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

What is Robin? ## Why "Robin"? ## Challenges of Dark Web OSINT ## 1. High Noise Ratio of Information ## 2. Difficulty in Optimizing Search Queries ## 3. Integration of Multiple Information Sources ## How Robin Works ## AI-Driven Query Optimization ## Intelligent Filtering of Search Results ## Installation and Initial Setup ## Prerequisites ## 1) Docker (Web UI Mode - Recommended) ## 2) Release Binary (CLI Mode) ## 3) Python (Development Version) ## Basic Usage (CLI According to README) ## 1. Simple Search ## 2. Save Output to a File ## 3. Use Local LLM (Privacy-Focused) ## Practical Example: Investigating Ransomware-Related Keywords ## Search Query ## Automatic Processing by Robin (Concept) ## Considerations and Best Practices ## Security Considerations ## Privacy Protection ## Tips for Effective Searching ## Conclusion ## Reference Links 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 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. 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. 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. Traditional dark web investigations faced the following challenges: Search results from the dark web are filled with irrelevant content, spam, and scam sites. Manually filtering this information takes too much time. Effective searching requires knowledge of dark web-specific terminology and search techniques. This task has a high barrier to entry for beginners. Investigating across multiple sources requires understanding the unique UIs and quirks of each. Robin addresses these challenges with the power of AI. Robin operates in the following flow: 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): Optimized After LLM (Example): Note: The queries mentioned here are examples only. Please use them in compliance with laws and organizational policies for legitimate investigative purposes. Robin sends the search results to the LLM for filtering based on the following criteria: 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. Example of installing Tor (from the README): For more details on Tor, refer to The Tor Project. 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. You can access the Web UI by opening http://localhost:8501 in your browser. If you prefer not to set up a local Python environment, you can easily obtain the binary from the Releases. The README specifies that Python 3.10+ is required. The CLI for Robin is executed by specifying the "model," "query," "number of threads," and "output file." If running the development version in Python, it would look like this: If you do not want to send queries or results to external APIs, you can choose Ollama (local model). 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). Let's look at a practical use case by investigating keywords related to ransomware. Below is a fictional sample showing the output format (not actual data). 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. Always Use a Tor Connection Management of API Keys Prohibition on Accessing Illegal Content When using external LLM APIs, search queries and results are sent to the provider. For sensitive investigations, consider the following measures: Use Specific Keywords Cross-Verify with Multiple Sources Regular Investigations Robin is a groundbreaking tool that streamlines dark web OSINT using the power of AI. 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. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: 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 Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: 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 CODE_BLOCK: 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 CODE_BLOCK: credit card leaks Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: credit card leaks CODE_BLOCK: credit card leaks CODE_BLOCK: "CVV" OR "dumps" OR "fullz" site:.onion Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: "CVV" OR "dumps" OR "fullz" site:.onion CODE_BLOCK: "CVV" OR "dumps" OR "fullz" site:.onion COMMAND_BLOCK: # macOS brew install tor # Linux / WSL sudo apt install tor Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: # macOS brew install tor # Linux / WSL sudo apt install tor COMMAND_BLOCK: # macOS brew install tor # Linux / WSL sudo apt install tor COMMAND_BLOCK: docker pull apurvsg/robin:latest Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: docker pull apurvsg/robin:latest COMMAND_BLOCK: docker pull apurvsg/robin:latest COMMAND_BLOCK: git clone https://github.com/apurvsinghgautam/robin.git cd robin cp .env.example .env Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: git clone https://github.com/apurvsinghgautam/robin.git cd robin cp .env.example .env COMMAND_BLOCK: git clone https://github.com/apurvsinghgautam/robin.git cd robin cp .env.example .env COMMAND_BLOCK: docker run --rm \ -v "$(pwd)/.env:/app/.env" \ --add-host=host.docker.internal:host-gateway \ -p 8501:8501 \ apurvsg/robin:latest ui --ui-port 8501 --ui-host 0.0.0.0 Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: docker run --rm \ -v "$(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 COMMAND_BLOCK: 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 CODE_BLOCK: chmod +x robin ./robin cli --model gpt-4.1 --query "ransomware payments" Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: chmod +x robin ./robin cli --model gpt-4.1 --query "ransomware payments" CODE_BLOCK: chmod +x robin ./robin cli --model gpt-4.1 --query "ransomware payments" COMMAND_BLOCK: pip install -r requirements.txt python main.py cli -m gpt-4.1 -q "ransomware payments" -t 12 Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: pip install -r requirements.txt python main.py cli -m gpt-4.1 -q "ransomware payments" -t 12 COMMAND_BLOCK: pip install -r requirements.txt python main.py cli -m gpt-4.1 -q "ransomware payments" -t 12 CODE_BLOCK: ./robin -m gpt-4.1 -q "ransomware payments" -t 12 Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: ./robin -m gpt-4.1 -q "ransomware payments" -t 12 CODE_BLOCK: ./robin -m gpt-4.1 -q "ransomware payments" -t 12 CODE_BLOCK: python main.py cli -m gpt-4.1 -q "ransomware payments" -t 12 Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: python main.py cli -m gpt-4.1 -q "ransomware payments" -t 12 CODE_BLOCK: python main.py cli -m gpt-4.1 -q "ransomware payments" -t 12 CODE_BLOCK: ./robin --model gpt-4.1 --query "sensitive credentials exposure" --threads 8 --output report.md Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: ./robin --model gpt-4.1 --query "sensitive credentials exposure" --threads 8 --output report.md CODE_BLOCK: ./robin --model gpt-4.1 --query "sensitive credentials exposure" --threads 8 --output report.md CODE_BLOCK: ollama pull llama3.1 ./robin -m llama3.1 -q "zero days" Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: ollama pull llama3.1 ./robin -m llama3.1 -q "zero days" CODE_BLOCK: ollama pull llama3.1 ./robin -m llama3.1 -q "zero days" CODE_BLOCK: ./robin -m gpt-4.1 -q "ransomware payments" -t 12 --output ransomware_report.md Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: ./robin -m gpt-4.1 -q "ransomware payments" -t 12 --output ransomware_report.md CODE_BLOCK: ./robin -m gpt-4.1 -q "ransomware payments" -t 12 --output ransomware_report.md CODE_BLOCK: { "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" ] } } Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: { "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" ] } } CODE_BLOCK: { "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" ] } } - 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 - Relevance: Degree of match with the query - Reliability: Evaluation of the information source - Freshness: Recency of the information - Risk Assessment: Determination of threat level - 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 - Pull the Docker image - Prepare .env - Configure API keys in .env - Start the container - Query Optimization: Adjusting the query to improve search accuracy - Searching & Scraping: Collecting information from multiple sources - Summarizing & Organizing: Compiling key points and next actions - Always Use a Tor Connection Accessing the dark web must be done via Tor Minimizes the risk of IP address leakage - Accessing the dark web must be done via Tor - Minimizes the risk of IP address leakage - Management of API Keys Manage API keys with environment variables or in the .env file Do not commit .env to Git - Manage API keys with environment variables or in the .env file - Do not commit .env to Git - Prohibition on Accessing Illegal Content Robin is a tool for investigative purposes Viewing or downloading illegal content is strictly prohibited - Robin is a tool for investigative purposes - Viewing or downloading illegal content is strictly prohibited - Accessing the dark web must be done via Tor - Minimizes the risk of IP address leakage - Manage API keys with environment variables or in the .env file - Do not commit .env to Git - Robin is a tool for investigative purposes - Viewing or downloading illegal content is strictly prohibited - Use Local Models with Ollama - Generalize Queries: Avoid using specific organization names - Process Results Before Sending to LLM: Mask personal information, etc. - Use Specific Keywords Example: ransomware payments, sensitive credentials exposure - Example: ransomware payments, sensitive credentials exposure - 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 - Information from the dark web can be rife with misinformation and noise - Do not rely on a single result; verify consistency across multiple sources - Regular Investigations Information on the dark web is fluid Regular execution helps keep track of trends - Information on the dark web is fluid - Regular execution helps keep track of trends - Example: ransomware payments, sensitive credentials exposure - Information from the dark web can be rife with misinformation and noise - Do not rely on a single result; verify consistency across multiple sources - Information on the dark web is fluid - Regular execution helps keep track of trends - 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 - Monitoring activities of ransomware groups - Discovering leaked data - Collecting threat intelligence - Security research - Official Robin Repository - Article on Robin for OSINT (English) - Ahmia - Tor Search Engine - Official Ollama Site - The Tor Project - Robin Releases - Docker Hub: apurvsg/robin