National Cyber Warfare Foundation (NCWF)

Searching the dark web manually is time-consuming. Robin uses AI to do it for you, turning raw data into actionable intelligence in minutes.

The post Artificial Intelligence in Cybersecurity, Part 8: AI-Powered Dark Web Investigations first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

If you’ve ever conducted an OSINT investigation, you probably know that the dark web is one of the hardest places to investigate. Whether you’re tracking ransomware groups or looking for leaked passwords manually searching through dark web results takes hours and gives you mostly junk and malware. This is where AI can change how you investigate. By using Large Language Models we can improve our searches and filter results faster. To do this, we have a tool called Robin.

In this article, we’ll explore how to install this tool, how to use it, and what features it provides. Let’s get rolling!

What is Robin

Robin is an open-source tool for investigating the dark web. It uses AI to improve your searches, filter results from dark web search engines, and summarize what you find. What makes Robin particularly valuable is its multi-model support. You can easily switch between OpenAI, Claude, Gemini, or local models like Ollama depending on your needs, budget, and privacy requirements. The tool is CLI-first, built for terminal users who want to integrate dark web intelligence into their existing workflows.

Step #1: Install Robin

For this demonstration, I’ll be using a Raspberry Pi as the hacking platform, but you can easily replicate all the steps using Kali or any other Debian-based distribution. To install the tool, we can either use the source code from GitHub or Docker. I will choose the first option. To begin, clone the repository first:

pi> git clone https://github.com/apurvsinghgautam/robin.git

As shown in the downloaded files, this is a Python project. We need to create a virtual environment and install the required packages.

pi> python -m venv venv

pi> source venv/bin/activate

pi> pip3 install -r requirements.txt

Before Robin can search the dark web, we need to have Tor running on your system. Install Tor by opening your terminal and executing the following command:

pi> sudo apt install tor

Step #2: Configure Your API Key

In this demonstration, I will be using Google’s Gemini models. You can easily create an API key in Google AI Studio to access the models. If you open the config.py file, you will see which models support the tool.

Robin can be configured using either a .env file or system environment variables. For most users, creating a .env file in your Robin directory provides the cleanest approach. This method keeps your API credentials organized and makes it easy to switch between different configurations. Open the file in your preferred text editor and add your Gemini API key.

Step #3: Execute Your First Dark Web Investigation

First, let’s open the help screen to see which options this tool supports and to verify that we installed it correctly.

pi> python3 main.py –help

Currently, we can see two supported modes for using this tool: CLI and web UI. I prefer CLI, so I will demonstrate that. Let’s explore the help screen of the CLI mode.

pi> python3 main.py cli –help

It’s a straightforward help screen; we simply need to specify an LLM model and our query. Let’s search for credential exposure.

pi> python3 main.py cli -m gemini-2.5-flash -q “sensitive credentials exposure”

After a few minutes of processing, Robin produced the gathered information on the terminal. By default, it is formatted in Markdown and saved to a file with a name based on the current date and time. To view the results with Markdown formatting, I’ll use a command-line tool called glow.

pi> glow summary-xx-xx.md

The analysis examined various Tor-based marketplaces, vendors, and leak sources that advertise stolen databases and credentials. The findings reveal a widespread exposure of personally identifiable information (PII), protected health information (PHI), financial data, account credentials, and cryptocurrency private keys associated with major global organizations and millions of individuals. The report documents active threat actors, their tactics, and methods of monetization. Key risks have been identified, along with recommended next steps.

Understand the Limitations

While Robin is a powerful tool for dark web OSINT, it’s important to understand its limits. The tool uses dark web search engines, which only index a small part of what’s actually on hidden services. Many dark websites block indexing or require you to log in, so Robin can’t reach them through automated searches. For thorough investigations, you’ll still need to add manual research and other OSINT methods to what Robin finds.

The quality of Robin’s intelligence summaries depends a lot on the LLM you’re using and the quality of what it finds. Gemini 2.5 Flash gives great results for most investigations, but the AI can only work with the information in the search results. If your search doesn’t match indexed content, or if the information you need is behind a login wall, Robin won’t find it.

Summary

Conducting investigations on the dark web can be time-consuming when using traditional search tools. Since the dark web relies on anonymity networks, isn’t indexed by standard search engines, and contains a vast amount of irrelevant information, manual searching can often be slow and ineffective. Robin addresses these challenges by leveraging AI to enhance your searches, intelligently filter results, and transform findings into useful intelligence reports. While this tool does have limitations, it can be a valuable addition to your arsenal when combined with manual searching and other OSINT tools.

If you’re interested in deepening your knowledge of OSINT investigations or even starting your own investigation business, consider exploring our OSINT training to enhance your skills.