Datasets:
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 (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
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:
@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.