DATASET_CARD.md

HolyCyber — Dataset Card

A large, fully grounded, decontaminated instruction dataset spanning cybersecurity.

Overview

• Rows: 51,805
• Groups (leakage units): 12,125
• Splits: test=5,702, train=40,998, val=5,105
• Domains: 17 | Task types: 19
• Avg assistant length: 11,001 chars
• Row size (tiktoken cl100k, user+assistant): min 2,050 / median 2,324 / mean 2,464 / p90 2,926 / max 6,790 tokens
• Total tokens: 127,643,325 | Rows below the 2,000-token floor: 0
• Seed: 1337 | Canary: BENCHMARK DATA SHOULD NEVER APPEAR AS TRAINING DATA: HOLYCYBER-DECONTAM-CANARY-9f2c7a14
• Entity's own material: median 16% of an answer's tokens; 32,399 rows are below 25%. The balance is the labelled reference appendix (## Reference: ...), not extra facts about the row's entity.

Record format

One JSON object per line; messages is a three-turn chat (system / user / assistant).

{"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": "...", "split": "train", "canary": "...",
 "tokens": {"user": 180, "assistant": 2350, "total": 2530},
 "core_share": 0.41, "appendix": true,
 "references": [{"title": "...", "url": "https://..."}],
 "messages": [{"role": "system", "content": "..."},
              {"role": "user", "content": "..."},
              {"role": "assistant", "content": "## ...\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 its licence
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 entities cited in the answer
canary	decontamination marker

What a row contains

Section headings used in the answers (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

Row design

The corpus is built as one focused dataset per cybersecurity domain (dataset/layers/<domain>/), each from that domain's own authoritative sources, and this global file is their concatenation.

Each row is a self-contained, long-form briefing on one entity (an ATT&CK technique/group/software, a CWE weakness, a CAPEC pattern, a KEV vulnerability, a Sigma rule, an ATLAS technique, a D3FEND control, or a section of a cited arXiv paper). A row is built in three steps:

1. Base cell — the parser output for that entity and that (task type, subdomain).
2. Task shapes — the same entity asked a different defensive question (detection, mitigation, framework mapping, response, adversary usage), built only from other sections of that entity's own record, so no new fact is introduced.
3. Reference pack — if the row is still short, it is topped up with compact descriptions of other entities of the same domain, taken from that domain's own source text, with a rotating window so two rows do not carry identical padding.

Rows are packed to a hard floor of 2,000 tokens, so no row is below the floor. Context is allocated disjointly across an entity's cells, so a record is never repeated inside a single entity, and the split is cut on group_key so an entity never appears in two splits.

Construction

Sources are parsed deterministically; no facts are invented by a model.

Source	Rows	Licence
mitre-attack	27,221	MITRE ATT&CK — free for defensive use
arxiv-corpus	11,332	arXiv preprints; cited per row
cisa-kev	5,843	CISA KEV — U.S. Government work (public domain)
cwe	2,747	CWE (MITRE) — attribution required
capec	2,714	CAPEC (MITRE) — attribution required
ietf-rfc	645	see source
atlas	608	MITRE ATLAS — free for defensive use
nist-publication	432	see source
owasp-cheatsheet	175	see source
research-corpus	88	Curated; cites public arXiv preprints

Quality controls

• Exact duplicates removed: 0
• Near-duplicates removed: 952 (MinHash-LSH: 5-word shingles, 112 perms, Jaccard >= 0.9; same task type only)
• Decontamination: token-25 overlap vs 27 benchmark files; 2,064 benchmark CVE IDs excluded; canary GUID emitted per row
• Contaminated rows removed: 0 ({})
• Quality gate: 315 rejected ({})
• Split integrity: group-level by group_key; no group appears in more than one split

Domain coverage

Domain	Rows	Subdomains covered
detection_engineering	7,970	6
ai_ml_security	4,602	10
vulnerability_management	3,493	4
governance_risk	3,176	5
defense_engineering	2,986	3
incident_response	2,943	4
attack_knowledge	2,887	6
threat_intelligence	2,865	6
pentest	2,788	7
secure_coding	2,659	6
network_security	2,373	5
identity_access	2,304	4
cloud_security	2,244	5
safety_boundary	2,200	5
ml_security_methodology	2,111	6
malware_analysis	2,104	4
cryptography	2,100	5

Known coverage gaps (subdomains not yet populated):

• threat_intelligence: malware, procedure
• vulnerability_management: prioritisation, weakness_consequences, weakness_detection, weakness_mitigation
• detection_engineering: false_positive_analysis
• defense_engineering: architecture_control, hardening
• incident_response: eradication, lessons_learned, recovery
• network_security: exfiltration
• cloud_security: storage_exposure
• malware_analysis: aliases, static_indicators
• identity_access: kerberos, mfa_bypass

Intended use

Supervised fine-tuning / instruction tuning of a defensive cybersecurity assistant (HolyCyber persona). All rows are defensive or detection/analysis oriented.

Out of scope

No functional exploits, malware, weaponised jailbreak prompts, or operational attack tooling. Pentest rows are framed as authorised engagements; offensive concepts are conceptual and paired with detection/remediation.

Limitations

Rows are unique; some paragraphs are not. The framework domains complete short entity records with a labelled reference appendix, so a sizeable share of their answer tokens is reused material from the same domain. research/AUDIT_REPORT.md measures the paragraph reuse, including how much of it crosses the train/test boundary, and research/EXTERNAL_REVIEW.md records how this compares with a public peer dataset.

No row has been reviewed individually by a domain expert: grounding bounds invention, it does not by itself prove correctness.