Datasets:

SpongeBOB9684
/

CVE-TO-MITRE-V2

---
license: cc-by-4.0
task_categories:
  - text-classification
size_category: 10K<n<100K
pretty_name: CVE-TO-MITRE V2
tags:
  - multi-label-classification
  - cybersecurity
  - mitre-attack
  - threat-intelligence
  - cve
language:
  - en
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-00000-of-00001.parquet
---
# CVE-TO-MITRE V2

> Supersedes [SpongeBOB9684/CVE-TO-MITRE](https://huggingface.co/datasets/SpongeBOB9684/CVE-TO-MITRE) (2025 real+synthetic mix): this release is 15,000 human-reviewed CVEs only, re-labelled on ATT&CK v19.2 (697 techniques), CC-BY-4.0, with a reproducible builder.

## Dataset Description

Multi-label text classification: every row is a CVE description annotated with
the ordered set of MITRE ATT&CK techniques an attacker would chain to exploit
it. The dataset contains 15,000 rows over 697 labels
(ATT&CK Enterprise v19.2, parents and sub-techniques).
Each row carries the technique ids, their human-readable names, a 697-way
multi-hot vector in the stable internal label order, the CVE id(s), the EPSS
score when available, and the CISA Known Exploited Vulnerabilities (KEV) flag.

## Source and Provenance

This dataset uses data from the CVE Program (cve.org) and MITRE ATT&CK (attack.mitre.org).

The rows are a subset of the reviewed training split of an internal mapping
pipeline. Upstream of labelling, descriptions were redacted (1,712 redacted
descriptions dropped) and duplicate text/label groups collapsed (557
duplicates collapsed). Every row is traceable to the internal split manifest:
manifest train entry i is index-aligned with train row i, and the CVE id(s)
are read from that manifest's records.

### How this dataset was produced

```mermaid
flowchart LR
    A["cvelistV5 repo<br/>(public CVE records)"] --> B["Collector + dedup"]
    B --> C["Enrichment<br/>EPSS (FIRST) + KEV (CISA)"]
    C --> D["Description filter<br/>1,712 redacted dropped"]
    D --> E["LLM mapping<br/>GLM → ordered ATT&CK chains"]
    E --> F["Rule-based validation<br/>id format · confidence · evidence<br/>vs pinned ATT&CK v19.2 matrix"]
    F --> G["LLM-as-judge review<br/>+ golden gates"]
    G --> H["Human arbitration<br/>conflict-group corrections"]
    H --> I[("claude_reviewed_v2<br/>32,100 reviewed rows")]
    I --> J["Deterministic subset<br/>random.Random(42) → 15,000"]
    J --> K[("Published<br/>CVE-TO-MITRE V2")]
```

### Validation applied to the labels

| Layer | What it checks |
| --- | --- |
| Description filter | Vague, short and pre-2005 descriptions rejected before any labelling spend (1,712 dropped) |
| Rule-based validator | Technique-id format, per-technique confidence threshold (sub-threshold techniques dropped individually), evidence requirements, existence checks against the pinned ATT&CK v19.2 matrix |
| Rule-based auto-correction | Deterministic sanitisation of mapper output (chain re-ordering, generic-technique demotion) before validation |
| LLM-as-judge | Independent model verdicts on mapping quality; judge-approved rate tracked per run |
| Human arbitration | Conflict-group corrections adjudicated manually; arbitration posture calibrated on a 30-group pilot then generalised with a third-model arbiter |

### Model lineage (what this dataset trained)

A 15,000-row superset of this sampling frame (the full reviewed train split)
trained a series of encoder classifiers benchmarked under a multi-seed,
min-per-seed protocol. The winning configuration — ettin-1B encoder, LoRA
rank 32, learning rate 2e-4 — reached **micro F1 0.7607 / exact match 0.4433
on a 4,051-row held-out test set** that is *not* included in this release
(kept private, zero description overlap with the public train split). The
public dataset therefore has a validated, reproducible downstream use.

## Labels

697 MITRE ATT&CK Enterprise techniques (v19.2) -
parent techniques and sub-techniques - constitute the label space. Labelling
method, stated honestly: labels were produced by an LLM mapping pipeline
(GLM model) with rule-based validation (technique-id format, confidence
threshold, evidence requirements, existence checks against the pinned
v19.2 matrix), followed by PARTIAL human review of
conflict groups; they are model-derived and human-reviewed, not human-authored
from scratch. Format: `techniques` lists ids in source order,
`technique_names` the matching readable names, and `labels` is a 697-way
multi-hot vector whose order is the internal stable label file (a sorted list
of technique ids).

## Splits

Only the `train` split is published (15,000 rows). The remainder of the
reviewed data — 17,100 additional train rows plus the validation (4,045 rows)
and test (4,051 rows) splits — is deliberately retained by the author: this
dataset represents a substantial amount of work, and the rest is kept for the
author's own projects. As a side property, the retained splits have zero
description overlap with the published train split (verified by content
hashing).

## Sampling

Uniform random sampling without replacement: 15,000 rows drawn from
the 32,100-row reviewed train split with Python
CPython 3.11.9 `random.Random(42).sample` (Mersenne
Twister), selected indices emitted in ascending original order. The published
train contains no duplicated description. The 15,000-row size is a deliberate
release choice by the author (see Splits), not a data-availability
constraint.

## Columns

| Column | Type | Semantics |
| --- | --- | --- |
| text | string | CVE description (English, redaction-filtered) |
| cve_id | string | CVE id(s); the 166 multi-record rows join their distinct ids with the pipe character |
| techniques | list[string] | ATT&CK technique ids in source order |
| technique_names | list[string] | readable names aligned with `techniques` |
| labels | list[int64] | 697-way multi-hot vector, order = internal stable label file |
| attack_version | string | ATT&CK Enterprise version the labels were validated against (19.2) |
| epss_score | float64 (nullable) | EPSS score; null when unavailable (max across records on multi-record rows) |
| kev | bool | true when any record CVE id is in the CISA KEV catalog as of 2026-10-16 |

A canonical JSONL copy of the same rows ships at `data/train.jsonl` for
provenance; the parquet file above is the viewer-facing rendering (nulls are
genuine: non-finite EPSS values were coerced to null).

## Limitations and Biases

- Descriptions are English only; other languages are out of scope.
- Long tail of rare labels: 70 of 697 labels have zero support in this subset (full-train support > 0): T1003.007, T1010, T1014, T1016, T1021, T1027.004, T1027.010, T1036.001, T1036.003, T1036.007, T1049, T1052.001, T1059.012, T1069.002, T1071.001, T1071.002, T1098.001, T1098.003, T1114.001, T1119, T1134, T1136.003, T1140, T1176, T1201, T1202, T1218, T1218.005, T1218.015, T1219, T1221, T1491.001, T1496, T1496.001, T1505.001, T1543.002, T1546.002, T1546.008, T1547, T1547.009, T1548, T1548.004, T1548.005, T1557.003, T1560, T1561.001, T1563.002, T1564.010, T1564.012, T1569, T1572, T1574.011, T1578.005, T1580, T1583.002, T1583.004, T1584.001, T1584.004, T1588.002, T1590.001, T1591, T1591.001, T1598.003, T1602, T1608.001, T1619, T1684, T1685.001, T1685.003, T1688.
- Labels are model-derived with rule-based validation and partial human
  review; they carry the biases of that pipeline and should not be treated as
  ground-truth annotations.
- EPSS is partial by design: 18.6% of rows carry a score
  (capture 2026-09-29 to 2026-09-30); the absence
  of a score is NOT evidence of a low exploit probability.
- KEV membership is a snapshot taken at 2026-10-16T11:33:28+00:00; catalog
  membership changes over time.
- CVSS scores and CWE ids are not included in this v1.

## Citation

If you use this dataset, please cite it and the sources it derives from:

```bibtex
@misc{cve2attack_v2_2026,
  author       = {SpongeBOB9684},
  title        = {CVE-TO-MITRE V2: CVE descriptions mapped to MITRE ATT\&CK attack chains},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/SpongeBOB9684/CVE-TO-MITRE-V2}},
  license      = {CC-BY-4.0},
  note         = {Labels produced by an LLM mapping pipeline with rule-based validation, LLM-as-judge review and partial human arbitration}
}
```

Source attributions:

- CVE data: The CVE Program (cve.org), CVE records are distributed free of charge.
- Label taxonomy: MITRE ATT&CK Enterprise v19.2, (c) 2026 The MITRE Corporation. This work uses MITRE ATT&CK; MITRE is not affiliated with, nor does it endorse, this dataset.

	---
	license: cc-by-4.0
	task_categories:
	- text-classification
	size_category: 10K<n<100K
	pretty_name: CVE-TO-MITRE V2
	tags:
	- multi-label-classification
	- cybersecurity
	- mitre-attack
	- threat-intelligence
	- cve
	language:
	- en
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-00000-of-00001.parquet
	---
	# CVE-TO-MITRE V2

	> Supersedes [SpongeBOB9684/CVE-TO-MITRE](https://huggingface.co/datasets/SpongeBOB9684/CVE-TO-MITRE) (2025 real+synthetic mix): this release is 15,000 human-reviewed CVEs only, re-labelled on ATT&CK v19.2 (697 techniques), CC-BY-4.0, with a reproducible builder.

	## Dataset Description

	Multi-label text classification: every row is a CVE description annotated with
	the ordered set of MITRE ATT&CK techniques an attacker would chain to exploit
	it. The dataset contains 15,000 rows over 697 labels
	(ATT&CK Enterprise v19.2, parents and sub-techniques).
	Each row carries the technique ids, their human-readable names, a 697-way
	multi-hot vector in the stable internal label order, the CVE id(s), the EPSS
	score when available, and the CISA Known Exploited Vulnerabilities (KEV) flag.

	## Source and Provenance

	This dataset uses data from the CVE Program (cve.org) and MITRE ATT&CK (attack.mitre.org).

	The rows are a subset of the reviewed training split of an internal mapping
	pipeline. Upstream of labelling, descriptions were redacted (1,712 redacted
	descriptions dropped) and duplicate text/label groups collapsed (557
	duplicates collapsed). Every row is traceable to the internal split manifest:
	manifest train entry i is index-aligned with train row i, and the CVE id(s)
	are read from that manifest's records.

	### How this dataset was produced

	```mermaid
	flowchart LR
	A["cvelistV5 repo<br/>(public CVE records)"] --> B["Collector + dedup"]
	B --> C["Enrichment<br/>EPSS (FIRST) + KEV (CISA)"]
	C --> D["Description filter<br/>1,712 redacted dropped"]
	D --> E["LLM mapping<br/>GLM → ordered ATT&CK chains"]
	E --> F["Rule-based validation<br/>id format · confidence · evidence<br/>vs pinned ATT&CK v19.2 matrix"]
	F --> G["LLM-as-judge review<br/>+ golden gates"]
	G --> H["Human arbitration<br/>conflict-group corrections"]
	H --> I[("claude_reviewed_v2<br/>32,100 reviewed rows")]
	I --> J["Deterministic subset<br/>random.Random(42) → 15,000"]
	J --> K[("Published<br/>CVE-TO-MITRE V2")]
	```

	### Validation applied to the labels

	\| Layer \| What it checks \|
	\| --- \| --- \|
	\| Description filter \| Vague, short and pre-2005 descriptions rejected before any labelling spend (1,712 dropped) \|
	\| Rule-based validator \| Technique-id format, per-technique confidence threshold (sub-threshold techniques dropped individually), evidence requirements, existence checks against the pinned ATT&CK v19.2 matrix \|
	\| Rule-based auto-correction \| Deterministic sanitisation of mapper output (chain re-ordering, generic-technique demotion) before validation \|
	\| LLM-as-judge \| Independent model verdicts on mapping quality; judge-approved rate tracked per run \|
	\| Human arbitration \| Conflict-group corrections adjudicated manually; arbitration posture calibrated on a 30-group pilot then generalised with a third-model arbiter \|

	### Model lineage (what this dataset trained)

	A 15,000-row superset of this sampling frame (the full reviewed train split)
	trained a series of encoder classifiers benchmarked under a multi-seed,
	min-per-seed protocol. The winning configuration — ettin-1B encoder, LoRA
	rank 32, learning rate 2e-4 — reached **micro F1 0.7607 / exact match 0.4433
	on a 4,051-row held-out test set** that is not included in this release
	(kept private, zero description overlap with the public train split). The
	public dataset therefore has a validated, reproducible downstream use.

	## Labels

	697 MITRE ATT&CK Enterprise techniques (v19.2) -
	parent techniques and sub-techniques - constitute the label space. Labelling
	method, stated honestly: labels were produced by an LLM mapping pipeline
	(GLM model) with rule-based validation (technique-id format, confidence
	threshold, evidence requirements, existence checks against the pinned
	v19.2 matrix), followed by PARTIAL human review of
	conflict groups; they are model-derived and human-reviewed, not human-authored
	from scratch. Format: `techniques` lists ids in source order,
	`technique_names` the matching readable names, and `labels` is a 697-way
	multi-hot vector whose order is the internal stable label file (a sorted list
	of technique ids).

	## Splits

	Only the `train` split is published (15,000 rows). The remainder of the
	reviewed data — 17,100 additional train rows plus the validation (4,045 rows)
	and test (4,051 rows) splits — is deliberately retained by the author: this
	dataset represents a substantial amount of work, and the rest is kept for the
	author's own projects. As a side property, the retained splits have zero
	description overlap with the published train split (verified by content
	hashing).

	## Sampling

	Uniform random sampling without replacement: 15,000 rows drawn from
	the 32,100-row reviewed train split with Python
	CPython 3.11.9 `random.Random(42).sample` (Mersenne
	Twister), selected indices emitted in ascending original order. The published
	train contains no duplicated description. The 15,000-row size is a deliberate
	release choice by the author (see Splits), not a data-availability
	constraint.

	## Columns

	\| Column \| Type \| Semantics \|
	\| --- \| --- \| --- \|
	\| text \| string \| CVE description (English, redaction-filtered) \|
	\| cve_id \| string \| CVE id(s); the 166 multi-record rows join their distinct ids with the pipe character \|
	\| techniques \| list[string] \| ATT&CK technique ids in source order \|
	\| technique_names \| list[string] \| readable names aligned with `techniques` \|
	\| labels \| list[int64] \| 697-way multi-hot vector, order = internal stable label file \|
	\| attack_version \| string \| ATT&CK Enterprise version the labels were validated against (19.2) \|
	\| epss_score \| float64 (nullable) \| EPSS score; null when unavailable (max across records on multi-record rows) \|
	\| kev \| bool \| true when any record CVE id is in the CISA KEV catalog as of 2026-10-16 \|

	A canonical JSONL copy of the same rows ships at `data/train.jsonl` for
	provenance; the parquet file above is the viewer-facing rendering (nulls are
	genuine: non-finite EPSS values were coerced to null).

	## Limitations and Biases

	- Descriptions are English only; other languages are out of scope.
	- Long tail of rare labels: 70 of 697 labels have zero support in this subset (full-train support > 0): T1003.007, T1010, T1014, T1016, T1021, T1027.004, T1027.010, T1036.001, T1036.003, T1036.007, T1049, T1052.001, T1059.012, T1069.002, T1071.001, T1071.002, T1098.001, T1098.003, T1114.001, T1119, T1134, T1136.003, T1140, T1176, T1201, T1202, T1218, T1218.005, T1218.015, T1219, T1221, T1491.001, T1496, T1496.001, T1505.001, T1543.002, T1546.002, T1546.008, T1547, T1547.009, T1548, T1548.004, T1548.005, T1557.003, T1560, T1561.001, T1563.002, T1564.010, T1564.012, T1569, T1572, T1574.011, T1578.005, T1580, T1583.002, T1583.004, T1584.001, T1584.004, T1588.002, T1590.001, T1591, T1591.001, T1598.003, T1602, T1608.001, T1619, T1684, T1685.001, T1685.003, T1688.
	- Labels are model-derived with rule-based validation and partial human
	review; they carry the biases of that pipeline and should not be treated as
	ground-truth annotations.
	- EPSS is partial by design: 18.6% of rows carry a score
	(capture 2026-09-29 to 2026-09-30); the absence
	of a score is NOT evidence of a low exploit probability.
	- KEV membership is a snapshot taken at 2026-10-16T11:33:28+00:00; catalog
	membership changes over time.
	- CVSS scores and CWE ids are not included in this v1.

	## Citation

	If you use this dataset, please cite it and the sources it derives from:

	```bibtex
	@misc{cve2attack_v2_2026,
	author = {SpongeBOB9684},
	title = {CVE-TO-MITRE V2: CVE descriptions mapped to MITRE ATT\&CK attack chains},
	year = {2026},
	howpublished = {\url{https://huggingface.co/datasets/SpongeBOB9684/CVE-TO-MITRE-V2}},
	license = {CC-BY-4.0},
	note = {Labels produced by an LLM mapping pipeline with rule-based validation, LLM-as-judge review and partial human arbitration}
	}
	```

	Source attributions:

	- CVE data: The CVE Program (cve.org), CVE records are distributed free of charge.
	- Label taxonomy: MITRE ATT&CK Enterprise v19.2, (c) 2026 The MITRE Corporation. This work uses MITRE ATT&CK; MITRE is not affiliated with, nor does it endorse, this dataset.