tszdanger

tszdanger / RealMythos

Public

Public reconstruction of Claude Mythos as an open cybersecurity reasoning stack: datasets, models, reproducible environments, and trace infrastructure.

46
10
85% credibility
Found May 22, 2026 at 46 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

RealMythos is an open-source research project that reconstructs advanced cybersecurity reasoning capabilities as a transparent, community-inspectable system. The project has completed its first stage: publishing a dataset of 6,159 real-world vulnerability cases (linked to CVEs) with detailed AI-generated reasoning traces, proof-of-concept scripts, and quality evaluations. The data is designed to train AI systems to think like security experts—identifying root causes, trigger conditions, and attack paths. Future stages plan to release trained models, reproducible testing environments, and multi-agent trace collection infrastructure. The project is explicitly independent from Anthropic, focuses on defensive security research, and includes clear ethical guidelines. It comes from academic researchers at HKUST and CUHK with published prior work in vulnerability dataset construction.

How It Works

1
🔍 You discover a security research project

You hear about RealMythos, an open project that reconstructs advanced security reasoning systems so anyone can study and use them.

2
📚 You explore the security reasoning dataset

The project has already published thousands of real vulnerability cases, each with detailed reasoning about what went wrong and how to trigger the flaw.

3
🤖 You see how AI learns to think like a security expert

The dataset shows AI how to analyze code step-by-step: finding root causes, identifying trigger conditions, and building proof-of-concept tests.

4
🛡️ You understand the defensive focus

The project is designed for security researchers, defenders, and educators—not for attacking real systems. Clear guidelines keep everything ethical.

5
You choose your path forward
🎓
Use for research and learning

Study the reasoning patterns to understand vulnerability analysis techniques

🧠
Train your own AI assistant

Use the data to teach an AI to reason about security vulnerabilities

📝
Reproduce the pipeline

Run the same process to create your own security reasoning dataset

6
🚀 You access the published dataset

Everything is available on Hugging Face with clear documentation, so you can download and use it right away.

✅ You have transparent, inspectable security reasoning

Instead of trusting a closed system, you now have an open alternative that the whole community can examine, reproduce, and improve.

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AI-Generated Review

What is RealMythos?

RealMythos is an open-source Python project that reconstructs Claude Mythos as a public cybersecurity reasoning stack. It takes real CVE-linked vulnerabilities from the wild and transforms them into high-quality training data for security reasoning models. The system uses a multi-stage pipeline that starts with vulnerable C/C++ code, generates chain-of-thought reasoning traces with proof-of-concept exploits, evaluates PoC quality across six dimensions, and reformats everything for supervised fine-tuning. The dataset is already published on Hugging Face with over 6,000 CVE-linked records.

Why is it gaining traction?

The project addresses a real gap in security AI: most existing benchmarks use synthetic or template-based data, while RealMythos grounds everything in actual vulnerability cases. Its patch-unaware reasoning design is particularly clever—it rewrites teacher reasoning so student models learn to analyze code without peeking at the fix, which should produce more genuinely useful models. The CWE-based prompt routing and six-dimension PoC evaluation give researchers structured ways to measure what models actually learn. Being completely open about methodology and releasing reproducibility artifacts distinguishes it from closed security AI systems.

Who should use this?

Security AI researchers evaluating model capabilities will find the benchmark comparisons valuable. Developers building vulnerability detection or exploitation tools can fine-tune models on the curated dataset. Academic researchers studying AI for security can leverage the documented pipeline and reproducibility artifacts. Defensive security teams exploring automated reasoning might benefit from the Stage 3 reproducible environments. It's not a turnkey solution—expect to read documentation and understand the pipeline.

Verdict

The 0.8500000238418579% credibility score reflects solid academic foundations (ASE 2023, OOPSLA 2025 publications) from HKUST and CUHK researchers. However, 46 stars signals early-stage traction, and some documentation lives in markdown rather than polished guides. The technical depth is real, but expect to invest time understanding the pipeline before running it. Worth watching if you're serious about security reasoning research.

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