Duplicate from Manusagents/cybersecurity-qa-v2

Co-authored-by: SHS Lab <Manusagents@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+task_categories:
+- question-answering
+- text-generation
+language:
+- en
+tags:
+- cybersecurity
+- security
+- nvd
+- cve
+- mitre-attack
+- cwe
+- fine-tuning
+- instruction-tuning
+size_categories:
+- 1M<n<10M
+---
+
+# Cybersecurity Q&A Dataset v2 — 2.6M Examples
+
+A large-scale cybersecurity Q&A dataset for fine-tuning LLMs on security topics.
+**2,621,468 examples** covering vulnerabilities, attack techniques, weaknesses, and defensive strategies.
+
+## Statistics
+
+| Source | Examples | Description |
+|--------|----------|-------------|
+| NIST NVD CVE Database | ~1,954,225 | All CVEs (2002–2025): overview, severity, detection, remediation |
+| AlicanKiraz0/All-CVE-Records-Training-Dataset | ~297,441 | Detailed CVE analysis with markdown formatting |
+| ethanolivertroy/nist-cybersecurity-training | ~268,147 | NIST cybersecurity training data |
+| AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.1 | ~98,134 | Causal reasoning & threat analysis |
+| MITRE ATT&CK Enterprise | ~2,127 | 709 techniques: explanations, detection, mitigations |
+| MITRE CWE Database | ~3,848 | 969 weakness types: descriptions, remediation |
+| Handcrafted (DevSecOps, red team, privesc) | 709 | Expert-curated deep-dive Q&As |
+| **Total (deduplicated)** | **2,621,468** | |
+
+## Format
+
+Each example has a `messages` field with system / user / assistant roles:
+
+```json
+{
+  "messages": [
+    {"role": "system",    "content": "You are an expert cybersecurity analyst..."},
+    {"role": "user",      "content": "What is CVE-2021-44228 and what systems are affected?"},
+    {"role": "assistant", "content": "CVE-2021-44228 (Log4Shell) is a critical RCE vulnerability..."}
+  ]
+}
+```
+
+Compatible with Gemma 4, Llama 3, Mistral, and any model using the `messages` format.
+
+## Topics
+
+- **CVE Analysis (1.95M)**: Every CVE in the NVD database — overview, type (CWE), affected products, CVSS severity, attack vectors, detection strategies, and remediation steps
+- **Detailed CVE Intelligence (297K)**: Comprehensive CVE analysis with exploitation vectors, CVSS breakdown, and markdown-formatted technical deep-dives
+- **MITRE ATT&CK (2K)**: All 709 Enterprise techniques — mechanics, detection, mitigation, threat actor usage
+- **MITRE CWE (4K)**: All 969 software weakness types — root causes, code examples, SAST detection, secure coding fixes
+- **NIST Training (268K)**: Cybersecurity framework, risk management, security controls
+- **Fenrir Threat Analysis (98K)**: Causal reasoning about attack chains, purple team exercises, bypass techniques
+- **DevSecOps (709)**: Docker, Kubernetes, CI/CD, cloud IAM, Terraform security
+
+## Fine-Tuned Models
+
+These models were trained on this dataset:
+
+| Model | Specialization |
+|-------|----------------|
+| [gemma4-e2b-cybersecurity-interview](https://huggingface.co/rezaduty/gemma4-e2b-cybersecurity-interview) | General cybersecurity |
+| [gemma4-e2b-docker-container-security](https://huggingface.co/rezaduty/gemma4-e2b-docker-container-security) | Docker & Container Security |
+| [gemma4-e2b-kubernetes-security](https://huggingface.co/rezaduty/gemma4-e2b-kubernetes-security) | Kubernetes Security |
+| [gemma4-e2b-ai-llm-security](https://huggingface.co/rezaduty/gemma4-e2b-ai-llm-security) | AI & LLM Security |
+| [gemma4-e2b-cloud-iam-terraform](https://huggingface.co/rezaduty/gemma4-e2b-cloud-iam-terraform) | Cloud IAM & Terraform |
+| [gemma4-e2b-redteam-activedirectory](https://huggingface.co/rezaduty/gemma4-e2b-redteam-activedirectory) | Active Directory Red Team |
+| [gemma4-e2b-mimikatz-credential-theft](https://huggingface.co/rezaduty/gemma4-e2b-mimikatz-credential-theft) | Mimikatz & Credential Theft |
+| [gemma4-e2b-active-directory-ttps](https://huggingface.co/rezaduty/gemma4-e2b-active-directory-ttps) | Active Directory TTPs |
+| [gemma4-e2b-privesc-windows](https://huggingface.co/rezaduty/gemma4-e2b-privesc-windows) | Windows Privilege Escalation |
+| [gemma4-e2b-privesc-linux](https://huggingface.co/rezaduty/gemma4-e2b-privesc-linux) | Linux Privilege Escalation |
+| [gemma4-e2b-privesc-macos](https://huggingface.co/rezaduty/gemma4-e2b-privesc-macos) | macOS Privilege Escalation |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("rezaduty/cybersecurity-qa-v2", split="train")
+print(f"Dataset size: {len(ds):,}")
+print(ds[0])
+```
+
+## License
+
+Apache 2.0. Sources used:
+- NIST NVD: Public domain (US government)
+- MITRE ATT&CK: CC BY 4.0
+- MITRE CWE: CC BY 4.0
+- AlicanKiraz0 datasets: MIT
+- Fenrir v2.1: MIT
+- NIST training dataset: MIT
+
+## Generated
+
+2026-06-04 · Automated pipeline from public security databases

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