npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

docker-pypy-sandbox

v0.0.17

Published

A docker-based sandbox to execute untrusted python code using PyPy

Downloads

11

Readme

Build Status

Docker PyPy Sandbox

This is a fork of docker-python-sandbox, intended to be used with PyPy instead of the typical Python compiler (CPython). Unless you know that you need PyPy, it is advised that you stick with the original package.

Why PyPy over CPython?

PyPy provides a robust sandboxing feature, whereas CPython is notoriously difficult to lock down. PyPy in combination with Docker is a good place to start when exploring arbitrary Python code execution.

Example use (Linux)

  1. Install Docker
  2. mkdir docker-pypy-sandbox-example && cd docker-pypy-sandbox-example
  3. npm init (press return until done)
  4. Install the library: npm install --save docker-pypy-sandbox
  5. Pull the docker image used by the library: docker pull murtyjones/docker-pypy-sandbox
  6. Create a new file, index.js, with the following code:
let Sandbox = require('docker-pypy-sandbox')

const poolSize = 5
let mySandbox = new Sandbox({poolSize})

mySandbox.initialize(err => {
  if (err) throw new Error(`unable to initialize the sandbox: ${err}`)
  
  const code = 'print "Hello, world!"'
  const timeoutMs = 2 * 1000
  
  mySandbox.run({code, timeoutMs}, (err, result) => {
    if (err) throw new Error(`unable to run the code in the sandbox: ${err}`)
    
    console.log(result.stdout); // Hello, world!
  })
});
  1. node index.js

Example use (macOS)

NOTE: For an unidentified reason, this library does not work well on macOS. The instructions below will help you to use the library for testing purposes, but this library should only be used in production on a Linux server.

  1. Install Docker
  2. mkdir docker-pypy-sandbox-example && cd docker-pypy-sandbox-example
  3. npm init (press return until done)
  4. Install the library: npm install --save docker-pypy-sandbox
  5. Pull the docker image used by the library: docker pull murtyjones/docker-pypy-sandbox
  6. Create a new file, index.js, with the following code:
let Sandbox = require('docker-pypy-sandbox')

const poolSize = 5
let mySandbox = new Sandbox({poolSize})

mySandbox.initialize(err => {
  if (err) throw new Error(`unable to initialize the sandbox: ${err}`)
  
  const code = 'print "Hello, world!"'
  const timeoutMs = 2 * 1000
  
  mySandbox.run({code, timeoutMs}, (err, result) => {
    if (err) throw new Error(`unable to run the code in the sandbox: ${err}`)
    
    console.log(result.stdout); // Hello, world!
  })
});
  1. docker run -it --rm -p 3000:3000 murtyjones/docker-pypy-sandbox
  2. open a new tabL CMD + T
  3. node index.js --mac=true