Blame for 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).

```json

{"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.

```python

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.