README.md

metadata

license: cc0-1.0
tags:
  - security
  - cybersecurity
  - cve
  - cisa
  - kev
  - nvd
  - parquet
  - rag
  - public-domain
pretty_name: CVE-KEV Snapshot (2025-10-29)
viewer: true
configs:
  - config_name: preview
    data_files:
      - split: train
        path: parquet/preview.parquet
  - config_name: cve
    data_files:
      - split: train
        path: parquet/cve.parquet
  - config_name: nvd_meta
    data_files:
      - split: train
        path: parquet/nvd_meta.parquet
  - config_name: kev
    data_files:
      - split: train
        path: parquet/kev.parquet
  - config_name: edges
    data_files:
      - split: train
        path: parquet/edges.parquet
  - config_name: rag_meta
    data_files:
      - split: train
        path: rag/meta.parquet
  - config_name: rag_mapping
    data_files:
      - split: train
        path: rag/mapping.parquet

CVE-KEV Snapshot (one-time, offline bundle)

This bundle lets you rank likely exploited CVEs and cite official sources without any APIs or accounts, fully offline. It’s a one-time snapshot of the last 90 days of NVD, aligned with CISA KEV for immediate focus on likely exploited CVEs. Query-ready Parquet tables and an optional small RAG pack let you rank by severity, pivot by CWE, and fetch references for briefings. Every row includes provenance; validation metrics and an integrity manifest are included. The compiled dataset is dedicated to the public domain (CC0).

Quick start (60 seconds)

• Open demo/OVX_quickstart.html (no network) or demo/OVX_quickstart.ipynb.
• Or run this DuckDB query for top KEV CVEs by severity:

SELECT c.cve_id, m.cvss_v3_score, k.date_added
FROM read_parquet('parquet/kev.parquet') k
JOIN read_parquet('parquet/cve.parquet') c USING (cve_id)
LEFT JOIN read_parquet('parquet/nvd_meta.parquet') m USING (cve_id)
ORDER BY (m.cvss_v3_score IS NULL) ASC, m.cvss_v3_score DESC, k.date_added DESC
LIMIT 20;

Snapshot as of (UTC): 2025-10-29T13:51:37Z NVD window (days): 90

Accuracy at a glance

• Total CVE rows: 12307
• Total KEV rows: 28
• Artifacts: Validation Report · Integrity Check · Build Manifest
  • KEV is window‑aligned; within‑window coverage = 1.0 by design. See docs/VALIDATION.json for kev_rows_total_fetched, kev_rows_within_window, kev_rows_filtered_out, and kev_within_window_over_global_ratio.

What this is

A single, immutable dataset combining:

• CVE records (IDs, summaries) with CVSS and CWE from NVD (public domain)
• KEV flags from CISA (CC0)
• Provenance on every row and edge (source, source_url, retrieved_at, source_record_hash)
• A tiny RAG pack (embeddings + FAISS index) built only from PD/CC0 text

Who this is for

• Security ML teams: feature prototyping (KEV=true, CVSS, CWE signals)
• RAG/QA prototypers: grounded retrieval with official citations
• Analysts: local, verifiable artifact (no APIs/accounts)

What you can do quickly (offline)

• Rank KEV CVEs by severity; pivot by CWE categories
• Retrieve references for a CVE (official NVD/KEV links)
• Trial RAG with a tiny index (if the embedding model is cached locally)
• Inspect validation metrics and per-row provenance

Files

• parquet/: cve.parquet, nvd_meta.parquet, kev.parquet, edges.parquet, preview.parquet (~1000 CVEs; see preview criteria)
• rag/: index.faiss, meta.parquet, mapping.parquet, vectors.npy (optional)
• docs/: LICENSES.md, LICENCE.md (CC0 legal code), NOTICE.md, INTEGRITY.txt, VALIDATION.json (if enabled), BUILD_MANIFEST.json
• demo/: OVX_quickstart.ipynb (offline quickstart)

Validation and integrity

• VALIDATION.json includes: counts, cvss_v3_presence_ratio, cwe_presence_ratio, kev_cve_coverage_ratio, kev_cve_coverage_ratio_within_window, kev_rows_total_fetched, kev_rows_within_window, kev_within_window_over_global_ratio, rejected_cve_count, url_shape_failures, http_head_failures_hard, http_head_failures_flaky, dead_reference_links, duplicate_edges_dropped, snapshot_as_of

• URL checks (if present in this snapshot’s validation step) were executed conservatively with a single worker to reduce flakiness and rate limiting.

• INTEGRITY.txt: SHA-256 list of all files in this bundle. Verify locally:

  • macOS:

    shasum -a 256 -c docs/INTEGRITY.txt
    

  • Linux:

    sha256sum -c docs/INTEGRITY.txt
    

• Build metadata: see docs/BUILD_MANIFEST.json for snapshot parameters (timestamp, NVD window, tool/version info, internal commit/config). Provided for transparency; the build system is not included.

RAG constraints

• Texts are PD/CC0-only (NVD short descriptions, KEV notes)
• meta.parquet: normalize=true, metric="IP", pinned model_name="BAAI/bge-small-en-v1.5" and dimension=384
• Retrieval requires the model to be present in local cache; no downloads
• If rag/mapping.parquet is present, it maps FAISS row_index → cve_id with columns: row_index (int32), cve_id (string)

Preview parquet

• parquet/preview.parquet is a convenience subset for quick inspection.
• Selection: first 1000 rows by cve_id ordering from parquet/cve.parquet.
• Columns: cve_id, summary, published_date, modified_date (when available); otherwise falls back to a best-effort subset.

Non-affiliation and license

• Not affiliated with NIST/NVD, CISA/KEV, or FIRST/EPSS
• NVD non-endorsement: "This product uses data from the NVD API but is not endorsed or certified by the NVD."
• Compiled artifact dedicated to the public domain under CC0 1.0 (see docs/LICENCE.md)
• Upstream sources: NVD (public domain), CISA KEV (CC0). Third‑party pages reached via reference URLs are governed by their own terms

Start here

• Open demo/OVX_quickstart.ipynb (no network calls)
• Or open demo/OVX_quickstart.html for a view-only quickstart
• Or query with DuckDB directly:

SELECT c.cve_id, m.cvss_v3_score, k.date_added
FROM read_parquet('parquet/kev.parquet') k
JOIN read_parquet('parquet/cve.parquet') c USING (cve_id)
LEFT JOIN read_parquet('parquet/nvd_meta.parquet') m USING (cve_id)
ORDER BY (m.cvss_v3_score IS NULL) ASC, m.cvss_v3_score DESC, k.date_added DESC
LIMIT 20;

Top queries to try (DuckDB)

-- Top KEV CVEs by CVSS
SELECT c.cve_id, m.cvss_v3_score, k.date_added
FROM read_parquet('parquet/kev.parquet') k
JOIN read_parquet('parquet/cve.parquet') c USING (cve_id)
LEFT JOIN read_parquet('parquet/nvd_meta.parquet') m USING (cve_id)
ORDER BY (m.cvss_v3_score IS NULL) ASC, m.cvss_v3_score DESC, k.date_added DESC
LIMIT 20;

-- Count malformed or missing CVSS
SELECT SUM(cvss_v3_score IS NULL) AS missing_cvss_v3, COUNT(*) AS total
FROM read_parquet('parquet/nvd_meta.parquet');

-- Top CWE categories by count
WITH u AS (
  SELECT UNNEST(cwe_ids) AS cwe FROM read_parquet('parquet/nvd_meta.parquet')
)
SELECT cwe, COUNT(*) AS cnt
FROM u
GROUP BY cwe
ORDER BY cnt DESC
LIMIT 20;

-- References for a specific CVE
SELECT dst_id AS reference_url
FROM read_parquet('parquet/edges.parquet')
WHERE src_type='cve' AND src_id='CVE-2021-44228' AND edge_type='cve_ref_url'
ORDER BY reference_url;

Validation notes

• Where URL reachability checks were included, they used a single worker by default and domain-specific pacing (e.g., stricter for vuldb.com). Counts of malformed URLs and network failures are summarized in docs/VALIDATION.json.

Limitations

• Some CVEs may lack CVSS vectors/scores in the NVD window (nulls are expected).
• URL checks are conservative and may still include dead or redirected links; always verify with official sources.
• KEV rows are filtered to the NVD window by design; KEV counts reflect in-window coverage, not global totals.

Scope and KEV alignment

• KEV is filtered to the same NVD window; only KEV CVEs that are also in the NVD window are included. Edges never reference out‑of‑window CVEs.
• Coverage metric naming: kev_cve_coverage_ratio_within_window reflects this alignment and is expected to be 1.0 by design. The legacy key kev_cve_coverage_ratio is retained and equals the within-window value.
• Global context ratio: kev_within_window_over_global_ratio = kev_rows_within_window / kev_rows_total_fetched (share of KEV entries that fall into this snapshot’s NVD window).

Schemas (columns and types)

• parquet/cve.parquet

  • cve_id: string
  • summary: string
  • description_hash: string
  • published_date: timestamp[us, UTC]
  • modified_date: timestamp[us, UTC]
  • is_rejected: boolean
  • source: string
  • source_url: string
  • retrieved_at: timestamp[us, UTC]
  • source_record_hash: string

• parquet/nvd_meta.parquet

  • cve_id: string
  • cvss_v3_score: float64 (nullable)
  • cvss_v3_vector: string (nullable)
  • cvss_v2_score: float64 (nullable)
  • cwe_ids: list
  • reference_urls: list
  • ref_tags: list
  • source: string
  • source_url: string
  • retrieved_at: timestamp[us, UTC]
  • source_record_hash: string

• parquet/kev.parquet

  • cve_id: string
  • date_added: date32[day]
  • notes: string (nullable)
  • source: string
  • source_url: string
  • retrieved_at: timestamp[us, UTC]
  • source_record_hash: string

• parquet/edges.parquet

  • src_type: string
  • src_id: string
  • edge_type: string
  • dst_type: string
  • dst_id: string
  • source: string
  • source_url: string
  • retrieved_at: timestamp[us, UTC]

• rag/meta.parquet

  • model_name: string
  • dim: int32
  • normalize: boolean
  • metric: string
  • texts_count: int64

Storage and performance notes

• Parquet compression: snappy (writer default).
• Disk footprint: varies by window; see your build output directory sizes to estimate download needs.

Uniqueness rules

• Primary keys:
  • cve.parquet: cve_id
  • nvd_meta.parquet: cve_id
  • kev.parquet: cve_id
• Edges composite uniqueness:
  • Unique on (src_type, src_id, edge_type, dst_type, dst_id, source)
  • Duplicates are dropped; see duplicate_edges_dropped in docs/VALIDATION.json.

Citation

If you use this snapshot, please cite:

"CVE-KEV Snapshot (2025-10-29T13:51:37Z)", CC0-1.0, https://huggingface.co/datasets/NostromoHub/cve-kev-snapshot-90d-2025-10-29

Usage

• DuckDB

SELECT c.cve_id, m.cvss_v3_score, k.date_added
FROM read_parquet('parquet/kev.parquet') k
JOIN read_parquet('parquet/cve.parquet') c USING (cve_id)
LEFT JOIN read_parquet('parquet/nvd_meta.parquet') m USING (cve_id)
ORDER BY (m.cvss_v3_score IS NULL) ASC, m.cvss_v3_score DESC, k.date_added DESC
LIMIT 20;

• Python (Pandas)

import pandas as pd
k = pd.read_parquet('parquet/kev.parquet')
c = pd.read_parquet('parquet/cve.parquet')
m = pd.read_parquet('parquet/nvd_meta.parquet')
df = k.merge(c, on='cve_id').merge(m[['cve_id','cvss_v3_score']], on='cve_id', how='left')
print(df.head())

• Python (Polars)

import polars as pl
k = pl.read_parquet('parquet/kev.parquet')
c = pl.read_parquet('parquet/cve.parquet')
m = pl.read_parquet('parquet/nvd_meta.parquet').select('cve_id','cvss_v3_score')
df = k.join(c, on='cve_id').join(m, on='cve_id', how='left')
print(df.head())