Buckets:
| license: mit | |
| task_categories: | |
| - text-generation | |
| - question-answering | |
| language: | |
| - en | |
| size_categories: | |
| - 10K<n<100K | |
| pretty_name: RedTeam-Premium | |
| tags: | |
| - red-team | |
| - offensive-security | |
| - penetration-testing | |
| - cybersecurity | |
| - vulnerability-research | |
| - ctf | |
| - security | |
| - sft | |
| - fine-tuning | |
| - distillation | |
| - alpaca | |
| - knowledge-base | |
| - mit-license | |
| # 🗡️ RedTeam-Premium | |
|  | |
| A **deduplicated, quality-filtered, instruction-SFT-ready** red-team dataset of **19,033 traces**, converted from the raw WNT3D Ultimate Red Team collection into a single clean format. Part of the Premium series — see [fable-5-premium](https://huggingface.co/datasets/saidutta69/fable-5-premium), [fable-5.1-premium](https://huggingface.co/datasets/saidutta69/fable-5.1-premium), [CyberSec-Reasoning-Premium](https://huggingface.co/datasets/saidutta69/CyberSec-Reasoning-Premium), [Kimi-K3-Premium](https://huggingface.co/datasets/saidutta69/Kimi-K3-Premium), and [Qwen3.8-Agent-Premium](https://huggingface.co/datasets/saidutta69/Qwen3.8-Agent-Premium). | |
| > **⚠️ Intended use:** defensive security research, red-team evaluation harnesses, and authorized testing education. Do not use for unauthorized access against systems you do not own or have permission to test. | |
| > **Priorities:** Quality > Ease of Access > Quantity | |
| ## 📊 Dataset Overview | |
| Property | Value | |
| --- | --- | |
| **Total Traces** | 19,033 | |
| **Train Split** | 16,178 (85.0%) | |
| **Validation Split** | 1,427 (7.5%) | |
| **Test Split** | 1,428 (7.5%) | |
| **Average Quality** | 0.62 (dedup + oversize cap applied) | |
| **Source** | WNT3D/Ultimate-Offensive-Red-Team (MIT) | |
| **Language** | English only | |
| **Created** | 2026-09-20 | |
| ## 🔧 What we did (and why this is a different repo than the source) | |
| The source is a valuable but raw collection: 6 overlapping Alpaca-JSONL files with **27,386 exact duplicate rows**, generic wrapper instructions ("User query", "Answer the question"), 66% of rows with sub-100-char outputs, and several 6.5MB single-row JSON dumps. This repo is the claimable clean version: | |
| 1. **Deduplication** — SHA-256 on normalized instruction+output; 27,000+ cross-file duplicates collapsed | |
| 2. **Oversize Cap** — Rows with >60KB outputs dropped (SFT-unusable mega-dumps) | |
| 3. **Instruction Normalization** — Generic wrappers folded into real task content | |
| 4. **Knowledge-Base Conversion** — 8 structured JSON knowledge bases (attack playbooks, vulnerability database, tool/exploit references, DEFI exploits, rust security, smart-contract vulns, target analysis, operational framework) converted into instruction→explanation rows | |
| 5. **PII Scrubbing** — Paths, API keys, emails, IPs replaced with [REDACTED_*] | |
| 6. **Quality Filtering** — Short (<200 char) and sub-threshold rows dropped | |
| Source yield: 78,430 raw rows → 19,033 kept (24.3%). | |
| ## 📈 Coverage | |
| Initial access · privilege escalation · lateral movement · persistence · exfiltration · defense evasion · vulnerability database (KEV) · tool usage (ghidra, burpsuite, metasploit, nmap, responder, bloodhound, mimikatz) · DEFI/smart-contract exploits · rust memory-safety · operational decision framework | |
| ## 🎯 Usage | |
| ```python | |
| from datasets import load_dataset | |
| ds = load_dataset("saidutta69/RedTeam-Premium", data_files="openai_chat/train.parquet", split="train") | |
| ``` | |
| ### With Axolotl | |
| ```yaml | |
| datasets: | |
| - path: saidutta69/RedTeam-Premium | |
| type: alpaca | |
| data_files: openai_chat/train.parquet | |
| split: train | |
| ``` | |
| ## 🧰 Schema | |
| Both formats (`openai_chat/` and `agent_traces/`) carry identical rows: `messages` (JSON string, user/assistant), `model`, `source`, `session_id`, `quality_scores` (JSON string). | |
| ## 🧾 Provenance & License | |
| MIT (inherited from WNT3D/Ultimate-Offensive-Red-Team). Full build stats shipped in `build_stats.json`. Built through the premium cleaning pipeline from the listed public source. Companion datasets: [CyberSec-Reasoning-Premium](https://huggingface.co/datasets/saidutta69/CyberSec-Reasoning-Premium) · [Kimi-K3-Premium](https://huggingface.co/datasets/saidutta69/Kimi-K3-Premium) · [Qwen3.8-Agent-Premium](https://huggingface.co/datasets/saidutta69/Qwen3.8-Agent-Premium). | |
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