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https://github.com/jehna/humanify

Deobfuscate Javascript code using ChatGPT
https://github.com/jehna/humanify

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Deobfuscate Javascript code using ChatGPT

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README 

        
# HumanifyJS

> Deobfuscate Javascript code using LLMs ("AI")



This tool uses large language modeles (like ChatGPT & llama) and other tools to

deobfuscate, unminify, transpile, decompile and unpack Javascript code. Note

that LLMs don't perform any structural changes – they only provide hints to

rename variables and functions. The heavy lifting is done by Babel on AST level

to ensure code stays 1-1 equivalent.



### Version 2 is out! 🎉



v2 highlights compared to v1:

* Python not required anymore!

* A lot of tests, the codebase is actually maintanable now

* Renewed CLI tool `humanify` installable via npm



### ➡️ Check out the [introduction blog post][blogpost] for in-depth explanation!



[blogpost]: https://thejunkland.com/blog/using-llms-to-reverse-javascript-minification



## Example



Given the following minified code:



```javascript

function a(e,t){var n=[];var r=e.length;var i=0;for(;i=20



The preferred whay to install the tool is via npm:



```shell

npm install -g humanifyjs

```



This installs the tool to your machine globally. After the installation is done,

you should be able to run the tool via:



```shell

humanify

```



If you want to try it out before installing, you can run it using `npx`:



```

npx humanifyjs

```



This will download the tool and run it locally. Note that all examples here

expect the tool to be installed globally, but they should work by replacing

`humanify` with `npx humanifyjs` as well.



### Usage



Next you'll need to decide whether to use `openai`, `gemini` or `local` mode. In a

nutshell:



* `openai` or `gemini` mode

  * Runs on someone else's computer that's specifically optimized for this kind

    of things

  * Costs money depending on the length of your code

  * Is more accurate

* `local` mode

  * Runs locally

  * Is free

  * Is less accurate

  * Runs as fast as your GPU does (it also runs on CPU, but may be very slow)



See instructions below for each option:



### OpenAI mode



You'll need a ChatGPT API key. You can get one by signing up at

https://openai.com/.



There are several ways to provide the API key to the tool:

```shell

humanify openai --apiKey="your-token" obfuscated-file.js

```



Alternatively you can also use an environment variable `OPENAI_API_KEY`. Use

`humanify --help` to see all available options.



### Gemini mode



You'll need a Google AI Studio key. You can get one by signing up at

https://aistudio.google.com/.



You need to provice the API key to the tool:



```shell

humanify gemini --apiKey="your-token" obfuscated-file.js

```



Alternatively you can also use an environment variable `GEMINI_API_KEY`. Use

`humanify --help` to see all available options.



### Local mode



The local mode uses a pre-trained language model to deobfuscate the code. The

model is not included in the repository due to its size, but you can download it

using the following command:



```shell

humanify download 2b

```



This downloads the `2b` model to your local machine. This is only needed to do

once. You can also choose to download other models depending on your local

resources. List the available models using `humanify download`.



After downloading the model, you can run the tool with:



```shell

humanify local obfuscated-file.js

```



This uses your local GPU to deobfuscate the code. If you don't have a GPU, the

tool will automatically fall back to CPU mode. Note that using a GPU speeds up

the process significantly.



Humanify has native support for Apple's M-series chips, and can fully utilize

the GPU capabilities of your Mac.



## Features



The main features of the tool are:

* Uses ChatGPT functions/local models to get smart suggestions to rename

  variable and function names

* Uses custom and off-the-shelf Babel plugins to perform AST-level unmanging

* Uses Webcrack to unbundle Webpack bundles



## Contributing



If you'd like to contribute, please fork the repository and use a feature

branch. Pull requests are warmly welcome.



## Licensing



The code in this project is licensed under MIT license.