layers/attack_knowledge/DATASET_CARD.md

HolyCyber — Layer: attack_knowledge

A focused dataset for the attack_knowledge domain of the HolyCyber corpus, built on its own from that domain's authoritative sources only.

Overview

• Rows: 2,925
• Groups (leakage units): 647
• Entities in the domain: 647
• Splits: test=280, train=2,367, val=278
• Task types: 10 | Subdomains: 6
• Row size (tiktoken cl100k, user+assistant): min 2,060 / median 2,265 / mean 2,287 / p90 2,477 / max 2,648 tokens
• Entity's own material: median 6% of an answer's tokens; 2,734 rows are below 25% (the balance is the labelled reference appendix)
• Total tokens: 6,689,467 | Rows below the 2,000-token floor: 0
• Canary: every row carries the decontamination canary

Record format

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

{"id": "6d408e38f3cb8dff", "row_key": "<domain>:<group_key>:<task_type>:<subdomain>:<part>",
 "domain": "attack_knowledge", "subdomain": "...",
 "task_type": "...", "difficulty": "intermediate", "group_key": "...",
 "source": "...", "license": "...", "split": "train", "canary": "...",
 "tokens": {"user": 180, "assistant": 2350, "total": 2530},
 "core_share": 0.41, "appendix": true,
 "references": [{"title": "...", "url": "..."}],
 "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	this layer, and the slice of it the row covers
task_type	the question shape (detection, mitigation, mapping, ...)
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

Topic sections used in this layer's answers (structural labels such as Answer or Context and the appendix are excluded):

Section	Rows
Summary	912
Defensive use	839
Underlying weaknesses	588
Consequences	584
Classification	541
Definition	539
Options	534
Rationale	534
Reference	534
Chaining	481
Related attack patterns	441
The chain	419

How this layer is built

1. Base cells — the rows the source parsers emit for this domain (one per entity x task type x subdomain).
2. Task shapes — the same entity asked a different defensive question (detection, mitigation, framework mapping, response), built only from other sections of that entity's own record. No new facts are introduced.
3. Reference pack — a row still under the token floor is padded with compact descriptions of other entities of this domain, taken from this domain's own source text; the window rotates per row.

Every row therefore comes from this domain's sources only, clears the 2,000-token floor, and no two rows are the same.

Task types

Task type	Rows
relationship_reasoning	953
knowledge_explanation	636
identification	534
risk_assessment	277
chain_reasoning	196
definition	161
control_mapping	72
incident_response	52
mitigation_remediation	41
threat_attribution	3

Subdomains

Subdomain	Rows
pattern_overview	1,331
pattern_chain	558
pattern_prerequisites	421
pattern_consequences	329
pattern_execution	245
pattern_mitigation	41

Sources

Source	Rows
capec	2,714
mitre-attack	211

Quality controls

• Exact duplicates removed: 0
• Near-duplicates removed: 74 (MinHash-LSH, Jaccard >= 0.90, same task type only)
• Quality gate: 0 rejected ({})
• Contaminated rows removed: 0 ({})
• Split integrity: group-level by group_key; no group crosses a split

Intended use

Supervised fine-tuning / evaluation of a defensive cybersecurity assistant specialised in this domain. All rows are defensive, detection or analysis oriented; none contains functional exploit code or weaponised prompts.

from datasets import load_dataset
ds = load_dataset("json", data_files=dict(train="attack_knowledge/train.jsonl", validation="attack_knowledge/val.jsonl", test="attack_knowledge/test.jsonl"))
# rows whose answer is mostly the entity itself, not the appendix
core = ds["train"].filter(lambda r: r["core_share"] >= 0.5)

Limitations

• 2,734 of 2,925 rows are more than 75% labelled reference appendix: this domain's own records are shorter than the 2,000-token floor, so the row is completed with other entities' records. Mask the appendix when fine-tuning if you only want the core answer.

• Paragraph-level reuse is measured in research/AUDIT_REPORT.md; rows are unique but their appendix material is shared.