README.md

metadata

language:
  - en
  - zh
license: apache-2.0
extra_gated_prompt: >-
  This dataset is provided strictly for academic, non-commercial research
  purposes only. It contains cybersecurity content including vulnerability
  details, exploit techniques, and offensive security information. By requesting
  access, you confirm that you will use this dataset solely for lawful academic
  research, education, or defensive security purposes, and will not use it for
  any commercial or illegal activities. Access requests using personal email
  addresses (Gmail, Outlook, QQ, etc.) will be rejected — please use your
  institutional/organizational email.
extra_gated_fields:
  Full Name: text
  Institution/Organization: text
  Country: country
  Institutional Email: text
  Intended Use: text
  I confirm that I will use this dataset solely for academic, non-commercial research or education purposes, and I will not redistribute, sell, or use it for any unlawful activities: checkbox
extra_gated_heading: Access Request (Academic Use Only)
extra_gated_description: >-
  This dataset is available exclusively for academic and non-commercial
  research. Please provide your institutional email and affiliation below.
  Requests from non-institutional email addresses will not be approved. Review
  typically takes 1-2 business days.
extra_gated_button_content: Request Access
license_details: >-
  The dataset compilation is released under Apache 2.0 for academic,
  non-commercial use. Individual content items retain their original source
  licensing. Users must verify licensing terms before redistributing any
  specific content. Commercial use is prohibited without explicit authorization.
annotations_creators:
  - machine-generated
language_creators:
  - machine-generated
pretty_name: CyberSecurity-1M
tags:
  - cybersecurity
  - vulnerability-research
  - threat-intelligence
  - incident-response
  - security
  - forensics
  - ai-security
task_categories:
  - text-generation
  - summarization
  - question-answering
  - text-classification
task_ids:
  - language-modeling
  - news-articles-summarization
  - open-domain-qa
  - topic-classification
source_datasets:
  - original
size_categories:
  - 1M<n<10M
configs:
  - config_name: vulnerability
    data_files:
      - split: train
        path: merged/vulnerability.jsonl
  - config_name: ctf
    data_files:
      - split: train
        path: merged/ctf.jsonl
  - config_name: framework
    data_files:
      - split: train
        path: merged/framework.jsonl
  - config_name: reference
    data_files:
      - split: train
        path: merged/reference.jsonl
  - config_name: cn_sec
    data_files:
      - split: train
        path: merged/cn_sec.jsonl
  - config_name: books
    data_files:
      - split: train
        path: merged/books.jsonl
  - config_name: threat_intel
    data_files:
      - split: train
        path: merged/threat_intel.jsonl
  - config_name: tool
    data_files:
      - split: train
        path: merged/tool.jsonl
  - config_name: offsec
    data_files:
      - split: train
        path: merged/offsec.jsonl
  - config_name: incident_response
    data_files:
      - split: train
        path: merged/incident_response.jsonl
  - config_name: vuln_research
    data_files:
      - split: train
        path: merged/vuln_research.jsonl
  - config_name: news
    data_files:
      - split: train
        path: merged/news.jsonl
  - config_name: ics_ot
    data_files:
      - split: train
        path: merged/ics_ot.jsonl
  - config_name: conference
    data_files:
      - split: train
        path: merged/conference.jsonl
  - config_name: bug_bounty
    data_files:
      - split: train
        path: merged/bug_bounty.jsonl
  - config_name: ai_security
    data_files:
      - split: train
        path: merged/ai_security.jsonl
dataset_info:
  features:
    - name: id
      dtype: string
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: _original_source
      dtype: string
    - name: url
      dtype: string
    - name: category
      dtype: string
    - name: cve
      dtype: string
    - name: author
      dtype: string
    - name: date
      dtype: string
    - name: tags
      sequence: string
    - name: description
      dtype: string
    - name: markdown
      dtype: string
    - name: exploit_code
      dtype: string
    - name: bounty
      dtype: string
    - name: extra
      struct:
        - name: feed_url
          dtype: string
    - name: scraped_at
      dtype: string
    - name: schema_version
      dtype: int32

CyberSecurity-1M

A large-scale, multi-source cybersecurity knowledge dataset containing 1.1M+ records across 16 categories, collected exclusively for academic, non-commercial research purposes.

Disclaimer: This dataset is provided for academic research only. All content is aggregated from publicly available sources. The views, opinions, and information expressed in the dataset content do not represent the views or positions of the research team. The research team does not endorse, support, or take responsibility for any of the content. Users access and use this dataset at their own risk.

Security Notice: This dataset contains information about cybersecurity vulnerabilities, exploitation techniques, and offensive security methods. This information is already publicly available and is collected here solely for defensive security research and education. Misuse of this information to attack systems without authorization is illegal. Users must comply with all applicable laws and regulations.

Dataset Description

• Curated by: morinoppp
• License: Apache 2.0 — academic, non-commercial use only
• Languages: English (primary), Chinese (secondary)
• Size: ~9.1GB, 1.11M+ records
• Format: JSON Lines (JSONL)

Dataset Summary

CyberSecurity-1M aggregates publicly available cybersecurity content from diverse sources into a unified, categorized, and quality-annotated dataset. It is designed to support cybersecurity research, LLM fine-tuning for security domains, threat intelligence analysis, and security education.

The dataset covers the full spectrum of cybersecurity knowledge:

• Vulnerability databases (CVE records, security advisories)
• Threat intelligence (APT reports, malware analysis, IOCs)
• Offensive security (penetration testing, red teaming)
• Defensive security (incident response, detection rules, forensics)
• Security frameworks (attack frameworks, detection rulesets)
• CTF & training (CTF writeups, exercises, tutorials)
• Security tools (templates, modules, exploit code)
• Reference materials (cheat sheets, documentation, curated lists)
• Chinese-language security community content
• Books & conference talks (OCR-extracted PDFs, presentation transcripts)

Supported Tasks

• Language modeling / text generation: Pre-train or fine-tune LLMs on cybersecurity domain text
• Summarization: Generate summaries of threat reports, vulnerability advisories
• Question answering: Build cybersecurity QA systems over the knowledge base
• Text classification: Categorize security content by type, severity, or topic
• Information extraction: Extract IOCs, CVEs, TTPs from unstructured text
• Retrieval-augmented generation (RAG): Use as a knowledge base for security-focused systems

Dataset Structure

CyberSecurity-1M/
├── merged/                          # Categorized & merged data (16 categories)
│   ├── vulnerability.jsonl          # ~876K records
│   ├── ctf.jsonl                    # ~74K records
│   ├── cn_sec.jsonl                 # ~50K records
│   ├── reference.jsonl              # ~48K records
│   ├── tool.jsonl                   # ~17K records
│   ├── incident_response.jsonl      # ~13K records
│   ├── framework.jsonl              # ~7.8K records
│   ├── bug_bounty.jsonl             # ~6.4K records
│   ├── threat_intel.jsonl           # ~5.1K records
│   ├── offsec.jsonl                 # ~4.9K records
│   ├── books.jsonl                  # ~3.3K records
│   ├── vuln_research.jsonl          # ~1.8K records
│   ├── news.jsonl                   # ~846 records
│   ├── ics_ot.jsonl                 # ~767 records
│   ├── conference.jsonl             # ~353 records
│   └── ai_security.jsonl            # ~207 records
└── source_registry.json             # Inventory of all sources with quality status

Data Instances

Each JSONL record follows the CyberRecord schema:

{
  "id": "a1b2c3d4e5f6",
  "title": "CVE-2024-1234: Remote Code Execution in Framework X",
  "source": "vuln_db",
  "_original_source": "vuln_db",
  "url": "https://example.com/advisory/CVE-2024-1234",
  "category": "vulnerability",
  "cve": "CVE-2024-1234",
  "author": null,
  "date": "2024-03-15",
  "tags": ["rce", "critical"],
  "description": "A remote code execution vulnerability exists in...",
  "markdown": "# CVE-2024-1234\n\n## Description\nA remote code execution vulnerability...",
  "exploit_code": null,
  "bounty": null,
  "extra": {},
  "scraped_at": "2026-05-08T12:00:00+00:00",
  "schema_version": 1
}

Data Fields

Field	Type	Description
id	string	Unique record identifier
title	string	Record title
source	string	Source identifier
_original_source	string	Same as source; preserved for provenance after merging
url	string	Original URL of the content
category	string	Category name (one of 16 categories)
cve	string	CVE identifier, if applicable
author	string	Author name(s)
date	string	Publication date
tags	list[string]	Content tags/topics
description	string	Brief description/summary
markdown	string	Full text content in markdown format
exploit_code	string	Source code / payload content (for tool sources)
bounty	string	Bug bounty amount (for bug_bounty category)
extra	object	Source-specific metadata
scraped_at	string	ISO 8601 timestamp of when the record was collected
schema_version	int	Schema version (currently 1)

Data Splits

This dataset uses a single train split. Records are organized into 16 category-based configurations (see configs in YAML metadata). Each configuration can be loaded independently:

from datasets import load_dataset

# Load a specific category
ds = load_dataset("morinoppp/CyberSecurity-1M", "vulnerability", split="train")

# Load all categories
ds = load_dataset("morinoppp/CyberSecurity-1M", split="train")

Category Overview

Category	Records	Size	Description
vulnerability	~876K	3.1GB	CVE records, security advisories
ctf	~74K	1.9GB	CTF writeups and exercises
cn_sec	~50K	515MB	Chinese-language security content
reference	~48K	626MB	Documentation, cheat sheets, curated lists
tool	~17K	146MB	Security tools and templates
incident_response	~13K	64MB	Incident response and forensics
framework	~7.8K	1.2GB	Security frameworks and detection rulesets
bug_bounty	~6.4K	255MB	Bug bounty writeups
threat_intel	~5.1K	641MB	Threat research and APT reports
offsec	~4.9K	71MB	Offensive security and penetration testing
books	~3.3K	407MB	OCR-extracted cybersecurity books
vuln_research	~1.8K	80MB	Vulnerability research and analysis
news	~846	68MB	Security news
ics_ot	~767	35MB	ICS/OT/SCADA security
conference	~353	0.2MB	Security conference presentations
ai_security	~207	2.3MB	LLM/AI security research

Considerations

Academic Use Only

This dataset is compiled and distributed strictly for academic, non-commercial research purposes. Any commercial use, redistribution for profit, or application in commercial products is strictly prohibited without explicit written authorization. The research team receives no financial benefit from this dataset.

Disclaimer

The content in this dataset is aggregated from publicly available sources and represents the views of the original authors, not the research team. The research team:

• Does not endorse, verify, or guarantee the accuracy of any content
• Does not take responsibility for any claims, opinions, or information in the dataset
• Does not encourage or support the use of this information for unauthorized access or illegal activities
• Makes no warranties, express or implied, regarding the dataset's fitness for any particular purpose

Security Risk Notice

This dataset contains technical information about vulnerabilities, exploitation methods, and offensive security techniques. While this information is already publicly available, users should be aware that:

• Unauthorized use of exploit techniques against systems you do not own or have explicit permission to test is illegal in most jurisdictions
• Responsible disclosure practices should be followed when discovering new vulnerabilities
• Users must comply with all applicable local, national, and international laws
• The dataset should only be used to improve defensive security capabilities

Licensing

The dataset compilation is released under Apache 2.0 for academic, non-commercial use. Individual content items retain their original source licensing. Some content may have specific terms of service or attribution requirements — users must verify licensing for specific content before any redistribution. Commercial use is prohibited.

Biases

• Language bias: English content dominates (80%), with Chinese as secondary (18%)
• Source bias: Content reflects the perspectives and coverage of the original sources
• Recency bias: Content is more complete for recent years
• Category imbalance: Vulnerability data (~876K) vastly outnumbers other categories

Citation

@dataset{cybersecurity-1m,
  title={CyberSecurity-1M: A Large-Scale Multi-Source Cybersecurity Knowledge Dataset},
  author={WhitzardAgent Team (SIIxFudan)},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/morinoppp/CyberSecurity-1M}
}