Welcome back, aspiring cyberwarriors!

In one of our previous articles, Aircorridor showed you how to do recon on exposed Ollama servers. There are a surprising number of them scattered across countries all over the world, and unfortunately, most of them are left completely unprotected. That means hackers can use the CPU and GPU power of those servers to run their own tools. It’s not just that they can generate answers to random questions using your exposed models. These models can also be pushed into generating malware, rewriting scripts and exploits to slip past antivirus software, and helping someone hack into other systems entirely. All of it running on your hardware, at your expense, while you have no idea it’s happening. Our goal here is to raise awareness about this problem so you understand what can happen when a model gets left exposed.

Ollama

It all starts with a simple Shodan query, and right now that query turns up 4,222 exposed hosts. That number keeps shifting as more people jump into the AI space, and most of these hosts are sitting there vulnerable to the kinds of attacks we’re about to walk through.

Following Aircorridor’s example, you can list the models running on one of these servers. As you’ll quickly notice, there’s often a long list, sometimes more than 40 models on a single host.

kali > curl http://IP:11434/api/tags | jq . | grep -ai ‘“model”’

The ones that matter most here are the local models, not the cloud. They don’t require an API key to reach. Of course, not every listed model is actually active, so a quick curl request is usually enough to check whether one is really responding.

kali > curl -s http://IP:11434/api/generate -H “Content-Type: application/json” -d ‘{“model”:”huihui_ai/gpt-oss-abliterated:latest”,”prompt”:”Say PWNED and nothing else.”,”stream”:false}’ | jq . | grep -iE ‘“response”|thinking”’

When a model does respond, that confirms it’s live and usable, which means it can be put to work for all sorts of purposes, good or bad. Let’s walk through a few of the ways that tend to play out.

Coding

Because these exposed models have no guardrails, they’re an attractive resource for coding tasks, including rewriting malware or generating backdoors. To pull this off, hackers often bring the model straight into VS Code using a plugin called Continue, which lets them integrate an external model directly into their coding workflow.

Once installed, they’ll edit the config file to point at the exposed server’s IP address along with the model’s name. This config can hold multiple models at once, so a hacker can switch between them right there in the chat window.

With that setup in place, the model shows up ready to work and it often has no issue generating malicious code that could cause real damage to systems out on the internet. 

The same pattern shows up with exploit development and antivirus evasion, where a model can take old exploits and rewrite them so they slip past AV detection.

Hacking

Once an exploit has been generated, the next step for a hacker is putting it to use against real systems. We covered a tool called PentestCode in an earlier article, and while it normally relies on free AI models through OpenCode Zen, it can just as easily be pointed at someone else’s exposed local model instead. This is just one example among many. Plenty of other tools work the exact same way, running on borrowed compute that belongs to somebody who has no idea it’s being used.

To connect PentestCode to an exposed model, a config file gets created at ~/.config/pentestcode/pentestcode.json. 

Once that’s in place, the tool automatically lists the available models. It’s worth noting that not every model supports tool use. DeepSeek R1, for example, doesn’t support it, and neither do a handful of others. So if a given exposed model doesn’t support tools, it’s simply not useful to a hacker in this particular scenario.

Chat Assistant

Finally, exposed local models can also be accessed through a full chat interface using Open WebUI, which looks a lot cleaner than working from the command line. It has the kind of layout people are used to by now, with folders, chat history, channels, and separate workspaces. It takes a bit of disk space and a little patience to install, but once it’s running, it’s a solid and polished experience.

Summary

Running Ollama is not inherently dangerous. Simply exposing a model doesn’t automatically put you at risk of a data breach or account compromise. What it does do is hand hackers free access to your CPU and GPU, letting them run their own workloads on your dime without your knowledge or consent. That alone is a real cost, even if nothing else goes wrong.

The bigger danger shows up with older, outdated Ollama instances. Older versions are more likely to carry known vulnerabilities, and there are documented CVEs out there that can lead to full API exposure. When that happens, hackers aren’t just borrowing your compute anymore. They can steal your API keys outright and use them for whatever purpose they like. Keeping Ollama updated and making sure it isn’t sitting exposed to the open internet goes a long way toward avoiding both problems entirely.

We also invite you to join our AI for Cybersecurity training, available to our Subscriber Pro members. During the training, we’ll cover practical ways to use AI in cybersecurity, show you how to install and run local models, and much more. The field is evolving rapidly, and the sooner you learn to use these tools, the greater your advantage will be.

The post Artificial Intelligence (AI) in Cybersecurity, Part 24: Abusing Exposed Ollama Models first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

Lately we have been covering the use of AI in cybersecurity and this space has been growing so fast that it’s hard to keep up sometimes. It’s only going to keep growing from here, so it’s smart to learn how to use it to your advantage instead of getting left behind.

Today we’re going to show you a pentest tool that works with different models. The tool comes ready to use right out of the box and you don’t have to provide your API key to get started. During our own testing, we did eventually hit a usage limit, but by that point we had already gotten a ton of work done. The limits will reset every day, sometimes you just need to wait 5-14 hours. But the daily limit should be enough for you to complete many of your tasks.

What is PentestCode

PentestCode is an autonomous agent that lives in your terminal. You point it at a target and from there it takes over. It can run tools, read the output, build a picture of the network as it decides what step makes sense next. Under the hood, it’s a hard fork of OpenCode, but stripped of all the code editing features and rebuilt from the ground up with offensive security in mind.

In our experience the tool did well in both web and network pentesting. Of course, everyone’s mileage may vary, so give it a shot yourself and see how it fits into your workflow. With that said, let’s get it set up.

Setting Up

All you need to do is unzip the release version and start it up. Before you do that though, make sure you are downloading the original project made by s0ld13rr and not some fork. There have been reports of forks being bundled with infected files, so stick to the source.

kali > wget https://github.com/s0ld13rr/pentestcode/releases/download/v0.2.5/pentestcode-linux-x64.tar.gz
kali > 7z x pentestcode-linux-x64.tar.gz
kali > 7z x pentestcode-linux-x64.tar

And that’s it, we are ready to launch.

Working with PentestCode

Once you launch the tool, the console will appear.

kali > ./pentestcode

At this point you can either leave everything at the default settings or tweak the model and the provider yourself. By default, the tool is set up with OpenCode Zen as the provider and Big Pickle as the model, though you can switch that over to DeepSeek v4 Flash. 

If you want to connect to a different provider, just type /connect.

And whenever you want to swap the model, just type /models and pick from the list.

Active Directory

Let’s start by testing this against our own lab. We gave it an Active Directory account with low privileges and asked to pull some interesting information from LDAP.

It came back with domain admins, misconfigs, machine accounts and more.

At the very end of the report, it suggested the next steps based on everything it found. 

Then we brought in BloodHound to see the relationships across the domain. If you have been following our earlier articles, you already know that our lowpriv account is set up as a kind of backdoor, since it holds GenericAll rights over AdminSDHolder. The tool found the backdoor and exploited it.

The agent performed a DCSync attack and pulled every user hash in the environment. Then we asked it to generate a golden ticket.

It pulled it off using the Impacket. Keep in mind, using Impacket won’t always work against a protected endpoint, so it’s important to spell out clearly how you want the pentest to be done. If you are running this against a live target, put real guardrails in place and give the tool much more detailed prompts so it does not wander somewhere it shouldn’t.

Finally, we get to the tedious part of a pentest. It’s writing up the report. You can do it in different formats using /report.

kali > sudo apt install glow
kali > glow report.md

Web Pentesting and Bug Bounty Hunting

Web pentesting is such a massive topic on its own that plenty of people end up specializing in just one or two attacks testing them across different targets. PentestCode can be used here too, once you give it a good starting point through solid reconnaissance. You can toggle between modes using Tab, switching back and forth between Recon and Pentest.

We intentionally kept our prompt vague, just to see how creative the tool would get on its own and pointed it at a website. Within 15 minutes, it mapped out every subdomain tied to that company and tested the infrastructure behind each one.

The goal was to get an RCE. We didn’t expect much to come of it, but it managed to do it.

PentestCode uploaded a webshell and used curl to do recon on the internal network from there. On top of that, it compromised both a mail account and a MySQL database. The admin panel was also exploited with a CSRF vulnerability. Pretty impressive stuff, honestly.

The tool comes in handy during post exploitation as well. In our test, it exploited a vulnerability in PostgreSQL and escalated its way up to superuser access, then went through the databases and pulled out some interesting data. You can see some of it below.

Summary

If you decide to test PentestCode yourself, make sure you steer clear of vague prompts and set clear boundaries so that it doesn’t go further than it should. Use /pause to choose a mode where it stops and waits for your approval before moving forward. We believe that it’s important to keep a human in the loop in cybersecurity work like this. 

We also invite you to join our AI for Cybersecurity training. During the training, we’ll show you different ways of using AI in cybersecurity, set up local models and solve labs. The field is evolving rapidly and the sooner you learn things, the greater the advantage you’ll have. There’s no reason to resist AI. It’s a tool to master.

The post Artificial Intelligence (AI) in Cybersecurity, Part 23: Using PentestCode for Pentesting and Bug Bounty Hunting first appeared on Hackers Arise.