mitkox

mitkox / megacode

Public

RLM based security scanner for massive .NET codebases

73
17
100% credibility
Found Feb 09, 2026 at 37 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This project is an AI-powered tool that scans large .NET codebases for security vulnerabilities and produces detailed reports with findings and recommendations.

How It Works

1
🔍 Find the security helper

You hear about a smart tool that checks your .NET project code for hidden safety risks using clever thinking power.

2
💻 Get it ready on your computer

You follow easy steps to set it up in a private workspace on your machine, making sure everything is prepared.

3
📁 Point to your project folder

You simply tell the tool the location of your .NET code folder so it knows what to examine.

4
🤖 Connect a thinking helper

You link it to a nearby smart service that helps it understand and analyze your code deeply.

5
🚀 Start the safety scan

You give it the go-ahead, and it dives in, carefully reviewing files and spotting potential issues.

6
⏳ Follow the progress

You watch updates as it builds a list of important files and searches for problems, building trust in the process.

📄 Get your safety report

You receive a friendly report listing risks, explanations, and easy fix ideas to make your project stronger and safer.

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Star Growth

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AI-Generated Review

What is megacode?

Megacode is a Python tool for auditing massive .NET codebases with GitHub RLM RAG prompts, spotting vulnerabilities like SQL injection, JWT flaws, and weak crypto without loading entire repos into LLM context. Point the CLI at your source root—like `security-audit --source-root ~/dev/PowerToys`—and it indexes files, ranks security signals, then iteratively searches and analyzes via bounded tools, spitting out Markdown reports, JSON metadata, and manifests. Handles huge projects via ripgrep or Python fallbacks, needing a local OpenAI-compatible endpoint like vLLM.

Why is it gaining traction?

It scales where static scanners falter on monorepos, using RLM for security—recursive tool calls keep analysis targeted and explainable, with severity ratings, attack scenarios, and fixes per CWE. Devs notice fast local runs in fast-mode (under 10 minutes), verbose logs for debugging, and outputs ready for PRs. Niche hook: AI-driven audits beat grep scripts for .NET specifics like FromSqlRaw or BinaryFormatter risks.

Who should use this?

.NET security engineers auditing enterprise codebases or legacy apps with thousands of CS files. Backend teams doing pre-prod hygiene on massive repos like PowerToys. Local LLM users wanting RLM meaning in security guard workflows, skipping cloud costs.

Verdict

Worth a spin for .NET teams with Deno and vLLM setups—solid CLI and docs make it usable now, despite 38 stars, 1.0% credibility score, and alpha maturity. Expect false positives; always validate before fixes. (198 words)

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