PentesterFlow

PentesterFlow / OffensiveSET

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Offensive Security Dataset Generator — MCP server for generating high-quality pentesting conversation datasets for LLM fine-tuning

31
8
100% credibility
Found Apr 03, 2026 at 31 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

OffensiveSET is a tool that creates realistic simulated penetration testing conversations for training AI models to perform security assessments.

How It Works

1
🔍 Find OffensiveSET

You hear about a helpful tool that creates practice conversations to teach AI how real security experts test for weaknesses.

2
📥 Get it set up

Download and prepare it with a simple one-click command on your computer.

3
🤖 Link to your AI helper

Connect it to your favorite AI chat like Claude so it can use special security tools right away.

4
✨ Make a practice set

Ask it to create sample security testing chats, picking topics like web attacks or cloud checks.

5
✅ Check your samples

Preview one chat to see realistic tool results, thinking steps, and reports before making more.

6
📊 Grow and refine

Generate a full set of thousands, analyze quality, and tweak for perfect training data.

🚀 Train your security AI

Use the chats to teach your AI to think and act like a pro penetration tester.

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AI-Generated Review

What is OffensiveSET?

OffensiveSET is a TypeScript MCP server that generates high-quality, multi-turn pentesting conversation datasets in ShareGPT/ChatML JSONL format for fine-tuning security-focused LLMs like Qwen3.5. It simulates full engagements—from recon and enumeration to exploitation, failures, and professional reports—with realistic tool outputs from 40 pentesting tools across 45 scenarios covering OWASP Top 10 and modern attacks. Developers get ready-to-train datasets with chain-of-thought reasoning, dynamic nmap scans, sqlmap dumps, and export options for Qwen, OpenAI, or Alpaca.

Why is it gaining traction?

Unlike generic conversation datasets, OffensiveSET delivers pentester-specific depth: unique failure cases like WAF blocks and honeypot detection, inline thinking blocks, and Qwen-native observation roles that mimic real OSCP-style workflows. The MCP integration lets Claude users generate 5000-entry datasets via simple prompts like "generate_dataset_v2 count:5000 thinking_ratio:0.6", with built-in validation and quality scoring. It's a niche github offensive security tool standing out in offensive security projects github for bridging pentesting realism and LLM training.

Who should use this?

Offensive security certified professional (OSCP) trainers building AI assistants for pen 200 prep, security researchers fine-tuning models for github offensive ai tasks, or offensive security web experts creating custom LLMs for recon-to-report pipelines. Ideal for teams in offensive security jobs needing conversation datasets that handle real-world pivots, tool chaining, and reporting—without manual data labeling.

Verdict

Grab it if you're in offensive security roadmap github territory and need pentest datasets now—MIT-licensed, solid docs, and one-line Claude setup make it instantly usable despite 31 stars and 1.0% credibility score signaling early maturity. Test small batches first; scale once your fine-tuned model crushes mock engagements.

(198 words)

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