tokeii0/lovelymisclab — explained in plain English
Analysis updated 2026-05-18
Decode and analyze suspicious files during a CTF forensics challenge by chaining nodes like Base64 decode, XOR, and LSB extraction.
Build a repeatable pipeline that only recalculates the steps affected by an edit, useful for iterating on a tricky steganography puzzle.
Connect an external AI client such as Claude Code or Cursor through the built in MCP server to run nodes or edit the canvas automatically.
Package a set of nodes you use often into a reusable module instead of rebuilding the same workflow each time.
| tokeii0/lovelymisclab | nzyuko/rustyvnc | arman-bd/chromiumfish | |
|---|---|---|---|
| Stars | 54 | 54 | 55 |
| Language | Rust | Rust | Rust |
| Setup difficulty | easy | hard | easy |
| Complexity | 3/5 | 4/5 | 3/5 |
| Audience | researcher | researcher | developer |
Figures from each repo's GitHub metadata at analysis time.
Portable desktop app for Windows, Linux, and macOS, no extra infrastructure needed unless you enable the optional MCP server.
LovelyMiscLab is a desktop application for CTF style forensics work: decoding, steganography, image analysis, and cryptography. At its core is a node graph engine similar in spirit to ComfyUI. Instead of writing scripts, you drag nodes onto a canvas, such as Base64 decode, XOR, LSB steganography extraction, QR code decode, or AES decrypt, and connect them with lines to build a data flow. The backend runs the nodes in order and shows each node's result on the canvas as it goes. The app can be used a few different ways. You can run a single node as a quick standalone tool, or wire up a full pipeline that only recalculates the parts that changed when you edit it. It also includes an AI agent that can build a workflow on the canvas step by step from a plain language description, and it can act as a platform for building your own tools by packaging a group of nodes into a reusable module or plugging in an external script as a custom node. There are 270 built in nodes across 16 categories, covering encoding and encryption, image processing, classic and modern ciphers, steganography, control and logic, file and binary analysis, text processing, archive handling, audio processing, character encoding, hashing, and visual analysis such as byte histograms and entropy curves. The app also embeds an MCP server, so external AI clients like Claude Code, Cursor, or Codex can connect to it, discover the available nodes, run them, and read or edit the canvas you are currently working on. This is turned off by default and requires a bearer token when enabled, and it only listens on your local machine. All computation happens locally, so data does not leave your device. The project is built with Rust, Tauri, and React, and is organized as a Cargo workspace that keeps the core analysis engine separate from the desktop app shell. It is licensed under GPLv3.
A desktop app where you build CTF forensics workflows by connecting nodes on a canvas, similar to how ComfyUI works for images.
Mainly Rust. The stack also includes Rust, Tauri, React.
You can use, modify, and share this freely, including commercially, but any modified version you distribute must also be open sourced under the same license.
Setup difficulty is rated easy, with roughly 30min to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
double-check against the repo, no cap.