Welcome back, aspiring cyberwarriors!

Recently a new project was released by Praetorian. Praetorian is open-sourcing the offensive tools their red team actually uses on engagements. They call it 12 Caesars and their team is planning to release one tool a week for twelve weeks. Every tool is written in Go and ships as a single binary with zero external dependencies.

Each tool is assigned a Caesar name and a blog post, where you can learn more about these tools.

We’ll look at four of these tools, including the basic ones, to make this article suitable for everyone. Before we proceed, it’s important to remind you that you can only test systems you own or have explicit written authorization to test. We will use our lab for it.

Titus

Our first tool is Titus. Titus finds credentials, API keys, and tokens in source code, files, git history, container images, and more. The tool has 487 detection rules covering AWS, GCP, Azure, GitHub, Slack, databases and hundreds of other services. Similar to TruffleHog, it can validate credentials as it finds them by calling the provider APIs.

It’s also available as a Burp Suite extension and a Chrome extension.

kali > curl -sL -o titus https://github.com/praetorian-inc/titus/releases/latest/download/titus-linux-amd64
kali > chmod 777 titus
kali > ./titus --help

Let’s start by pointing it at test keys released by Truffle Security.

It found all the keys in the repo. Companies often publish their secrets by accident. Some live in the repositories for years, so it’s good to do regular checks to prevent unnecessary leakage.

Nerva

Nerva is a fingerprinter supporting 170 protocols across TCP, UDP, and SCTP, plus dozens of HTTP technology fingerprinters. You can feed it with open ports from naabu, nmap or other tools and it’ll tell you what’s actually running with its version.

kali > curl -sL -o nerva.tar.gz https://github.com/praetorian-inc/nerva/releases/download/v1.70.3/nerva_1.70.3_linux_amd64.tar.gz
kali > tar --no-same-owner -xzf nerva.tar.gz
kali > chmod 777 nerva
kali > ./nerva --help

Now let’s use our Ubuntu to show a basic test.

kali > ./nerva -t 192.168.131.9:22,11434

It will be way more useful when you work with big lists of corporate assets during a pentest.

Brutus

Brutus is a modern Hydra alternative. It supports more than 40 protocols including SSH, RDP, MySQL, PostgreSQL and a lot more. Brutus was built to work with Nerva or naabu output.

kali > curl -sL -o brutus.tar.gz https://github.com/praetorian-inc/brutus/releases/latest/download/brutus-linux-amd64.tar.gz
kali > tar --no-same-owner -xzf brutus.tar.gz
kali > chmod 777 brutus
kali > ./brutus --help     

Each command has its own help menu to make it easy to work with.

kali > ./brutus creds --help

Now once again lets use our Ubuntu to spay passwords. 

kali > ./brutus creds --target 127.0.0.1:22 --protocol ssh “admin:admin”

It was easy. As mentioned before, this tool chains with Nerva, and that’s how it becomes useful to test the entire infrastructure against common password and detection.

Julius

With AI services proliferating inside corporate environments, we want to know what LLM infrastructure is running on our hosts. Julius will fingerprint more than 60 AI services including Ollama, vLLM, LiteLLM, LocalAI, Hugging Face TGI, AWS Bedrock and AnythingLLM. It can also list available models from endpoints.

kali > curl -sL -o julius.tar.gz https://github.com/praetorian-inc/julius/releases/download/v1.5.1/julius_1.5.1_linux_amd64.tar.gz

kali > tar --no-same-owner -xzf julius.tar.gz
kali > ./julius --help

You can list all the AI services Julious can fingerprint: 

kali > ./julius list

We will take a look at our machine.

kali > ./julius probe http://192.168.131.7:11434

Ollama sits on port 11434, and it’s important not to leave it exposed publicly, because everyone can use your models without authentication.

Summary 

There are more tools available in this project. Most of them are meant to be chained together and work well this way. The company says its team is actively using these same tools during their engagements. While you won’t need everything at once, experiment with those tools and see what fits you best.

If you like red teaming, we have our Red Team Operator training, where we cover more tools and techniques to help you emulate real APT work, so you can give a company a realistic stress test and help make it secure.

The post Pentesting: Twelve Caesars – Community Tools for Offensive Security first appeared on Hackers Arise.

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.

Welcome back, aspiring cyberwarriors! 

Our article on ADScan and ADPulse was popular. Those tools didn’t need the AI to work, they were basically using python libraries to do all the heavy work of analyzing the Active Directory setup. Today we will look at ADStike, which is somewhat similar but uses AI to arrange the attacks. AI here is completely optional. It analyzes the results of each step and decides what tool to run and what information to find. Models that support tool calls can be used for it, like Qwen 2.5-coder 7b.

ADStike has all the common AD attacks and different levels of OPSEC modes with 52 attack modules integrated. We will look at the workflow it has and use a local AI model to direct the pentest. 

Setting Up

The repository has the installation process laid out. Let’s use it.

kali > git clone https://github.com/capture0x/AdStrike.git && cd AdStrike && chmod +x install.sh run.sh && bash install.sh && source venv/bin/activate && bash run.sh

Usually it takes 5 minutes to set things up. Then you will be prompted to fill out the information about the domain. We are using our lab here with a regular account that doesn’t have any privileges in the domain to make it fair. 

When it has all the needed information, it will be saved in a config file. You will then see the main menu with options. Each phase has separate steps that can run individually.

We will go with step 53, which is AI. The tool already knows what local Ollama models we have installed and lets us choose if there are some. For this test we kept OPSEC level default.

Once you select the needed options, the scan will begin.

Pentesting Active Directory

The AI orchestrator will start from the first phase (Recon) and select only the appropriate phases and steps for your Active Directory setsups. All environments are different, some attacks will simply be impossible due to infrastructure limitations. 

As it progresses we start seeing the output on the screen from the commands it runs. 

Basically, it does all you would do but quicker. Below you can see that LAPS were empty, because they were not set up in this domain. You might want to recommend your client fix it, if you encounter the same results. LAPS help a lot in preventing lateral movement, since the Administrator password will never be the same across your machines. 

Then Kerberoasting follows. We did have a few roastable accounts. The compromise here will depend on your wordlist. 

Since it’s our lab, we knew the password of that user.

Another attack you may see is Timeroasting. Here it uses an impacket script to extract computer hashes. While those are basically impossible to brute force, some computers are vulnerable to Pre2k attack, where the password of the computer matches the DNS name. 

Here is more on Pre2k. Look at the NetExec commands. It’s passing the computer name and the password to the DC to find a valid account. These machines can have certain privileges, just like users do. All the privileges can be found in BloodHound. By the way, ADStrike will run SharpHound collection and analyze the data with AI to find paths and worthy accounts. 

Finally, after all the necessary steps, you will have a nice report generated in different formats.

Let’s open the HTML version and see what it looks like.

Scrolling down we see the MITRE ATT&CK Coverage

Then we have all the findings and their details. 

Summary

ADStrike looks very promising and easy to work with. It has 52 attack modules that are orchestrated with AI. AI here is completely optional and things should work without it as well. If you prefer not to let AI execute anything in your environment, you can use it for planning only or leave it alone to pwn your lab.

Join our AI for Cybersecurity training to learn more! 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 28: Using ADStrike to Compromise Active Directory first appeared on Hackers Arise.

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.