README.md

---
license: other
task_categories:
- text-generation
- question-answering
- text-classification
- text-retrieval
- text-ranking
language:
- en
tags:
- security
- cve
- nvd
- vulnerabilities
- cybersecurity
- cyber-security
- cwe
- cvss
- jsonl
- slimpajama
- text
- dataset
- rag
- retrieval
- question-answering
pretty_name: TanDev CVE Dataset
size_categories:
- 100K<n<1M
dataset_info:
  config_name: cve
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: source
      dtype: string
    - name: source_url
      dtype: string
    - name: license
      dtype: string
    - name: cve_id
      dtype: string
    - name: published
      dtype: string
    - name: last_modified
      dtype: string
    - name: cvss
      struct:
      - name: severity
        dtype: string
      - name: baseScore
        dtype: float64
      - name: vectorString
        dtype: string
    - name: cwes
      list: string
    - name: num_cpes
      dtype: int64
    - name: redpajama_set_name
      dtype: string
  splits:
  - name: train
    num_bytes: 379034186
    num_examples: 316780
  download_size: 90048495
  dataset_size: 379034186
configs:
- config_name: cve
  data_files:
  - split: train
    path: cve/train-*
---

# TanDev CVE Dataset (NVD SlimPajama Corpus)

A **SlimPajama‑style** corpus of CVE entries derived from the **NIST NVD (CVE 2.0)** data feeds (2002→present). Each row is a cleaned, single‑document text representation of a CVE with structured metadata for CVSS, CWE(s), timestamps, and a canonical NVD link—ready for **pretraining/continued‑pretraining**, **RAG**, **retrieval/evaluation**, and **downstream classifiers**.

**Token count:** ~108.2M tokens.

**License:** "TanDev Proprietary License — All Rights Reserved"

> ⚠️ **Ethical and responsible use.** This dataset summarizes publicly available vulnerability records. Use responsibly for research, education, and defensive security; always validate against vendor advisories before operational use.

---

## What’s in this release (Parquet)

* **Primary delivery = Parquet** shards under `data/<config>/…/train-*.parquet` for fast streaming with `datasets`.
* **Raw JSON** kept alongside in `raw/` for transparency and reproducibility.
* **One named config:** `cve` (covers all available CVE rows).

> If you previously downloaded `raw/cve.json[l]`, you can keep using it. The Hub will serve Parquet for `load_dataset(..., name="cve")` automatically.

---

## Directory layout

```
/                             # dataset root (this card lives here as README.md)
  raw/
    cve.json | cve.jsonl[.gz|.zst]   # original export retained
  data/
    cve/
      default/1.0.0/train/
        train-00000-of-XXXXX.parquet
        train-00001-of-XXXXX.parquet
        ...
```

---

## Schema

Each record follows **exactly** this structure:

```json
{
  "text": "<single-document CVE text with header, dates, CWEs, affected summary, description, references>",
  "meta": {
    "source": "nvd",
    "source_url": "https://nvd.nist.gov/vuln/detail/CVE-YYYY-XXXXX",
    "license": "Public Domain (US Gov / NIST NVD)",
    "cve_id": "CVE-YYYY-XXXXX",
    "published": "YYYY-MM-DDTHH:MM:SSZ",
    "last_modified": "YYYY-MM-DDTHH:MM:SSZ",
    "cvss": {
      "severity": "CRITICAL|HIGH|MEDIUM|LOW|NONE|UNSPECIFIED",
      "baseScore": 9.8,
      "vectorString": "CVSS:3.1/AV:N/AC:L/..."
    },
    "cwes": ["CWE-79", "CWE-89"],
    "num_cpes": 0,
    "redpajama_set_name": "SecurityNVD"
  }
}
```

**Field notes**

* `text` — plain UTF‑8 prose; no HTML; newlines preserved; boilerplate reduced.
* `meta.cvss.severity` — **string** label (e.g., `CRITICAL`, `HIGH`, …).
* `meta.cwes` — **deduped** CWE identifiers/names when available.
* `meta.num_cpes` — count of affected CPE matches retained for compactness.
* `meta.source_url` — canonical NVD details page for the specific CVE.

---

## Loading

### Load the Parquet config (recommended)

```python
from datasets import load_dataset

REPO = "tandevllc/cve_dataset"
ds = load_dataset(REPO, name="cve", split="train")
print(len(ds), ds.column_names)
print(ds[0]["text"].split("\n", 4)[0])  # header line
print(ds[0]["meta"]["cve_id"], ds[0]["meta"]["cvss"]["severity"])  # e.g., CVE id + severity
```

### Typical filters

```python
# Severity slice
critical = ds.filter(lambda r: (r.get("meta") or {}).get("cvss", {}).get("severity") == "CRITICAL")

# Year slice by published timestamp
recent_2024 = ds.filter(lambda r: (r.get("meta") or {}).get("published", "").startswith("2024-"))

# CWE presence
has_xss = ds.filter(lambda r: any("CWE-79" in c for c in (r.get("meta") or {}).get("cwes", [])))
```

### RAG / retrieval quickstart

```python
# Build a tiny vector index over the text field
from datasets import load_dataset
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.neighbors import NearestNeighbors

repo = "tandevllc/cve_dataset"
corpus = load_dataset(repo, name="cve", split="train")
texts = corpus["text"]
vec = TfidfVectorizer(min_df=3).fit(texts)
X = vec.transform(texts)
knn = NearestNeighbors(n_neighbors=10, metric="cosine").fit(X)

# Query
q = "unauthenticated RCE in Apache HTTP Server"
qv = vec.transform([q])
_, idx = knn.kneighbors(qv, n_neighbors=10)
results = corpus.select(idx[0].tolist())
```

---

## Intended uses

* **Security research**: trend analysis, CWE/technology clustering, severity drift.
* **Pretraining / continued pretraining** of security‑aware LLMs.
* **RAG** over vulnerability text for look‑ups and enrichment.
* **Classification**: severity, CWE family, vendor/product (with external joins), exploitability proxies.
* **Summarization & QA**: human‑readable notes out of CVE bulletins.

> *Not* a substitute for vendor advisories or patches. Always confirm details with original sources.

---

## Limitations & caveats

* **Abstraction**: Some vendor‑specific nuances are simplified in the textual rendering.
* **Coverage**: Mirrors what is present in NVD; if a CVE lacks English description, it may be omitted.
* **Metadata sparsity**: CWEs and CVSS may be missing for certain records.
* **CPEs**: Only the **count** (`num_cpes`) is preserved to keep records compact.


## Citation

```bibtex
@dataset{tandevllc_2025_cve_dataset,
  author = {Gupta, Smridh},
  title  = {TanDev CVE Dataset},
  year   = {2025},
  url    = {https://huggingface.co/datasets/tandevllc/cve_dataset}
}
```

---

## Maintainer

**Smridh Gupta** — [smridh@tandev.us](mailto:smridh@tandev.us)