Datasets:
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
Tags:
cybersecurity
defensive-security
detection-engineering
incident-response
threat-intelligence
vulnerability-management
License:
| # 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). | |
| ```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": "...", "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. |