Welcome back, aspiring cyberwarriors! 

We’re continuing our series on Artificial Intelligence in Cybersecurity with another tool called Shannon. Shannon is a white-box AI pentester. It was created to test the security of your project by pointing it at the source code and the test stand. That means it needs both the running application and the source code. First, Shannon does an in-depth analysis of the code in the repository, then looks for attacks against that same project you’re hosting locally. This white-box approach is certainly more effective, as models don’t have to guess possible vulnerabilities, since guessing varies greatly depending on the model you use. Vulnerabilities can only make their way to the report if they have a working PoC. As always, only test systems you own or have explicit written authorization to test.

Now let’s take a closer look at Shannon and test our Juice Shop with it.

Setting Up

Juice Shop is just a Docker container, so we will install Docker and npm first.

kali > sudo apt update
kali > sudo apt install -y docker.io nodejs npm
kali > sudo systemctl enable docker --now
kali > sudo usermod -aG docker $USER
kali > newgrp docker

Then comes the Juice Shop.

kali > docker pull bkimminich/juice-shop
kali > docker run -d --name juice-shop --restart unless-stopped -p 3000:3000 bkimminich/juice-shop

It’s up and running. Now we need to copy the repository to our disk.

kali > git clone --depth 1 https://github.com/juice-shop/juice-shop.git

Once we have it all set, we can get Shannon. Unlike Pentestcode and other tools, Shannon runs differently. By default, it expects your AI API keys in the env. In the next sections we’ll show how to run Shannon with OpenRouter and Ollama.

OpenRouter

Shannon wants to see your API key in the env. You will also need a models.json file somewhere on your disk. Copy the following command to create it.

cat > ~/models.json << 'EOF'
{
  "providers": {
    "openrouter": {
      "apiKey": "YOUR_API_KEY",
      "models": [
        {
          "id": "nvidia/nemotron-3.5-lightning:free",
          "name": "NVIDIA Nemotron 3.5 Lightning (free)",
          "reasoning": true,
          "input": ["text"],
          "thinkingLevelMap": {
            "off": null,
            "minimal": null,
            "low": "low",
            "medium": null,
            "high": "high",
            "xhigh": null,
            "max": "max"
          },
          "contextWindow": 262144,
          "maxTokens": 65536,
          "cost": {
            "input": 0,
            "output": 0,
            "cacheRead": 0,
            "cacheWrite": 0
          },
          "compat": {
            "supportsDeveloperRole": false,
            "thinkingFormat": "openrouter"
          }
        }
      ],
      "api": "openai-completions",
      "baseUrl": "https://openrouter.ai/api/v1"
    }
  }
}
EOF

Then pass the keys and validate the setup.

kali > export SHANNON_AI_API_KEY="sk-or-v1-YOUR_KEY"
kali > export SHANNON_AI_MODEL="openrouter:nvidia/nemotron-3.5-lightning:free"

kali > npx @keygraph/shannon@latest start -u http://172.17.0.1:3000 -r /home/kali/juice-shop --validate-model

If you see it’s validated, then you can run a full security analysis.

kali > npx @keygraph/shannon@latest start -u http://172.17.0.1:3000 -r /home/kali/juice-shop -c ~/juice-quick.yaml --models-config ~/models.json --follow

This will take a while and your free daily tokens won’t be enough to complete the scan.

As the security test progresses, you’ll see all the commands and prompts it runs. Obviously, the frontier models will give better results.

Ollama

Shannon isn’t only limited to APIs. You can perform the same scan with a local model. Many openweight models will be a great fit for it if you have the necessary hardware to make it work smoothly.

Just for the demonstration we’ll use Qwen 2.5-coder 7b.

kali > curl -fsSL https://ollama.com/install.sh | sh
kali > ollama pull qwen2.5-coder:7b

We’ll create the same models.json file to point Shannon to our Ollama.

{
  "providers": {
    "ollama": {
      "baseUrl": "http://127.0.0.1:11434/v1",
      "api": "openai-completions",
      "apiKey": "ollama",
      "models": [
        {
          "id": "qwen2.5-coder:7b",
          "name": "Qwen 2.5 Coder 7B",
          "input": ["text"],
          "contextWindow": 32768,
          "maxTokens": 8192,
          "cost": { "input": 0, "output": 0 },
          "compat": {
            "supportsDeveloperRole": false,
            "supportsReasoningEffort": false
          }
        }
      ]
    }
  }
}

Make sure your Ollama is accessible, and change the IP address if needed.

kali > export SHANNON_AI_API_KEY=ollama
kali > export SHANNON_AI_MODEL=ollama:qwen2.5-coder:7b
kali > npx @keygraph/shannon@latest start -u http://172.17.0.1:3000 -r /home/kali/juice-shop -c ~/juice-quick.yaml --models-config ~/models.json --validate-model

Before we do anything, let’s validate the model.

If your model has been validated and you can start scanning your project.

kali > kali > npx @keygraph/shannon@latest start -u http://172.17.0.1:3000 -r /home/kali/juice-shop -c ~/juice-quick.yaml --models-config ~/models.json --follow

Reporting

When the scan finishes, Shannon creates PDF and Markdown reports with the evidence attached to them. You will also see structured JSON and SARIF files for downstream systems.

Below is an example of a PDF report. As you can see, multiple vulnerabilities were found in the project, some of them are classified as High. As mentioned before, all the findings here were validated with PoCs.

Summary

Shannon was made to find flaws in your web apps to make them more secure. It digs through the project repository and runs checks against a live system. It then creates a report with findings that were discovered with PoCs, so you get a clean report. As AI becomes more prevalent in cybersecurity, it should be used to improve the security of your projects before their release. Of course, that doesn’t replace a human in the loop, but it makes things faster. Stick to better models and don’t cut back on your security.

AI is evolving rapidly and the sooner you learn how to work with it, the greater the advantage you’ll have. That’s why we invite you to join our AI for Cybersecurity training. During this training, you will see you different ways of using AI in cybersecurity, set up local models and solve labs.

The post Artificial Intelligence (AI) in Cybersecurity, Part 30: Shannon – AI Pentester for Web Apps and APIs first appeared on Hackers Arise.

http-terminator is PortSwigger's AI pipeline for discovering HTTP request-smuggling bugs. Which stages run, its dependencies, and its testing limits.

Welcome back, aspiring cyberwarriors!

With so many AI tools out there, it’s getting harder to find the ones actually worth your time. A lot of projects look alike and the differences between them can come down to minor UI tweaks. But it’s still worth digging, because every now and then you find something good.

One of the interesting projects we came across recently is Cybermes. It’s an offensive security assistant and automation framework built for bug bounty hunting, recon and reporting. It has more than 200 security playbooks and full Model Context Protocol (MCP) support. The most notable thing here is the reporting structure. The framework handles reporting really well, it has a scope.yaml file you can modify and the TUI interface looks clean.

We’ll show what the assistant can do and how well it handles the tasks.

Setting Up

Unlike PentestCode, there’s some work to do before you can start using the tool.

First, make sure you have Go installed on your Kali. The framework needs to compile tools and without Go your installation will be incomplete.

kali > sudo apt update 
kali > sudo apt install go-lang

Then download the repository and run the setup script.

kali > git clone https://github.com/Zyrexnn/Cybermes.git
kali > cd Cybermes
kali > python3 -m venv venv; source venv/bin/activate
kali > chmod 777 setup.sh
kali > ./setup.sh

It’ll take a few minutes. When it’s done, run the doctor script to make sure everything is in order.

kali > python3 tools/doctor.py

After that, add your OpenRouter API key to two env files before you can start working with the tool.

kali > vim .env
kali > vim hermes/.env

Make sure you add the API key to both files, otherwise it won’t work.

kali > ./cybermes model

Finally, specify the scope in scope.yaml. We’ve got OWASP Juice Shop for the test, listening on port 3000.

Now we’re all set.

IDOR/BOLA – Terminal User Interface 

The Terminal User Interface is clean and easy to work with, so we’ll start there. You might end up preferring it over the CLI version.

kali > TARGET=127_0_0_1_3000
kali > ./cybermes –tui

Our first prompt in TUI is hunting for IDOR using Nemotron 3.5 Lightning. As the model tests the target, you’ll see entries populating the screen with the commands the tool runs.

Fifteen minutes later we got our results and BOLA was confirmed.

Reporting is really the strongest side of this framework. It created a couple of files with reports and sorted them properly. As you keep hunting for bugs on the same target, all your findings get brought together in one file.

Findings are always stored in Markdown format and keep almost the same structure every time, which makes them look professional.

JWT & SQLi – Command-line Interface 

Now let’s look at the CLI version and try to find more bugs.

kali > ./cybermes --cli

Our first prompt will be testing JWT:

Authorized lab only. Target http://127.0.0.1:3000/rest/user/login.
Audit authentication and JWT handling with non-destructive requests. Try the publicly documented Juice Shop demo accounts if needed (admin@juice-sh.op / admin123 and a normal user you register).

Check token claims, privilege flags, and whether a standard user can hit admin-ish REST routes.
Record only confirmed issues under reports/127_0_0_1_3000/findings/.

It took some time to reason through and test the app, then gave the results.

The same files were produced again, with PoCs and an explanation of each bug and the risks tied to it.

We also tested SQLi on search:

Authorized lab only. Audit http://127.0.0.1:3000/rest/products/search?q= for SQL injection using safe syntax and error/timing evidence. Do not dump the full database into the terminal. If confirmed, write reports/127_0_0_1_3000/findings/high_sqli_product_search.md and a minimal PoC in pocs/.

Here’s the report on our SQLi finding, looking just as good as the previous ones.

The tool passed all our tests against the Juice Shop and brought the findings together in the final report. The skills it ships with work well. You might want to go through them and add some of the ones we covered recently. We covered a repository with 83 skills and almost half of those were built by studying 681 real bug reports that people actually got paid for on HackerOne.

Summary

Cybermes has clear strengths. It’s good at reporting, it has a clean TUI and a big library of built in skills. The framework sets up quickly if you have Go installed on your Kali and it already knows which tools to work with based on those skills. That’s handy. We used OpenRouter for this test, but you can also point it at your local Ollama models. For that you’ll need a tool calling model (qwen2.5:14b). Chat only 3B models won’t cut it here.

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.

The post Artificial Intelligence (AI) in Cybersecurity, Part 27: Web App Hacking with Cybermes first appeared on Hackers Arise.

AI IR Overlay specifies containment for agents using valid credentials, with a working kill-switch contract and an admitted gap when no SOC is staffed.

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.

An AI-driven threat actor called JADEPUFFER built ransomware that hunts AI model files specifically, entering through a known Langflow RCE and pivoting via an exposed Docker socket.

ENCFORGE Ransomware Targets AI Models After Langflow RCE Exploit on Latest Hacking News | Cyber Security News, Hacking Tools and Penetration Testing Courses.