layers/secure_coding/DATASET_CARD.md

HolyCyber — Layer: secure_coding

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

Overview

• Rows: 2,659
• Groups (leakage units): 1,001
• Entities in the domain: 1,018
• Splits: test=266, train=2,144, val=249
• Task types: 8 | Subdomains: 6
• Row size (tiktoken cl100k, user+assistant): min 2,088 / median 2,331 / mean 2,359 / p90 2,533 / max 5,369 tokens
• Entity's own material: median 14% of an answer's tokens; 2,046 rows are below 25% (the balance is the labelled reference appendix)
• Total tokens: 6,272,640 | 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": "secure_coding", "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	1,446
Related weaknesses	854
Mitigations	698
Observed examples	627
Worked example	546
What to notice	546
Principle	546
Demonstrative example 1	543
Detection methods	502
The weakness	345
How it shows up in code	345
How to write it safely	345

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
code_review	855
knowledge_explanation	483
control_mapping	467
secure_code_generation	358
incident_response	210
mitigation_remediation	190
detection_engineering	94
threat_attribution	2

Subdomains

Subdomain	Rows
vulnerable_vs_fixed	756
root_cause	581
remediation_principle	548
residual_risk	465
language_pattern	305
code_review	4

Sources

Source	Rows
cwe	2,548
arxiv-corpus	98
owasp-cheatsheet	13

Quality controls

• Exact duplicates removed: 0
• Near-duplicates removed: 0 (MinHash-LSH, Jaccard >= 0.90, same task type only)
• Quality gate: 225 rejected ({'unbalanced_code_fence': 225})
• 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="secure_coding/train.jsonl", validation="secure_coding/val.jsonl", test="secure_coding/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

• 2,046 of 2,659 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.