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Robin: Investigating the Dark Web with AI - A New Companion for Security Researchers

#security #ai #llm

Originally published on 2026-01-21
Original article (Japanese): Robin: AIを武器にダークウェブを調査する - セキュリティ研究者の新しい相棒

Robin Cover Image

Gathering information from the dark web is an essential task for security researchers. However, finding useful information amidst the vast amount of noise has been a daunting challenge. Robin is a new OSINT tool that leverages the power of LLMs to address this issue. OSINT (Open Source Intelligence) refers to investigative techniques that collect and organize information based on publicly available data.

In this article, we will introduce an overview of Robin, how it works, and the setup process according to the official README (as of version 2.0 on January 17, 2026). Since CLI options and model names may be updated, it is advisable to check the official README before execution.

What is Robin?

Robin is an OSINT (Open Source Intelligence) investigation tool designed for the dark web, utilizing AI. The name is derived from "Robin, Batman's sidekick," and it is designed to assist security researchers.

Key features:

  • Modular Structure: A design that separates search, scrape, and LLM processing
  • Multi-Model Support: Ability to switch between OpenAI, Claude, Gemini, Ollama (local), etc. (OpenRouter is also mentioned in the README)
  • CLI First: Can be executed from the terminal, suitable for automation
  • Docker Support: Can launch a Web UI without polluting the environment
  • Report Output: Ability to save investigation results to a file
  • Extensibility: Easy to add search engines and output formats

Why "Robin"?

Just as Batman protects the peace of Gotham City, security researchers protect organizations from threats. However, just as Batman needed Robin as a sidekick, researchers also need a tool to help organize the vast amount of information on the dark web. Robin fulfills that role perfectly.

Challenges of Dark Web OSINT

Traditional dark web investigations faced the following challenges:

1. High Noise Ratio of Information

Search results from the dark web are filled with irrelevant content, spam, and scam sites. Manually filtering this information takes too much time.

2. Difficulty in Optimizing Search Queries

Effective searching requires knowledge of dark web-specific terminology and search techniques. This task has a high barrier to entry for beginners.

3. Integration of Multiple Information Sources

Investigating across multiple sources requires understanding the unique UIs and quirks of each.

Robin addresses these challenges with the power of AI.

How Robin Works

Robin operates in the following flow:

sequenceDiagram
    participant User as ユーザー
    participant Robin as Robin
    participant LLM as LLM (GPT-4/Claude等)
    participant Tor as Tor検索エンジン

    User->>Robin: 検索クエリ入力
    Robin->>LLM: クエリ最適化依頼
    LLM->>Robin: 最適化されたクエリ
    Robin->>Tor: ダークウェブ検索実行
    Tor->>Robin: 検索結果（生データ）
    Robin->>LLM: 結果の要約・フィルタリング依頼
    LLM->>Robin: フィルタ済み情報
    Robin->>User: 整理された調査結果
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AI-Driven Query Optimization

For example, if a user wants to search for "leaked credit card information," Robin will rephrase the query for the dark web and add additional keywords to enhance search accuracy.

Original Query (Example):

credit card leaks
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Optimized After LLM (Example):

"CVV" OR "dumps" OR "fullz" site:.onion
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Note: The queries mentioned here are examples only. Please use them in compliance with laws and organizational policies for legitimate investigative purposes.

Intelligent Filtering of Search Results

Robin sends the search results to the LLM for filtering based on the following criteria:

  • Relevance: Degree of match with the query
  • Reliability: Evaluation of the information source
  • Freshness: Recency of the information
  • Risk Assessment: Determination of threat level

Installation and Initial Setup

Robin offers multiple execution methods. The official README (as of version 2.0 on January 17, 2026) recommends Docker (Web UI). Since CLI options and model names may be updated, please refer to the official Robin repository and Releases for the latest information.

Prerequisites

  • Tor: Tor is required for searching (the README assumes Tor is installed and running in the background)
  • API Key: For OpenAI / Anthropic / Google Gemini, etc. (if using online LLMs)
  • Ollama: If using a local LLM

Example of installing Tor (from the README):

# macOS
brew install tor

# Linux / WSL
sudo apt install tor
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For more details on Tor, refer to The Tor Project.

1) Docker (Web UI Mode - Recommended)

  1. Pull the Docker image 
docker pull apurvsg/robin:latest
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  1. Prepare .env 
git clone https://github.com/apurvsinghgautam/robin.git
cd robin
cp .env.example .env
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  1. Configure API keys in .env

Set the API keys for each provider in .env (e.g., OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY).

Note: Since .env contains sensitive information, do not commit it to Git.

  1. Start the container 
docker run --rm \
  -v "$(pwd)/.env:/app/.env" \
  --add-host=host.docker.internal:host-gateway \
  -p 8501:8501 \
  apurvsg/robin:latest ui --ui-port 8501 --ui-host 0.0.0.0
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You can access the Web UI by opening http://localhost:8501 in your browser.

2) Release Binary (CLI Mode)

If you prefer not to set up a local Python environment, you can easily obtain the binary from the Releases.

chmod +x robin
./robin cli --model gpt-4.1 --query "ransomware payments"
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3) Python (Development Version)

The README specifies that Python 3.10+ is required.

pip install -r requirements.txt
python main.py cli -m gpt-4.1 -q "ransomware payments" -t 12
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Basic Usage (CLI According to README)

The CLI for Robin is executed by specifying the "model," "query," "number of threads," and "output file."

1. Simple Search 

./robin -m gpt-4.1 -q "ransomware payments" -t 12
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If running the development version in Python, it would look like this:

python main.py cli -m gpt-4.1 -q "ransomware payments" -t 12
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2. Save Output to a File 

./robin --model gpt-4.1 --query "sensitive credentials exposure" --threads 8 --output report.md
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3. Use Local LLM (Privacy-Focused)

If you do not want to send queries or results to external APIs, you can choose Ollama (local model).

ollama pull llama3.1
./robin -m llama3.1 -q "zero days"
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If connecting to Ollama via Docker, you will need to configure OLLAMA_BASE_URL as mentioned in the README (this may vary based on your environment, such as host.docker.internal).

Practical Example: Investigating Ransomware-Related Keywords

Let's look at a practical use case by investigating keywords related to ransomware.

Search Query 

./robin -m gpt-4.1 -q "ransomware payments" -t 12 --output ransomware_report.md
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Automatic Processing by Robin (Concept)

  1. Query Optimization: Adjusting the query to improve search accuracy
  2. Searching & Scraping: Collecting information from multiple sources
  3. Summarizing & Organizing: Compiling key points and next actions

Below is a fictional sample showing the output format (not actual data).

{
  "query": "ransomware payments",
  "results": [
    {
      "title": "Example source",
      "url": "http://[redacted].onion",
      "summary": "Sample summary (does not represent real victims or organizations)",
      "risk_level": "medium",
      "confidence": 0.5,
      "timestamp": "2026-01-01"
    }
  ],
  "analysis": {
    "key_findings": [
      "Excerpt of important information",
      "Additional aspects to verify",
      "Next actions to take"
    ]
  }
}
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Considerations and Best Practices

As noted in Robin's README, this type of tool should only be used for educational purposes and legitimate investigative purposes. Access and handling may be prohibited by laws or organizational rules.

Security Considerations

  1. Always Use a Tor Connection

    • Accessing the dark web must be done via Tor
    • Minimizes the risk of IP address leakage

  2. Management of API Keys

    • Manage API keys with environment variables or in the .env file
    • Do not commit .env to Git

  3. Prohibition on Accessing Illegal Content

    • Robin is a tool for investigative purposes
    • Viewing or downloading illegal content is strictly prohibited

Privacy Protection

When using external LLM APIs, search queries and results are sent to the provider. For sensitive investigations, consider the following measures:

  • Use Local Models with Ollama
  • Generalize Queries: Avoid using specific organization names
  • Process Results Before Sending to LLM: Mask personal information, etc.

Tips for Effective Searching

  1. Use Specific Keywords

    • Example: ransomware payments, sensitive credentials exposure

  2. Cross-Verify with Multiple Sources

    • Information from the dark web can be rife with misinformation and noise
    • Do not rely on a single result; verify consistency across multiple sources

  3. Regular Investigations

    • Information on the dark web is fluid
    • Regular execution helps keep track of trends

Conclusion

Robin is a groundbreaking tool that streamlines dark web OSINT using the power of AI.

Key Benefits:

  • Reduces the hassle of searching and organizing (case-dependent)
  • Automatic filtering of noise
  • Enables effective investigations even for beginners
  • Supports local LLMs for privacy protection

Suitable Use Cases:

  • Monitoring activities of ransomware groups
  • Discovering leaked data
  • Collecting threat intelligence
  • Security research

The field of dark web investigation has traditionally required specialized knowledge and tools, but Robin is poised to change that significantly. It is truly a "reliable companion" for security researchers.

For those interested, I recommend starting by reviewing the official README and disclaimers, and safely testing it in an isolated verification environment.

Reference Links

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