WithinUsAI/cluade_mythos_preview_5k_v2

Claude Mythos Preview — Distillation Dataset

5,000 high-quality examples designed to distill the deep cybersecurity expertise, memory safety focus, autonomous technical analysis, and secure systems design capabilities of Claude Mythos Preview.

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Overview

This dataset captures Mythos Preview’s signature strengths:

• Exceptional depth in cybersecurity and vulnerability discovery
• Strong emphasis on memory safety and secure systems design
• Autonomous technical analysis with defensive framing
• Rigorous, professional-grade security reasoning
• Responsible handling of sensitive security topics

It is intended for fine-tuning open models to perform high-quality defensive security work, secure systems design, and deep technical analysis.

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Dataset Details

• File: mythos_preview_5k_v2.jsonl
• Size: 5,000 examples
• Format: Chat messages with visible <think> reasoning
• Difficulty: Expert / Professional security level

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Format

{
  "messages": [
    {
      "role": "system",
      "content": "You are Claude Mythos Preview: exceptional depth in cybersecurity, memory safety..."
    },
    {
      "role": "user",
      "content": "Real-world security analysis, memory safety, or secure systems prompt..."
    },
    {
      "role": "assistant",
      "content": "<think>\nVisible rigorous defensive reasoning...\n</think>\n\n**Defensive Technical Analysis...**"
    }
  ],
  "metadata": {
    "model_focus": "claude_mythos_preview",
    "category": "Advanced Cybersecurity" | "Memory Safety" | "Autonomous Defensive Analysis" | ...,
    "difficulty": "expert",
    "example_id": "...",
    "generated_at": "..."
  }
}

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Categories

• Advanced Cybersecurity & Vulnerability Discovery
• Memory Safety & Secure Systems Design
• Autonomous Technical Analysis & Exploitation (Defensive)
• Complex Systems Security & Hardening
• Secure Software Engineering at Scale

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Key Features

• True <think> tags on every response — visible, high-quality defensive reasoning
• Strong focus on memory safety and real-world exploitability
• All security content framed defensively and responsibly
• Concrete hardening recommendations and testing strategies
• High technical precision with professional tone
• No offensive exploit chains — built for defenders and builders

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Intended Use

This dataset is designed for:

• Training models strong in defensive cybersecurity and secure systems design
• Distilling Mythos Preview-level technical depth into open models
• Research on secure AI-assisted engineering and autonomous defensive analysis
• Building models that can perform high-quality vulnerability analysis and hardening work

Strongly recommended for teams working on security tooling, secure development platforms, or defensive AI capabilities.

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Important: Defensive Framing

All examples in this dataset are created with defensive framing only. No offensive exploit development or malicious guidance is included.

The goal is to help create models that make systems more secure, not less.

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How to Load

from datasets import load_dataset

dataset = load_dataset("json", data_files="mythos_preview_5k_v2.jsonl")

Compatible with Axolotl, TRL, Unsloth, Llama-Factory, etc.

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Quality Notes

This is a synthetic dataset created to emulate the professional defensive security reasoning style of Claude Mythos Preview.

Care was taken to ensure:

• High diversity across security domains
• Responsible, defensive-only content
• Visible rigorous reasoning
• Practical value for security engineering teams

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License & Responsibility

This dataset is provided for research and educational purposes in defensive security.

Users must ensure any models fine-tuned on this data are used responsibly and do not enable offensive or malicious activities.

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Generated June 2026 — Focused on professional defensive cybersecurity and secure systems excellence.