--- 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. 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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 **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:** [WhitzardAgent](https://huggingface.co/WhitzardAgent) - **License:** Apache 2.0 — **academic, non-commercial use only** - **Languages:** English (primary), Chinese (secondary) - **Size:** ~10.5GB, 1.19M+ 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 # 878,815 records │ ├── cn_sec.jsonl # 58,408 records │ ├── framework.jsonl # 57,925 records │ ├── reference.jsonl # 44,453 records │ ├── ctf.jsonl # 43,174 records │ ├── tool.jsonl # 18,972 records │ ├── vuln_research.jsonl # 19,189 records │ ├── incident_response.jsonl # 18,049 records │ ├── bug_bounty.jsonl # 16,927 records │ ├── threat_intel.jsonl # 13,663 records │ ├── offsec.jsonl # 10,087 records │ ├── conference.jsonl # 4,514 records │ ├── books.jsonl # 3,124 records │ ├── news.jsonl # 2,100 records │ ├── ai_security.jsonl # 1,761 records │ └── ics_ot.jsonl # 944 records └── source_registry.json # Inventory of all sources with quality status ``` ### Data Instances Each JSONL record follows the `CyberRecord` schema: ```json { "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: ```python from datasets import load_dataset # Load a specific category ds = load_dataset("WhitzardAgent/CyberSecurity-1M", "vulnerability", split="train") # Load all categories ds = load_dataset("WhitzardAgent/CyberSecurity-1M", split="train") ``` ## Category Overview | Category | Records | Size | Description | |----------|---------|------|-------------| | vulnerability | 878,815 | 3.3GB | CVE records, security advisories, exploit databases | | cn_sec | 58,408 | 627MB | Chinese-language security content | | framework | 57,925 | 710MB | Security frameworks, detection rules, standards | | reference | 44,453 | 941MB | Documentation, cheat sheets, curated lists | | ctf | 43,174 | 1.6GB | CTF writeups and exercises | | tool | 18,972 | 166MB | Security tools, templates, cloud audit samples | | vuln_research | 19,189 | 734MB | Vulnerability research, CWE, detection rules (YARA, Elastic, Sigma) | | incident_response | 18,049 | 175MB | Incident response, DFIR, forensics, security log data | | bug_bounty | 16,927 | 364MB | Bug bounty writeups and HackerOne disclosures | | threat_intel | 13,663 | 991MB | Threat research, APT reports, IOCs (ThreatFox, MalwareBazaar, URLhaus, MISP) | | offsec | 10,087 | 565MB | Offensive security and penetration testing | | conference | 4,514 | 336MB | Security conference papers and presentations | | books | 3,124 | 301MB | OCR-extracted cybersecurity books | | news | 2,087 | 168MB | Security news and podcasts | | ai_security | 1,761 | 14MB | LLM/AI security, Web3 security | | ics_ot | 944 | 38MB | ICS/OT/SCADA security | ### Category Content Types Each category contains different *types of knowledge*. Understanding these types helps select the right data for your use case: | Category | Knowledge Type | Content Characteristics | Example Sources | |----------|---------------|------------------------|-----------------| | **vulnerability** | Structured factual data | CVE records with standardized fields (CVSS scores, affected products, references). Highly structured, ID-keyed, minimal prose. Good for lookup, classification, and vulnerability assessment training. | NVD, OSV, ExploitDB, GitHub Advisories, CISA KEV, vendor security bulletins | | **cn_sec** | Mixed (articles + tutorials) | Chinese-language security articles spanning vulnerability analysis, tool tutorials, and threat commentary. Mix of knowledge-type reference and step-by-step guides. Language is zh-CN. | Anquanke, SecWiki, Xianzhi, Seebug, Govuln, Kanxue, Tttang | | **reference** | Knowledge-type reference | Curated link collections (awesome-lists), cheat sheets, quick-reference cards, and documentation indexes. High link density, concise descriptions, broad coverage. Best for building knowledge graphs and resource discovery. | awesome-security, awesome-web-security, Gtfobins, Lolbas, Seclists, GitHub security markdown | | **framework** | Rule-based structured data | Detection rules (Sigma, YARA, Suricata/Snort ET Open), MITRE ATT&CK mappings, OWASP standards (ASVS, WSTG, CheatSheetSeries, Top10), and security standards/benchmarks (CIS, NIST). Highly structured, machine-parseable format. Ideal for rule generation and detection engineering. | SigmaHQ, MITRE ATT&CK, CAPEC, YARA rules, ET Open rules, capa rules, CIS benchmarks, OWASP projects | | **ctf** | Tutorial/procedural | Step-by-step CTF writeups with detailed exploitation chains, debugging notes, and solution walkthroughs. Rich procedural reasoning — shows *how to think* about a problem, not just the answer. | CTFtime, CTFSearch, CTF Wiki, LiveOverflow, PentesterLand, Oxdf | | **tool** | Code + documentation | Security tool configurations (Nuclei templates, Metasploit modules), scanner rules, and tool documentation. Mixed code-and-prose format. Useful for tool usage training and rule authoring. | Nuclei templates, Metasploit, Loldrivers, CloudGoat, Kubesploit | | **incident_response** | Procedural + knowledge + log data | DFIR playbooks, forensic analysis guides, memory forensics documentation, attack sample datasets (EVTX, PCAP), and security log data (Splunk BOTS, Mordor, EVTX-ATTACK-SAMPLES). Mix of step-by-step procedures, reference knowledge, and raw log samples for training. | Volatility, FLARE-FLOSS, Mordor datasets, Malware Traffic Analysis, DFIR KB, Splunk BOTS, Security Log Data | | **offsec** | Tutorial/procedural | Red team infrastructure guides, penetration testing methodology docs, attack chain walkthroughs, and offensive tool usage. Strong procedural content showing *step-by-step exploitation*. | HackTricks, IRedTeam, PentestBook, PSTips, Offensive Security KB, Red Team Cheatsheets | | **threat_intel** | Analytical/report + IOC data | APT campaign analysis, malware family reports, threat landscape assessments, and structured IOC collections (ThreatFox, MalwareBazaar, URLhaus, MISP OSINT feeds). Mix of long-form analytical prose with TTP mapping and structured indicator data. Best for threat reasoning, report summarization, and IOC extraction. | Unit42, Kaspersky, CrowdStrike, ESET, Check Point, Mandiant, ThreatFox, MalwareBazaar, URLhaus, MISP feeds | | **bug_bounty** | Tutorial/procedural | Bug bounty writeups with reproduction steps, bypass techniques, and bounty amounts. Step-by-step vulnerability discovery narratives. Excellent for vulnerability discovery training. | PentesterLand, HackerOne Hacktivity, Bug Bounty KB | | **vuln_research** | Analytical/deep-dive + rule data | In-depth vulnerability analysis, exploit development writeups, reverse engineering case studies, CWE weakness taxonomy, and detection rules (YARA, Elastic, Sigma, capa). Mix of analytical prose and structured rule/code content. Shows *why* a vulnerability exists and *how* it was found and detected. | Trail of Bits, PortSwigger Research, Google Project Zero, GH Security Lab, full-disclosure, CWE KB, YARA Rules, Elastic Detection Rules, Sigma | | **books** | Long-form knowledge | Full-length cybersecurity books (OCR-extracted from PDFs). Covers topics from network security to cryptography to forensics. Long coherent text ideal for domain pre-training. | Local book collection | | **conference** | Academic/presentation | Security conference papers (USENIX Security, NDSS) and presentation slides (DEF CON, BlackHat). Mix of rigorous academic prose and slide-format bullet points. | USENIX papers, NDSS papers, DEF CON media, security conference repos | | **news** | Factual/news | Security industry news articles, breach reports, and podcast transcripts. Timely, event-driven content. Good for security awareness and event classification. | Krebs on Security, Security Now, Dark Reading, Darknet Diaries, BleepingComputer | | **ai_security** | Research/tutorial | LLM security research (jailbreaks, adversarial prompts), AI vulnerability scanning tools, and Web3/smart-contract security. Emerging domain with mixed analytical and procedural content. | LLM-attacks, NVIDIA garak, GPTFuzz, Guardrails, Web3 Security KB | | **ics_ot** | Specialized reference | Industrial control system security: SCADA protocols, OT pentesting guides, ICS-specific detection rules. Niche domain with specialized terminology and procedures. | ICS Security Tools, Redpoint, ThreatHunting Keywords, GRASSMARLIN | ### Content Type Legend - **Structured factual data**: Machine-parseable records with standardized fields (CVE, CVSS, CPE). Low prose density, high lookup value. - **Knowledge-type reference**: Curated lists, documentation, cheat sheets. Broad coverage, concise entries, high link density. - **Rule-based structured data**: Detection rules, security policies, compliance benchmarks. Machine-parseable format (YAML, Sigma, YARA). - **Tutorial/procedural**: Step-by-step walkthroughs, exploitation chains, and debugging narratives. Shows *how* to accomplish a task. - **Analytical/report**: Threat research reports, APT analysis, and vulnerability deep-dives. Long-form reasoning about *why* and *what it means*. - **Academic/presentation**: Peer-reviewed papers and conference slides. Rigorous methodology, formal language. - **Long-form knowledge**: Full books and extended reference works. Coherent narrative across chapters. - **Factual/news**: Time-bound event reporting, breach disclosures, industry updates. - **Specialized reference**: Domain-specific (ICS/OT) reference materials with niche terminology. - **Research/tutorial**: Emerging domain content mixing analytical findings and practical techniques. ## 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 (~879K) dominates at ~74%, though threat_intel (~14K), vuln_research (~19K), and framework (~58K) have grown significantly with new IOC data, detection rules, and CWE/OWASP content ## Citation ```bibtex @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/WhitzardAgent/CyberSecurity-1M} } ```