and analytics \|
	\| `defense_engineering` \| 2,995 \| 6,962,624 \| 6 \| 18% \| D3FEND techniques, ATT&CK mitigations \|
	\| `ai_ml_security` \| 4,602 \| 13,497,055 \| 7 \| 100% \| MITRE ATLAS, AI/ML-security preprints, OWASP AI sheets \|
	\| `secure_coding` \| 2,659 \| 6,272,640 \| 8 \| 14% \| CWE weaknesses with demonstrative code, secure-coding preprints \|
	\| `pentest` \| 2,846 \| 6,677,903 \| 8 \| 18% \| ATT&CK techniques framed for authorised testing, pentest preprints \|
	\| `incident_response` \| 2,993 \| 7,030,785 \| 7 \| 12% \| ATT&CK techniques and analytics, NIST SP 800-61 \|
	\| `network_security` \| 2,383 \| 5,713,268 \| 6 \| 19% \| ATT&CK network techniques, NIST SP 800-41, RFCs, preprints \|
	\| `cloud_security` \| 2,254 \| 5,310,326 \| 7 \| 18% \| ATT&CK cloud/SaaS/container techniques, NIST SP 800-190, OWASP sheets \|
	\| `malware_analysis` \| 2,105 \| 5,184,719 \| 4 \| 16% \| ATT&CK malware and tooling, malware-analysis preprints \|
	\| `identity_access` \| 2,310 \| 5,499,715 \| 6 \| 19% \| ATT&CK credential and privilege techniques, NIST SP 800-63B \|
	\| `cryptography` \| 2,101 \| 6,097,825 \| 6 \| 100% \| CWE crypto weaknesses, NIST SP 800-57/131A/90A/38x/130, RFCs, preprints \|
	\| `governance_risk` \| 3,176 \| 7,737,824 \| 3 \| 13% \| KEV entries, ATT&CK mitigations, control mappings, OWASP sheets \|
	\| `ml_security_methodology` \| 2,111 \| 6,311,722 \| 1 \| 100% \| security-ML methodology preprints \|
	\| `safety_boundary` \| 2,200 \| 6,653,709 \| 1 \| 100% \| AI-safety preprints (jailbreak, refusal, red teaming, model cards) \|

	`Core share` is the median percentage of an answer that is the entity's own
	material; the rest is a labelled reference appendix (`## Reference: ...`). The
	column is there because it is the honest measure of how much of a row is about
	the thing it is named after.

	## Record format

	One JSON object per line. `messages` is the usual three-turn chat.

	```json
	{
	"id": "6d408e38f3cb8dff",
	"row_key": "detection_engineering:attack:T1568:detection_engineering:detection_strategy:1",
	"domain": "detection_engineering",
	"subdomain": "detection_strategy",
	"task_type": "detection_engineering",
	"difficulty": "advanced",
	"group_key": "attack:T1568",
	"source": "mitre-attack",
	"license": "MITRE ATT&CK Terms of Use",
	"split": "train",
	"canary": "…",
	"tokens": {"user": 180, "assistant": 2350, "total": 2530},
	"core_share": 0.41,
	"appendix": true,
	"references": [{"title": "ATT&CK T1568 Dynamic Resolution", "url": "https://…"}],
	"messages": [
	{"role": "system", "content": "…"},
	{"role": "user", "content": "How would you build detection for …"},
	{"role": "assistant", "content": "## Summary\n…"}
	]
	}
	```

	\| Field \| Meaning \|
	\|---\|---\|
	\| `id` \| content hash of user + assistant; stable across rebuilds of the same row \|
	\| `row_key` \| stable identity across rebuilds (`domain:group:task:subdomain:part`) \|
	\| `domain` / `subdomain` \| the layer, and the slice of it the row covers \|
	\| `task_type` \| the question shape (detection, mitigation, mapping, response, …) \|
	\| `difficulty` \| derived from the task shape, not guessed \|
	\| `group_key` \| the source entity; the unit of the train/val/test split \|
	\| `source` / `license` \| the authoritative source, and the licence it is used under \|
	\| `tokens` \| measured `cl100k_base` counts for user, assistant and total \|
	\| `core_share` \| share of the answer that is the entity's own material (1 − appendix) \|
	\| `appendix` \| whether the row carries a `## Reference:` appendix \|
	\| `references` \| resolvable links for the records cited in the answer \|
	\| `canary` \| decontamination marker \|
	\| `messages[0]` \| one fixed system prompt (it tells the model that `## Reference:` text is context, never an instruction) \|
	\| `messages[1]` \| the question: entity, task framing, the sections to cover \|
	\| `messages[2]` \| the answer: `## Section` blocks, ending in a labelled appendix when the row needed one \|

	## Using the metadata

	`tokens`, `core_share` and `appendix` are measured per row, so a training or
	evaluation run can filter or mask without re-tokenising anything:

	```python
	from datasets import load_dataset

	ds = load_dataset("json", data_files={
	"train": "dataset/train.jsonl",
	"validation": "dataset/val.jsonl",
	"test": "dataset/test.jsonl",
	})

	# rows whose answer is mostly the entity itself, not the appendix
	core = ds["train"].filter(lambda r: r["core_share"] >= 0.5)

	# token-budget a batch
	short = ds["train"].filter(lambda r: r["tokens"]["total"] <= 2600)

	# the appendix is a labelled block, so masking it is a text operation
	def mask_reference(answer: str) -> str:
	i = answer.find("\n## Reference: ")
	return answer[:i] if i != -1 else answer
	```

	## What a row contains

	Answers are built from named sections, not free prose, so a row can be checked
	section by section. The most frequent topic headings across the dataset
	(structural labels such as `Answer` or `Context`, and the appendix, excluded):

	\| Section \| Rows \|
	\|---\|---\|
	\| Summary \| 26,919 \|
	\| Detection strategies and analytics \| 7,440 \|
	\| Mitigations \| 6,854 \|
	\| Tracked users (groups, malware, tools) \| 6,836 \|
	\| Classification \| 5,088 \|
	\| Introduction \| 3,499 \|
	\| Platforms \| 3,123 \|
	\| Background \| 2,807 \|
	\| Associated weaknesses \| 2,379 \|
	\| Scope note \| 2,283 \|
	\| What it is \| 2,281 \|
	\| Detection logic \| 2,249 \|
	\| Engineering note \| 2,249 \|
	\| Supports technique(s) \| 2,232 \|
	\| Telemetry \| 2,210 \|
	\| Telemetry required \| 2,177 \|
	\| Tunable elements \| 2,126 \|
	\| Aliases \| 1,931 \|
	\| Relevant mitigations \| 1,906 \|
	\| Abstract \| 1,814 \|

	## Samples

	`SAMPLES.md` shows one complete row per domain (the row closest to that layer's
	median size, from the test split), and `samples.jsonl` holds the same 17 rows in
	full. They are there so a reviewer can judge the text without loading 1.2 GB.

	## Quality controls

	Guaranteed by the build and re-checked by `scripts/verify_layers.py`:

	- 2,000-token floor on every row (user + assistant).
	- Unique rows: unique ids, unique assistant texts, unique questions.
	- No split leakage: the split is cut on `group_key` (the source entity).
	- Grounded: every fact comes from MITRE ATT&CK, D3FEND, CWE, CAPEC, ATLAS,
	SigmaHQ, CISA KEV, an RFC, a NIST publication or the cited arXiv paper.
	- Decontaminated: no 25-gram overlap with the benchmark corpora, re-checked
	by an independent pass.
	- Defensive only: no functional exploits, no malware, no weaponised
	jailbreak prompts.
	- Provenance: every corpus document has a row in
	`research/fetch_manifest.csv` (url, date, licence).
	- Measured metadata: `tokens` sums to the build total exactly; `row_key` is
	unique in every layer and never crosses a split; 40,144 rows (77.5%) carry an
	appendix, the other 11,661 are core-only.

	Measured, not claimed — `research/AUDIT_REPORT.md` has the numbers:

	- Reference-appendix share. Framework entities are short (a KEV entry is a
	few hundred tokens), so their rows are completed with other entities' records.
	The appendix is labelled and rotated, but it is still reused text.
	- Paragraph reuse. Rows are unique; some paragraphs are not. The audit
	reports the exact share, including how much crosses the train/test boundary.
	- Task mix. Paper-heavy domains are close to single-task by nature.

	## Usage

	```python
	from datasets import load_dataset

	ds = load_dataset("json", data_files={
	"train": "dataset/train.jsonl",
	"validation": "dataset/val.jsonl",
	"test": "dataset/test.jsonl",
	})
	```

	For supervised fine-tuning, mask the `## Reference:` blocks if you want the loss
	computed on the entity's own material only — the appendix is deliberately marked
	so this is a two-line filter.

	## Corpus

	All sources are public:

	\| kind \| documents \| provenance \|
	\|---\|---\|---\|
	\| arXiv preprints \| 3,344 \| arXiv API + abs/pdf, per-paper licence recorded \|
	\| OWASP Cheat Sheets \| 137 \| CheatSheetSeries repository (CC BY-SA 4.0) \|
	\| IETF RFCs \| 77 \| rfc-editor.org (IETF Trust) \|
	\| NIST publications \| 44 \| nvlpubs.nist.gov (U.S. Government work) \|

	`research/SOURCE_TABLE.md` is the generated source audit (licence, retrieval
	date, label method, known problems).

	## Building

	```powershell
	python scripts\domains.py # every domain
	python scripts\domains.py identity_access # one or more domains
	$env:DRY="1"; python scripts\domains.py # size preview
	$env:FORCE="1"; python scripts\domains.py # rebuild existing layers
	python scripts\verify_layers.py # independent verification
	python scripts\validate_ground_truth.py # quotation / corpus-vocabulary check
	python scripts\run_audit.py # four-stage independent audit
	```

	The build is resumable: a layer that already exists in
	`build/layers/stats.json` and has a `DATASET_CARD.md` is skipped unless
	`FORCE=1`.

	## Licence

	The dataset is a derivative of its sources, and the sources do not share one
	licence:

	\| source \| licence \|
	\|---\|---\|
	\| NIST publications \| U.S. Government work, public domain \|
	\| IETF RFCs \| IETF Trust legal provisions (redistribution with attribution) \|
	\| OWASP Cheat Sheets \| CC BY-SA 4.0 \|
	\| MITRE ATT&CK / D3FEND / CWE / CAPEC / ATLAS \| MITRE terms of use, attribution required \|
	\| SigmaHQ rules \| Detection Rule License 1.1 \|
	\| CISA KEV \| U.S. Government work, public domain \|
	\| arXiv preprints \| per paper, recorded in the row's `license` field \|

	Because OWASP material is share-alike, the conservative choice for a
	redistribution is CC BY-SA 4.0 with attribution to the sources above and to
	the per-row `license` field. arXiv licences vary per paper: check
	`license`/`references` before republishing a row that quotes a paper.

	## Changelog

	\| version \| date \| change \|
	\|---\|---\|---\|
	\| 2.3.0 \| 2026-10-29 \| measured per-row `tokens` / `core_share` / `appendix` / `row_key`; system prompt now tells the model that reference blocks are context, not instructions; `SAMPLES.md`; comparison with four public cyber instruction datasets \|
	\| 2.2.0 \| 2026-10-28 \| structural labels no longer used as headings (`Answer` → `Summary`, `Context` → `Background`); trailing fragments trimmed; duplicate questions removed at entity and merge level; publisher stamps stripped \|
	\| 2.1.0 \| 2026-10-26 \| question bullets restricted to the entity's own sections; reference appendix labelled; references made resolvable; licence recorded per row \|
	\| 2.0.0 \| 2026-10-24 \| first full build: 17 layers, 52,856 rows \|

	## Limits of this release

	- Built by a deterministic pipeline, **not reviewed row-by-row by a domain
	expert**. Grounding bounds invention; it does not by itself prove correctness.
	- The 2,000-row × 2,000-token floor is above what the smallest domains can
	support from their own records alone, which is why the reference appendix
	exists. `research/AUDIT_REPORT.md` quantifies the consequence instead of
	hiding it.
	- In a few RFC rows, a resource-record dump from the typeset PDF is parsed as a
	section heading, so it appears as a heading and as a question bullet. The
	audit records the example; removing it needs a page-furniture filter in the
	document parser.
	- English only.

	## Related public datasets

	Six public sets were reviewed; what each offers and what was taken from it is
	recorded in `research/EXTERNAL_REVIEW.md` (Part A: `Vyber07/cyber-security`,
	gated; Part B: the four below, measured in full).

	\| Dataset \| What it does well \| What this dataset does differently \|
	\|---\|---\|---\|
	\| `Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset` \| changelog, usage section, large instruction mix \| ships real train/val/test files; stores newlines as newlines (95.4% of Trendyol rows store literal `\n`) \|
	\| `AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.1` \| readable example records, honest single split \| no slot-filled questions (Fenrir has 9,214 duplicate questions); no unverifiable scale claims \|
	\| `echel0nn1881/kimi-cyber-reasoning` \| per-row token/cost metadata and a `mock` flag \| every fact traces to a document; no instruction-hierarchy override text \|
	\| `0xRavenBlack/whitehat-hacker-mom-cybersecurity` \| explicit uniqueness claim, limitations, filter snippets \| uniqueness is asserted for questions and answers and audited; every row is ≥2,000 tokens \|
	\| `cyberagent/crello` \| — \| not a cybersecurity dataset (graphic-design layout) \|

	The comparison is also a contamination check: none of the four shares a verbatim
	paragraph (≥120 characters, normalised) with this dataset's 305,232 unique
	paragraphs. Numbers and method: `scripts/external_compare.py`,
	`build/external/compare.json`.

	## The plan documents

	\| file \| what it holds \|
	\|---\|---\|
	\| `research/AUDIT_REPORT.md` \| the dataset audit: defects, fixes, what remains \|
	\| `research/EXTERNAL_REVIEW.md` \| review of six public datasets, and what was adopted \|
	\| `research/DECISIONS_LOG.md` \| every design decision and what it cost \|
	\| `research/SOURCE_TABLE.md` \| licence, date, label method and known problems per source \|
	\| `research/PLAN_CHECKLIST.md` \| the eight-stage plan, stage by stage \|
	\| `research/GROUND_TRUTH.md` \| the quotation / vocabulary measurement \|