---
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

![RACER IS OP](https://photu.kashyalabanavli.site/racer-is-op.png)

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.