Welcome back, aspiring cyberwarriors!

Sometimes you might run the same model twice and get different results. That often happens when you’ve upgraded it with skills. Skills are detailed text documents that lay out the tools the model should use, the approach it should take and how it should analyze the results. Good skills are practical, pulled from actual reports on HackerOne and other bug bounty platforms. A model can still lean on its own knowledge, but that’s just less efficient.

There are plenty of skills out there you might come across, but not everything can be trusted. Some skills can simply be dangerous and infect your system. To make sure they are safe, you can check them with SkillSpector by NVIDIA, so you don’t end up with anything malicious on your system.

Bug Bounty Skills

Both of these repositories do bug bounty hunting end to end, but they go about it in almost opposite ways.

The first is called Bountyforge. It’s actually just one single skill file, but it’s smart enough to split itself into eight different mini agents that all work at the same time. One looks at websites and apps, another at crypto and blockchain, others go after different angles hackers can exploit. It also checks each finding with four different tests to make sure it’s not a false alarm. Then you get a report in whatever format the bug bounty program wants.

You don’t even need Claude Code or any other coding tool for this, you can just run it right inside the regular Claude website in your browser.

The second bug bounty repository is Claude-BugHunter. It takes the opposite approach. The repo has 83 skills and almost half of those were built by studying 681 real bug reports that people actually got paid for on HackerOne. These skills aren’t locked to Claude Code either, you can use OpenCode, Codex or Hermes Agents with them.

Here are a few examples of the results we got with these skills.

API endpoints are often vulnerable and this is worth trying your luck on to see how it goes.

Another approach can be APK reverse engineering. Here we found a hardcoded RSA-2048 signing private key baked into the published APK. With that key, hackers can push a new app to the app store and infect every employee phone, getting access not just to the WiFi network at the workplace but to their personal life too. Quite dangerous.

We found an API endpoint vulnerable to an SQL injection and managed to pull the entire database.

Having skills built on real attacks keeps the model from wandering off into its own weird approaches and missing a lot of good findings. 

Active Directory Skills

Claude-AD was made by ADScanPro for testing a company’s internal network. It gives your model a playbook with skills and agents built for an Active Directory assessment. The developers are upfront that it’s not an auto pwn tool. It’s meant to guide you through the assessment. Every finding can get mapped to a compliance control (DORA, NIS2 and ENS).

Claude-AD is very careful about getting caught too. It explains what a security team would actually see on their end if that technique got used. And any time it’s about to do something that would actually change things on the company’s network, it stops and asks for confirmation first.

General Cybersecurity Skills

Antropic-Cybersecurity-Skills is basically a giant reference book. It has 817 skills covering 29 areas of security work, cloud security, malware analysis, all the way down to hardware and firmware. Each skill is its own small file, so your agent will quickly pull out the two or three it actually needs for its task.

Every skill ties back to real security frameworks that companies and auditors already use (NIST CSF, MITRE ATT&CK and so on). So if your model finds a problem using one of these skills, it can also tell you exactly which official standard it violates. You can use it to justify findings to a compliance team.

SCADA Skills

On an industrial network, a clumsy scan can shut down a production line or damage physical equipment, since a lot of this gear is old and wasn’t built to handle unexpected traffic. That’s why the ICS skill by Masriyan is built to never actively touch a live industrial network. Instead, it works off network captures someone already took. It reads the file, recognizes industrial protocols by the ports they normally run on (Modbus, DNP3, Siemens S7, EtherNet/IP, OPC-UA, and more) and counts which devices are talking to each other. It then shows you write commands, these are the ones that change a value on an industrial device. That’s the traffic you want to see first.

The second mode skips network captures and instead searches for exposed industrial equipment using Shodan and Censys. The skill can also help your model reason about how an industrial network is laid out and check findings against MITRE’s ICS specific attack framework and the IEC 62443 security standard.

Science Skills

Although science isn’t really what we want to focus on here, in one of our SCADA articles we mentioned that to carry out a successful attack requires hackers to understand the technical process of the plant. That means understanding how the chemicals are produced and which units are used along the way. We also showed how vinyl acetate is produced and talked about paracetamol production.

1 kg of paracetamol at 100% purity was reported to cost €8,205, while 1 kg at 99% purity cost just €5. So even a single day of sabotage could cause serious financial damage to an enterprise.

Finding a scientist among hackers is quite a challenge, which is why Stuxnet needed a group of people from different backgrounds working toward one objective. But now hackers can just import different skills to make their attacks more devastating. K-Dense published 140 skills with access to different scientific databases and Python tools.

The real concern here isn’t ICS exploits inside the repository, there aren’t any. It’s the access to sensitive scientific data paired with an AI agent that can actually understand that data and change it.

Summary

AI skills can be a gamechanger, especially when they’re based on actual reports hackers got paid for. These skills show your model how to approach things and what tools to use during the test, so it doesn’t wander off hallucinating and inventing its own ways of testing things. That can wreck your bug bounty flow, since you’ll end up overlooking plenty of potential targets.

Simply relying on the AI to find things isn’t enough, hunters that do it keep getting a lot of dupes. You need to test things manually too. For this reason we created our Bug Bounty training to show you how to find bugs and work with the AI more efficiently.

The post Artificial Intelligence (AI) in Cybersecurity, Part 25: Upgrading Your Model with Specific Skillset first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

Lately, the constrained AI models that companies keep shipping are becoming less and less useful for cybersecurity. We keep hearing a lot of complaints about Claude in this regard. What they are doing doesn’t really fix the problem, as hackers are not sitting around waiting for the guardrails to be lifted. The barrier to entry for hacking has dropped hard. AI can already automate huge chunks of this cybercrime work. Many of these latest models can even find zero days during engagements.

So poking around your infrastructure looks completely irrelevant. A more meaningful approach is to actually emulate these real attacks with AI, but for that we need a model with no guardrails. Today we are going to show you how to jailbreak a model and self host it for your pentesting work.

What is Obliteratus

Obliteratus is built to strip refusal behavior out of LLMs using abliteration. You’ll see it called abliteration or obliteration, same thing. It targets the internal representations causing the model to refuse in the first place and knocks them out. The model keeps all its core capability, it just stops throwing up artificial walls when you ask it something. It runs on CPU for smaller models, and it’s already been used to abliterate Kimi-K3 along with a bunch of others.

Setting Up

Setting up this tool will take some time, just like the jailbreak process itself. How long depends on your hardware and your internet speed.

kali > sudo apt update
kali > sudo apt install -y python3 python3-pip python3-venv git
kali > git clone https://github.com/elder-plinius/OBLITERATUS.git
kali > cd OBLITERATUS
kali > python3 -m venv venv
kali > source venv/bin/activate
kali > pip install --upgrade pip
kali > pip install -e .

Once it finishes, see if it works:

kali > obliteratus --help

If you don’t have a GPU, don’t worry. You can absolutely make this work with small models using just CPU power. Our Kali VM ran on 12 gigs of RAM and 7 processors, and that setup worked really well.

We went with Qwen 2.5-0.5B-Instruct for this test. You don’t need to have it downloaded beforehand. The tool will fetch it for you automatically. There are different methods available for the jailbreaking process, but advanced and nuclear are the most common. The advanced method is usually enough for most use cases, but if you see the model misbehaving you can escalate to nuclear.

kali > obliteratus obliterate Qwen/Qwen2.5-0.5B-Instruct --device cpu --method advanced --output-dir ./abliterated-qwen-0.5b

Once the model downloads, the tool starts running prompts designed to lift the guardrails.

You can find the full list of prompts in obliteratus/prompts.py. Right before it finishes, it runs a series of refusal tests to check whether the model actually complies with requests. Behavior varies a lot depending on which model you’re working with and which method you picked.

In our testing, the advanced method gave us approximately 75% of compliant answers.

At this point, everything is prepared and you can push your model to HuggingFace to share it. But if you want to run it locally, the next step is getting it working with Ollama.

Running Models with Ollama

Aircorridor previously made an article on running Ollama models locally and showed how to do it on a MacBook. If you don’t have it, you can still make this work on a Kali VM using your CPU. We need to convert our new model into a format that Ollama actually understands.

kali > git clone https://github.com/ggerganov/llama.cpp
kali > cd llama.cpp; python3 -m venv venv; source venv/bin/activate
kali > pip install -r requirements.txt
kali > python convert_hf_to_gguf.py /home/kali/OBLITERATUS/abliterated-qwen-0.5b --outfile qwen2.5-0.5b-abliterated-f16.gguf --outtype f16

Next, we create a Modelfile that points to the model:

kali > cat > Modelfile << EOF
FROM ./qwen2.5-0.5b-abliterated-f16.gguf
EOF

Then we create the model using Ollama:

kali > ollama create qwen05b-abliterated -f Modelfile

At this point, everything is ready and you can start testing it. The better the model you start with, the better your results will be.

kali > ollama run qwen05-abliterated

But even with a small model like this, you’ll see it do things that normally it wouldn’t.

Abliterated Models

This tool is helpful for doing the work yourself and understanding the logic behind the whole process. But if you’re working at scale and don’t have time to spend on each model individually, just keep in mind that many abliterated models are available on HuggingFace uploaded by huihui.ai. They’ve already done the heavy lifting for a lot of popular models.

If you can’t find exactly what you need in their collection, you now know how to do it yourself.

Summary

The landscape of offensive security has shifted because AI got so good at automation. Simple pentests with constrained models don’t prepare you for the reality out there anymore. As you can see, there’s no reason to work with constrained models in cybersecurity, when the people you’re up against are exploiting the full capability of a model with nothing holding them back. So test your environment with abliterated models before someone else does it. The tool is great for staying ahead of the actual threats.

The post Artificial Intelligence in Cybersecurity, Part 25: Jailbreaking AI Models with Obliteratus first appeared on Hackers Arise.

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.