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, fable-5.1-premium, CyberSec-Reasoning-Premium, Kimi-K3-Premium, and 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:
- Deduplication — SHA-256 on normalized instruction+output; 27,000+ cross-file duplicates collapsed
- Oversize Cap — Rows with >60KB outputs dropped (SFT-unusable mega-dumps)
- Instruction Normalization — Generic wrappers folded into real task content
- 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
- PII Scrubbing — Paths, API keys, emails, IPs replaced with [REDACTED_*]
- 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
from datasets import load_dataset
ds = load_dataset("saidutta69/RedTeam-Premium", data_files="openai_chat/train.parquet", split="train")
With Axolotl
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 · Kimi-K3-Premium · Qwen3.8-Agent-Premium.