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Nebula vs PentestGPT

Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.

Nebula

Open source · MIT

Open-source tool that links computers across the internet into one private, encrypted network.

PentestGPT

Open source · MIT

AI agent that runs penetration tests and CTF challenges in stages, with human-in-the-loop legacy mode.

The short answer

Choose Nebula if you need…

  • Teams connecting servers across several clouds or data centers
  • People who want a private network between their own machines without a fixed address plan
  • Operators who want group-based firewall rules similar to cloud security groups
  • Users comfortable managing certificates and a small cloud host

Choose PentestGPT if you need…

  • Security students working through CTF challenges
  • Security professionals who want an AI-assisted pentest workflow on authorized targets
  • Researchers studying LLM-driven penetration testing
  • Users who already have Claude Code or Codex CLI set up

Side-by-side

NebulaPentestGPT
TypeAppAI model / agent
PriceFree and open sourceFree and open source
LicenceMITMIT
GitHub stars18.5k16.2k
Setup difficultyAdvancedAdvanced
How you get itInstall with a package managerClone and build from source
Main languageGoPython
Last code update7 days ago4 months ago

What each one is

Nebula

Nebula is a networking tool that connects computers wherever they are into a single private network, so they can reach each other as if on the same local network. It uses certificates and encryption to authenticate each machine, and lets you write firewall rules based on named groups. The README says it runs on Linux, macOS, Windows, iOS, and Android, and can connect from a few computers up to tens of thousands.

PentestGPT

PentestGPT is a Python tool that uses large language models to help with security testing. In its main mode it runs an autonomous agent, driving Claude Code or Codex, through staged phases such as recon, exploit and walkthrough for CTF challenges, or asset discovery, vulnerability identification and report for penetration tests. An older interactive mode, pentestgpt-legacy, keeps a human in the loop and supports several LLM providers. It is published alongside a USENIX Security 2024 paper.

Best for

Nebula

  • Teams connecting servers across several clouds or data centers
  • People who want a private network between their own machines without a fixed address plan
  • Operators who want group-based firewall rules similar to cloud security groups
  • Users comfortable managing certificates and a small cloud host

PentestGPT

  • Security students working through CTF challenges
  • Security professionals who want an AI-assisted pentest workflow on authorized targets
  • Researchers studying LLM-driven penetration testing
  • Users who already have Claude Code or Codex CLI set up

Strengths

Nebula

  • Built on established cryptography: the README names the Noise Protocol Framework, ECDH, and AES-256-GCM by default
  • Runs on many platforms, including mobile, and is available through several package managers and Docker
  • MIT licensed and actively maintained, with an official example configuration and documentation site

PentestGPT

  • Multi-stage pipeline passes findings from one stage to the next
  • Supports several LLM providers in legacy mode, including local Ollama models
  • Sessions can be saved and resumed, and the project is MIT licensed

Not ideal for

Nebula

  • People who want a plug-and-play VPN with no setup: the README describes creating a certificate authority, signing certs, and editing config files
  • Teams that do not want to run their own certificate authority and lighthouse; the README points to a paid managed service from Defined Networking for that
  • Anyone needing a consumer 'hide my browsing' VPN, since the README frames Nebula as a network overlay for connecting hosts

PentestGPT

  • Anyone testing systems they do not have explicit permission to test, since it is built to attack targets
  • Beginners with no security or command-line background, because the setup and results need expert review
  • Teams needing a guaranteed, audited scanner, because agent output depends on the underlying model and may be wrong

Trade-offs

Nebula

  • Setup involves several steps: binaries, a certificate authority, host certificates, config files, and a lighthouse with a routable IP
  • The README notes that certificate authorities expire after one year by default and need rotation
  • Building with FIPS or BoringCrypto options involves extra build targets, and the README says BoringCrypto support is deprecated

PentestGPT

  • Autonomous mode depends on Claude Code or Codex CLI being installed and authenticated
  • Requires an API key or paid account for the chosen model provider; costs are not described in the README
  • Telemetry sends anonymous usage data to Langfuse by default, and users must opt out explicitly

Getting started

Nebula

Install on macOS with Homebrew

brew install nebula

Install on Arch Linux

sudo pacman -S nebula
Step-by-step guide: how to use Nebula

PentestGPT

Clone the repository

git clone https://github.com/GreyDGL/PentestGPT.git

Enter the project folder

cd PentestGPT
Step-by-step guide: how to use PentestGPT

Frequently asked questions

What is the difference between Nebula and PentestGPT?

Nebula: Open-source tool that links computers across the internet into one private, encrypted network. PentestGPT: AI agent that runs penetration tests and CTF challenges in stages, with human-in-the-loop legacy mode. Nebula suits teams connecting servers across several clouds or data centers, while PentestGPT suits security students working through ctf challenges.

Are Nebula and PentestGPT free?

Nebula: Free and open source. PentestGPT: Free and open source.

Is Nebula or PentestGPT more popular?

Nebula has more GitHub stars (18.5k) than PentestGPT (16.2k). Popularity isn't the same as fit — check which one matches your use case above.

Keep comparing

Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.