README.md

---
pretty_name: OffSec RedTeam Info
license: other
task_categories:
- text-generation
- text-retrieval
- text-ranking
- feature-extraction
- sentence-similarity
- question-answering
- summarization
language:
- en
tags:
- security
- red-team
- redteam
- offensive-security
- offsec
- pentesting
- penetration-testing
- osint
- dfir
- threat-intel
- cloud-security
- kubernetes
- active-directory
- malware-analysis
- reversing
- training-blogs
- websecurity
- web-security
- dataset
- jsonl
- parquet
- cybersecurity
- cyber-security
size_categories:
- 1M<n<10M
configs:
- config_name: ad_ops
  data_files:
  - split: train
    path: ad_ops/train-*
- config_name: binary_exploitation
  data_files:
  - split: train
    path: binary_exploitation/train-*
- config_name: c2_tradecraft
  data_files:
  - split: train
    path: c2_tradecraft/train-*
- config_name: cloud_redteam
  data_files:
  - split: train
    path: cloud_redteam/train-*
- config_name: core_wikis
  data_files:
  - split: train
    path: core_wikis/train-*
- config_name: dfir_detection
  data_files:
  - split: train
    path: dfir_detection/train-*
- config_name: ics_scada
  data_files:
  - split: train
    path: ics_scada/train-*
- config_name: kubernetes_container
  data_files:
  - split: train
    path: kubernetes_container/train-*
- config_name: linux_unix
  data_files:
  - split: train
    path: linux_unix/train-*
- config_name: mobile_wireless
  data_files:
  - split: train
    path: mobile_wireless/train-*
- config_name: osint_recon
  data_files:
  - split: train
    path: osint_recon/train-*
- config_name: password_cracking
  data_files:
  - split: train
    path: password_cracking/train-*
- config_name: phishing_se
  data_files:
  - split: train
    path: phishing_se/train-*
- config_name: reversing_malware
  data_files:
  - split: train
    path: reversing_malware/train-*
- config_name: threat_intel
  data_files:
  - split: train
    path: threat_intel/train-*
- config_name: training_blogs
  data_files:
  - split: train
    path: training_blogs/train-*
- config_name: web_app
  data_files:
  - split: train
    path: web_app/train-*
- config_name: windows_privesc
  data_files:
  - split: train
    path: windows_privesc/train-*
dataset_info:
- config_name: ad_ops
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 90620904
    num_examples: 13277
  download_size: 37244413
  dataset_size: 90620904
- config_name: binary_exploitation
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 47008528
    num_examples: 3079
  download_size: 22074655
  dataset_size: 47008528
- config_name: c2_tradecraft
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 2834407
    num_examples: 554
  download_size: 1327232
  dataset_size: 2834407
- config_name: cloud_redteam
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 2547733802
    num_examples: 270351
  download_size: 900519483
  dataset_size: 2547733802
- config_name: core_wikis
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 368030997
    num_examples: 44160
  download_size: 82175469
  dataset_size: 368030997
- config_name: dfir_detection
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 541381934
    num_examples: 90481
  download_size: 191197045
  dataset_size: 541381934
- config_name: ics_scada
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 243297943
    num_examples: 26862
  download_size: 88171425
  dataset_size: 243297943
- config_name: kubernetes_container
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 555091963
    num_examples: 63232
  download_size: 210104862
  dataset_size: 555091963
- config_name: linux_unix
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 1045679383
    num_examples: 89682
  download_size: 388247604
  dataset_size: 1045679383
- config_name: mobile_wireless
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 1007349882
    num_examples: 95645
  download_size: 315184952
  dataset_size: 1007349882
- config_name: osint_recon
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 2351643
    num_examples: 850
  download_size: 1085598
  dataset_size: 2351643
- config_name: password_cracking
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 340784203
    num_examples: 141474
  download_size: 116324635
  dataset_size: 340784203
- config_name: phishing_se
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 374751470
    num_examples: 25199
  download_size: 127489943
  dataset_size: 374751470
- config_name: reversing_malware
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 1086556625
    num_examples: 156629
  download_size: 444408093
  dataset_size: 1086556625
- config_name: threat_intel
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 1283368209
    num_examples: 204353
  download_size: 441308074
  dataset_size: 1283368209
- config_name: training_blogs
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 222311513
    num_examples: 18974
  download_size: 103640197
  dataset_size: 222311513
- config_name: web_app
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 1350432930
    num_examples: 202902
  download_size: 481909682
  dataset_size: 1350432930
- config_name: windows_privesc
  features:
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: category
      dtype: string
    - name: timestamp
      dtype: string
    - name: language
      dtype: string
  splits:
  - name: train
    num_bytes: 27421179
    num_examples: 5747
  download_size: 13600773
  dataset_size: 27421179
---

# OffSec RedTeam Info

**OffSec RedTeam Info** is a SlimPajama‑style, category‑organized corpus of *security knowledge text* crawled from reputable red‑team/blue‑team websites: wikis, training blogs, vendor research, CERT advisories, reversing/malware labs, cloud/kubernetes posts, OSINT handbooks, AD tradecraft, and more.

**Token count:** ~1.646B tokens.

> ⚠️ **Ethical use only.** Use for research, education, and defensive security. Respect robots.txt, site terms, and copyrights. Do **not** misuse this corpus to harm systems or violate laws.

---

## What’s new (Nov 2025)

* **Per‑category Parquet configs** published for fast streaming via `datasets.load_dataset(...)`.
* **`raw/`**** JSONL** kept alongside Parquet for reproducibility and low‑level processing.
* **Consistent schema** across all categories (see below) with required `text` and `meta` keys.
* **Balanced shard sizes** (≈256–512 MB) to keep memory steady during load & push.

---

## Repository layout

```
/                          # dataset root (this card lives here as README.md)
  raw/                     # line-delimited JSON for each category (post-clean)
    <category>.jsonl       # e.g., raw/ad_ops.jsonl
  <category>/              # per-category Parquet config (train split)
    <category>.parquet     # e.g., ad_ops/ad_ops.parquet
```

> Parquet is present for non‑empty categories. Some categories may be JSONL‑only depending on the snapshot.

---

## Categories

* **core_wikis** – foundational red‑team/blue‑team references (ATT&CK, CAPEC/CWE, GTFOBins, LOLBAS, PayloadsAllTheThings, etc.).
* **web_app** – OWASP content, web vulns, API security, web‑sec blogs.
* **training_blogs** – walkthroughs, labs, CTF‑style posts and methodology.
* **ad_ops** – Active Directory/Windows internals, abuse paths, domain tradecraft.
* **windows_privesc**, **linux_unix** – OS‑specific privilege escalation & hardening.
* **cloud_redteam**, **kubernetes_container** – cloud & container security.
* **osint_recon**, **phishing_se** – OSINT techniques, social engineering.
* **c2_tradecraft** – C2 techniques, operator tradecraft (defensive write‑ups included).
* **mobile_wireless** – mobile, Wi‑Fi/Bluetooth/802.11 and radio‑adjacent topics.
* **ics_scada** – industrial control systems / OT security.
* **reversing_malware** – reversing & malware analysis posts from labs and vendors.
* **binary_exploitation** – pwn, exploitation notes, vuln research.
* **password_cracking** – hashcat/john guides, NIST/NCSC guidance.
* **dfir_detection** – incident response, detection engineering, Sigma, DFIR reports.
* **threat_intel** – vendor TI, advisories, newsroom items with technical depth.

> Exact category availability depends on the current revision (feeds change; some snapshots may be sparser).

---

## Schema (UPDATED)

Each record follows **exactly this** structure:

```json
{
  "text": "<cleaned article/content text>",
  "meta": {
    "url": "https://example.com/path",
    "title": "<page title>",
    "source": "example.com",
    "category": "ad_ops",
    "timestamp": "2025-11-02T22:21:39.384421+00:00",
    "language": "en"
  }
}
```

**Field definitions**

* **text** *(string)* — cleaned article/content text (readability‑style extraction, normalized whitespace).
* **meta** *(object)* — metadata container with the following keys:

  * **url** *(string)* — canonical URL of the item.
  * **title** *(string|null)* — page title.
  * **source** *(string|null)* — site/domain the content came from (e.g., `www.semperis.com`).
  * **category** *(string)* — logical bucket matching the config name (e.g., `ad_ops`).
  * **timestamp** *(string, ISO‑8601)* — fetch/process time for the item.
  * **language** *(string)* — language code (e.g., `en`).

> The sample you shared from Semperis conforms to this schema.

---

## Load examples

### Load a single category (Parquet, recommended)

```python
from datasets import load_dataset

REPO = "tandevllc/offsec_redteam_info"
cat = "web_app"  # pick any listed config

ds = load_dataset(REPO, name=cat, split="train")
print(len(ds), ds.column_names[:6])
print(ds[0]["text"][:400])
print(ds[0]["meta"]["url"])  # access metadata
```

### Load multiple categories and interleave

```python
from datasets import load_dataset, interleave_datasets

REPO = "tandevllc/offsec_redteam_info"
names = ["core_wikis", "training_blogs", "threat_intel"]
parts = [load_dataset(REPO, name=n, split="train") for n in names]

# Uniform interleave (good for blended training/eval)
blend = interleave_datasets(parts, probabilities=[1/len(parts)]*len(parts), seed=42)
```

### Filter typical research slices

```python
# Keep only long English articles
long_en = ds.filter(lambda r: (r.get("text") and len(r["text"]) > 1200) and ((r.get("meta") or {}).get("language") == "en"))

# Narrow to a specific source/domain
from_portswigger = ds.filter(lambda r: ((r.get("meta") or {}).get("source") or "").endswith("portswigger.net"))
```

### Load raw JSONL

```python
from datasets import load_dataset

raw = load_dataset(
    "json",
    data_files="raw/web_app.jsonl",
    repo_id="tandevllc/offsec_redteam_info",
    split="train",
)
```

---

## Cleaning & quality (high level)

* Content extracted with readability‑style heuristics; multi‑block merge when the best block is short.
* Basic quality gates: minimum words/sentences, alpha‑fraction, optional index‑page filtering by link density.
* Normalization: canonicalized URLs, per‑category dedup by link/content hash (some snapshots may apply global dedup).
* Non‑content and noisy paths avoided (search, feeds, asset dirs, etc.).

> These heuristics favor clean prose and technical material, but may still include boilerplate or miss embedded code blocks.

---

## Intended uses

* **Pretraining / continued pretraining** of security‑aware language models.
* **RAG / retrieval** over current security references and blogs, by category/site.
* **Evaluation** of security knowledge, extraction, summarization, and long‑context QA.
* **Trend analysis** across sources (pair with timestamps when present).

> This dataset is not a CVE ground‑truth database and does not replace vendor advisories.

---

## Limitations & caveats

* **Copyrights & terms apply.** Underlying website content retains the publisher’s license/terms.
* **Temporal drift.** Websites change; snapshots may vary; links can rot.
* **Extraction noise.** Readability may omit figures/code or include navigation text.
* **Metadata sparsity.** Some fields are missing for certain sources.

---

## License & access

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

**Underlying content:** remains under each site’s terms. For conservative use, store only links and your own embeddings/summaries.

**Commercial usage:** A paid **TanDev Commercial License** is available for commercial training/inference and internal derivatives. Contact [smridh@tandev.us](mailto:smridh@tandev.us) with organization, intended use, and deployment details.

**Takedowns:** If you own content included here and want it removed, please open an issue or email the maintainer.

---

## Citation

```bibtex
@dataset{tandevllc_2025_offsec_redteam_info,
  author = {Gupta, Smridh},
  title  = {OffSec RedTeam Info},
  year   = {2025},
  url    = {https://huggingface.co/datasets/tandevllc/offsec_redteam_info}
}
```

---

## Maintainer

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