README.md

---
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configs:
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license: odc-by
task_categories:
- text-generation
language:
- en
tags:
- cybersecurity
- pretraining
pretty_name: RedSage-CFW
size_categories:
- 10M<n<100M
---

# Dataset Card for RedSage-CFW

<p align="center">
  <b> RedSage: A Cybersecurity Generalist LLM" (ICLR 2026). </b>
  <br>
  <b>Authors:</b> Naufal Suryanto<sup>1</sup>, Muzammal Naseer<sup>1†</sup>, Pengfei Li<sup>1</sup>, Syed Talal Wasim<sup>2</sup>, Jinhui Yi<sup>2</sup>, Juergen Gall<sup>2</sup>, Paolo Ceravolo<sup>3</sup>, Ernesto Damiani<sup>3</sup>
  <br>
  <sup>1</sup>Khalifa University, <sup>2</sup>University of Bonn, <sup>3</sup>University of Milan  
  <br>
  <sup>†</sup>Project Lead
  <br>
  <br>
  <a href="https://openreview.net/forum?id=W4FAenIrQ2"><img src="https://img.shields.io/badge/Paper-OpenReview-B31B1B.svg"></a>
  <a href="https://huggingface.co/RISys-Lab"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-RISys--Lab-orange"></a>
  <br>
  🌐 <a href="https://risys-lab.github.io/RedSage/">Project Page</a>&nbsp;&nbsp;|&nbsp;&nbsp;
  🤖 <a href="https://huggingface.co/collections/RISys-Lab/redsage-models">Model Collection</a>&nbsp;&nbsp;|&nbsp;&nbsp;
  📊 <a href="https://huggingface.co/collections/RISys-Lab/redsage-benchmarks">Benchmark Collection</a>&nbsp;&nbsp;|&nbsp;&nbsp;
  📘 <a href="https://huggingface.co/collections/RISys-Lab/redsage-datasets">Data Collection </a>
  
  
</p>

****


## Dataset Description

* **Developed by:** RISysLab
* **Repository:** [GitHub](https://github.com/RISys-Lab/RedSage)
* **Paper:** [RedSage: A Cybersecurity Generalist LLM](https://openreview.net/forum?id=W4FAenIrQ2)
* **Arxiv:** https://arxiv.org/abs/2601.22159

### Dataset Summary

**RedSage-CFW** (CyberFineWeb) is a large-scale, cybersecurity dataset designed for the continual pretraining of Large Language Models (LLMs). It consists of approximately **11.7 billion tokens** spanning **13 million documents**.

The dataset was constructed by filtering the **FineWeb** corpus (Common Crawl 2013–2024) using a custom ModernBERT-based classifier to identify cybersecurity-relevant content. To prevent catastrophic forgetting of general capabilities during pretraining, the cybersecurity data is mixed with general educational content from **FineWeb-Edu**.

### Supported Tasks

* **Continual Pretraining:** Designed to adapt general-purpose LLMs (e.g., Qwen, Llama) to the cybersecurity domain.
* **Domain Adaptation:** Enhances model performance on cybersecurity knowledge, skills, and tool usage

### Languages

The dataset primarily consists of English text, derived from Common Crawl sources.

## Dataset Structure

### Data Instances

The dataset is partitioned into 5 chunks (config names: `chunk_1` through `chunk_5`). Each instance represents a single document (e.g., a web page, article, or forum post).

### Data Fields

Based on the provided configuration, the data fields are:

* **`text`** (string): The full text content of the document.
* **`id`** (string): A unique identifier for the document.
* **`metadata`** (struct): Contains detailed attributes about the source and filtering:
* `probability` (float64): The confidence score from the cybersecurity classifier.
* `relevant` (bool): A flag indicating if the document passed the relevance filter.
* `url` (string): The source URL of the document.
* `date` (timestamp): The crawl or publication date.
* `dump` (string): The Common Crawl dump identifier (e.g., `CC-MAIN-2024-51`).
* `file_path` (string): Path information for the original file.
* `language` (string): The detected language of the text.
* `language_score` (float64): Confidence score of the language detection.
* `token_count` (int64): The number of tokens in the document.
* `score`, `int_score`: Additional quality or relevance metrics.

### Data Splits

The dataset is segmented into 5 chunks. The paper notes that the final corpus consists of the "latest 5 chunks" from the filtered pipeline to fit training budgets.

* **Total Size:** ~11.7B tokens.
* **Total Documents:** ~13M documents.

## Dataset Creation

### Curation Rationale

Existing cybersecurity solutions often rely on proprietary APIs or lack domain adaptation. RedSage-CFW bridges this gap by providing a transparent, open-source corpus for training local, privacy-preserving cybersecurity assistants.

### Source Data

* **FineWeb:** The base corpus is FineWeb, aggregated from 104 Common Crawl subsets between Summer 2013 and December 2024 (~17.2T tokens).
* **FineWeb-Edu:** Used for mixing general knowledge to maintain reasoning capabilities.

### Data Processing & Filtering

1. **Classifier Training:** A binary classifier based on **ModernBERT-base** was trained on the "Cybersecurity Topic Classification" dataset (sourced from Reddit, StackExchange, and arXiv). It achieved 97.3% accuracy on validation.
2. **Filtering:** This classifier was applied to FineWeb, identifying ~125M cybersecurity-relevant documents (~89.8B tokens).
3. **General Knowledge Replay:** To avoid catastrophic forgetting, the cybersecurity data was mixed with FineWeb-Edu samples at a **30% replay ratio**.
4. **Deduplication:** Global deduplication was performed using MinHash-LSH (via DataTrove), reducing the corpus size by ~47.9% in tokens.
5. **Chunking:** The final dataset comprises the latest 5 chronological chunks from the processed data to manage computational costs.


## Considerations for Using the Data

### Social Impact

The dataset enables the development of open-source cybersecurity assistants, potentially helping to bridge the global skills shortage in the field.

### Discussion of Biases and Limitations

* **Source Bias:** As a web-crawled dataset, it may inherit biases present in Common Crawl and online cybersecurity discussions.
* **Dual Use:** The dataset may contains offensive security knowledge (e.g., penetration testing techniques). While intended for defense, there is an inherent risk of misuse.

---

## Citation

```bibtex
@inproceedings{suryanto2026redsage,
  title={RedSage: A Cybersecurity Generalist {LLM}},
  author={Naufal Suryanto and Muzammal Naseer and Pengfei Li and Syed Talal Wasim and Jinhui Yi and Juergen Gall and Paolo Ceravolo and Ernesto Damiani},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
  url={https://openreview.net/forum?id=W4FAenIrQ2}
}

```