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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 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}
}
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