PentestGPT vs OffensiveGPT: What Can Be Verified and How to Judge AI Pentest Tools

Key takeaways

• PentestGPT is a real open-source project with a research paper; OffensiveGPT could not be verified.
• AI helps with planning, summarising output and reporting, but not with scope or judgement.
• Evaluate tools on evidence, data handling, human control and reproducibility.
• Use lab targets only; unauthorised testing is illegal.

A note on the two names

PentestGPT is a real, published tool. OffensiveGPT is not something this article can verify as a single product with documentation. The name appears on various pages and as the name of custom chatbots, and nothing reliable could be found to compare it feature by feature. An earlier version of this post compared them in a table of features that could not be checked, so that table has been removed. What follows is an honest account of PentestGPT, a method for judging any tool of this kind, and a safe way to try AI in a lab.

What PentestGPT is

PentestGPT started as an academic project that used large language models to assist penetration testing. The research was presented as "PentestGPT: An LLM-empowered Automatic Penetration Testing Tool" and the code is public at GreyDGL/PentestGPT on GitHub. In the original design, the model works alongside a human tester. It keeps a structured plan of the testing task, helps interpret tool output such as Nmap results, and suggests next steps.

The project has changed since then and newer versions may add more automation, so read the current README for what it does today. Forks and lookalike repositories also exist, which means you should confirm you are looking at the original before you run anything.

What AI assistants can and cannot do in a pentest

How to judge any AI pentest tool

1. Is it real and maintained? A public repository, a named author or company, recent updates and documentation.
2. Is the claim backed by evidence? Look for a paper, benchmark or a reproducible demo. Be careful with claims such as "finds zero days automatically".
3. Where does your data go? Pasting client scan results into a cloud model may breach your contract. Check whether the tool can use a local model.
4. Does it keep a human in control? Tools that run commands on their own are risky unless sandboxed and restricted to the authorised scope.
5. Can you reproduce its results? Test on a deliberately vulnerable lab and compare with a manual run.

A safe way to try it

• Use only targets you own or that are meant for practice, such as a Metasploitable VM, DVWA on your own machine or a HackTheBox lab.
• Isolate the lab on a host-only network.
• Give the tool the least access it needs and watch every command it proposes before running it.
• Compare the AI's advice with your own notes and score it on right, partly right and wrong.

Testing any system without written authorisation is illegal under the IT Act, whichever tool you use. AI does not change that.

The defender's view

If attackers use AI to speed up reconnaissance and report writing, defenders gain by making the basics harder: patching internet-facing systems, removing unneeded exposure, strong authentication and good logging. The same assistants help blue teams summarise alerts and draft detections. Ask which side of the work a tool helps with before you adopt it.

Which should you choose?

For learning, pick the tool that is documented and open to inspection, which in this pair means PentestGPT, and use it as a study aid beside manual methods. For work, choose by your data-handling rules, not by a feature list. Many testers get more value from a general assistant used carefully for note-taking and explanations than from a specialised tool they do not fully trust.

Next steps

To build the underlying skills, see the VAPT course. Related reading: AI chatbots for cybersecurity professionals and AI in red teaming.