Buckets:
| license: apache-2.0 | |
| task_categories: | |
| - text-generation | |
| - question-answering | |
| language: | |
| - en | |
| size_categories: | |
| - 1K<n<10K | |
| pretty_name: CyberSec-Reasoning-Premium | |
| tags: | |
| - cybersecurity | |
| - security | |
| - reasoning | |
| - chain-of-thought | |
| - cot | |
| - ctf | |
| - vulnerability | |
| - red-team | |
| - blue-team | |
| - sft | |
| - fine-tuning | |
| - distillation | |
| - synthetic | |
| - defensive-security | |
| - pentest | |
| - kimik3 | |
| - agents | |
| # 🛡️ CyberSec-Reasoning-Premium | |
|  | |
| A **rigorously cleaned, English-only** cybersecurity reasoning SFT dataset of **3,067 chain-of-thought traces**, covering offensive, defensive, and CTF domains. Part of the Premium series, upholding the standards of [fable-5-premium](https://huggingface.co/datasets/saidutta69/fable-5-premium), [fable-5-premium-v2](https://huggingface.co/datasets/saidutta69/fable-5-premium-v2), and [fable-5.1-premium](https://huggingface.co/datasets/saidutta69/fable-5.1-premium). | |
| > **Priorities:** Quality > Ease of Access > Quantity | |
| ## 📊 Dataset Overview | |
| Property | Value | |
| --- | --- | |
| **Total Traces** | 3,067 | |
| **Train Split** | 2,606 (85.0%) | |
| **Validation Split** | 230 (7.5%) | |
| **Test Split** | 231 (7.5%) | |
| **Average Quality** | 0.74 (0.5–1.0 band) | |
| **Language** | English only (CJK-filtered) | |
| **License** | Apache-2.0 (per-source attribution below) | |
| **Created** | 2026-09-20 | |
| ## 🔗 Sources | |
| Source | Raw | Kept | License | Description | |
| --- | --- | --- | --- | --- | |
| dpevzner (Cybersecurity_Reasoning_Dataset) | 17,991 | 1,588 | Apache-2.0 | Canonical expert cyber reasoning corpus — 9,870 internal dups collapsed, 3 template formats (ChatML, User/Assistant, Instruction/Response) normalized to messages | |
| echel0nn1881 (kimi-cyber-reasoning) | 997 | 997 | WTFPL | Kimi K3 CoT across 13 cybersecurity disciplines and 4 systems-engineering domains | |
| verno-labs (financial-ai-ctf-dataset) | 400 | 333 | CC-BY-4.0 | Adversarial prompt-injection CTF defense conversations (red vs blue) | |
| davidquicast (vulnerability-intelligence-diagrammatic-reasoning) | 107 | 107 | Apache-2.0 | CVE deep-dive reasoning: socratic, logical, diagrammatic, and abductive chains | |
| Nanhang (ctf-dataset) | 2,763 | 42 | MIT | CTF task corpus — 95% Chinese, English-only rows kept | |
| **Excluded:** Vyber07/cyber-security | — | — | Apache-2.0 | Gated (auto) at build time — not redistributable until access is accepted | |
| **Excluded:** expertdata-factory/cybersecurity-reasoning-cot-v1 | — | — | rights-review-required | Source flags unverified redistribution rights — dropped for licensing hygiene | |
| ## 🧹 Quality Pipeline | |
| 1. **Deduplication** — Cross-source SHA-256 hashing on normalized messages JSON (9,870+ exact dups collapsed) | |
| 2. **Language Filtering** — CJK-ratio check; rows >5% Chinese dropped for English-only consistency | |
| 3. **Template Normalization** — ChatML / User-Assistant / Instruction-Response raw text parsed into clean messages | |
| 4. **PII Scrubbing** — Local paths, API keys (sk-*, hf_*, AKIA), emails, IPs replaced with [REDACTED_*] | |
| 5. **Content Filtering** — Refusal patterns and short (<300 char) records removed | |
| 6. **Quality Scoring** — Length, structure, and depth composite; sub-0.5 traces dropped (mean 0.74) | |
| ## 📈 Domain Coverage | |
| pwn · crypto · reverse · heap · kernel · misc · SOC threat intel · software engineering · web · network · forensics · prompt-injection defense · CVE analysis · vulnerability triage | |
| ## 🎯 Usage | |
| ### With Hugging Face Datasets | |
| ```python | |
| from datasets import load_dataset | |
| ds = load_dataset("saidutta69/CyberSec-Reasoning-Premium", data_files="openai_chat/train.parquet", split="train") | |
| ``` | |
| ### With Axolotl | |
| ```yaml | |
| datasets: | |
| - path: saidutta69/CyberSec-Reasoning-Premium | |
| type: chat_template | |
| data_files: openai_chat/train.parquet | |
| split: train | |
| ``` | |
| ## 🧰 Schema | |
| Both formats (`openai_chat/` and `agent_traces/`) carry identical rows: `messages` (JSON string), `model`, `source`, `domain`, `session_id`, `quality_scores` (JSON string). | |
| ## 🧾 Provenance | |
| Built from the listed public sources through the premium cleaning pipeline; full build stats shipped in `build_stats.json`. Companion datasets: [fable-5-premium](https://huggingface.co/datasets/saidutta69/fable-5-premium) · [fable-5-premium-v2](https://huggingface.co/datasets/saidutta69/fable-5-premium-v2) · [fable-5.1-premium](https://huggingface.co/datasets/saidutta69/fable-5.1-premium). | |
| > ⚠️ **Intended use:** defensive security research and model training. Educational/research framing throughout; no operational exploitation guidance is included beyond what sources already provide publicly. | |
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