DedSecInside/TorBot

Status/Social links

Features

Recent improvements

Dependencies

Quick start

Local setup

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
python -m pip install -e .

Run the CLI

python main.py --help
python main.py --version
python main.py -u https://example.com --depth 2 --visualize table

Common examples

# Gather basic site information
python main.py -u https://example.com --info

# Save the crawl tree as JSON
python main.py -u https://example.com --depth 2 --save json

# Disable SOCKS5 if you are not using Tor locally
python main.py -u https://example.com --disable-socks5 --visualize tree

# Launch the optional desktop app when TorBotApp is installed
torbot app

Evidence-first AI analysis

torbot --url https://example.com --disable-socks5 \
  --save result --result-file crawl-result.json
torbot analyze crawl-result.json --provider ollama --model qwen3 \
  --output investigation/

Options

usage: Gather and analyze data from Tor sites.

optional arguments:
  -u URL, --url URL     Specify a website link to crawl
  --depth DEPTH         Specify max depth of crawler (default 1)
  -h, --help            Show this help message and exit
  --host                Set IP address for SOCKS5 proxy (defaults to 127.0.0.1)
  --port                Set port for SOCKS5 proxy (defaults to 9050)
  -v                    Displays DEBUG level logging, default is INFO
  --version             Show the current version of TorBot.
  --update              Update TorBot to the latest stable version
  --app                 Run optional TorBot desktop app
  --app-dir APP_DIR     Path to a TorBotApp checkout
  -q, --quiet           Prevents display of header and IP address
  --save FORMAT         Save results in a file. (tree, JSON)
  --visualize FORMAT    Visualizes tree of data gathered. (tree, JSON, table)
  -i, --info            Info displays basic info of the scanned site
  --disable-socks5      Executes HTTP requests without using SOCKS5 proxy

Optional desktop app

code/
+-- TorBot/
+-- TorBotApp/

torbot app

TORBOT_APP_DIR=/path/to/TorBotApp torbot app
torbot --app --app-dir /path/to/TorBotApp

Curated Features

Contribution Guidelines

Found an issue?

Developer Guidelines

References

1.  M. Glassman and M. J. Kang, “Intelligence in the internet age: The emergence and evolution of Open Source Intelligence (OSINT),” Comput. Human Behav., vol. 28, no. 2, pp. 673–682, 2012.
2.  D. Bradbury, “In plain view: open source intelligence,” Comput. Fraud Secur., vol. 2011, no. 4, pp. 5–9, 2011.
3.  B. Butler, B. Wardman, and N. Pratt, “REAPER: an automated, scalable solution for mass credential harvesting and OSINT,” 2016 APWG Symp. Electron. Crime Res., pp. 1–10, 2016.
4.  B. Zantout and R. A. Haraty, “I2P Data Communication System I2P Data Communication System,” no. April 2002, 2014.
5.  J. Qin, Y. Zhou, G. Lai, E. Reid, M. Sageman, and H. Chen, “The dark web portal project: collecting and analyzing the presence of terrorist groups on the web,” in Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics, 2005, pp. 623–624.
6.  D. Moore, T. Rid, D. Moore, and T. Rid, “Cryptopolitik and the Darknet Cryptopolitik and the Darknet,” vol. 6338, 2016.
7.  G. Weimann, “Going dark: Terrorism on the dark Web,” Stud. Confl. Terror., vol. 39, no. 3, pp. 195–206, 2016.
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9.  T. Minárik and A.-M. Osula, “Tor does not stink: Use and abuse of the Tor anonymity network from the perspective of law,” Comput. Law Secur. Rev., vol. 32, no. 1, pp. 111–127, 2016.
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11. B. Nafziger, “Data Mining in the Dark : Darknet Intelligence Automation,” 2017.
12. I. Sanchez-Rola, D. Balzarotti, and I. Santos, “The onions have eyes: A comprehensive structure and privacy analysis of tor hidden services,” in Proceedings of the 26th International Conference on World Wide Web, 2017, pp. 1251–1260.
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16. S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” Cryptogr. Mail. List https//metzdowd.com, 2009.
17. M. Wesam, A. Nabki, E. Fidalgo, E. Alegre, and I. De Paz, “Classifying Illegal Activities on Tor Network Based on Web Textual Contents”, vol. 1, pp. 35–43, 2017.
18. Sathyadevan S, Gangadharan S.“Crime analysis and prediction using data mining”. In Networks & Soft Computing (ICNSC), 2014 First International Conference on 2014 Aug 19 (pp. 406-412). IEEE.
19. Chau M, Chen H. "A machine learning approach to web page filtering using content and structure analysis. Decision Support Systems." 2008 Jan 1;44(2):482-94.
20. Ani R, Jose J, Wilson M, Deepa OS. “Modified Rotation Forest Ensemble Classifier for Medical Diagnosis in Decision Support Systems”, In Progress in Advanced Computing and Intelligent Engineering 2018 (pp. 137-146). Springer, Singapore.
21. Ani R, Augustine A, Akhil N.C. and Deepa O.S., 2016. “Random Forest Ensemble Classifier to Predict the Coronary Heart Disease Using Risk Factors”, In Proceedings of the International Conference on Soft Computing Systems (pp. 701-710). Springer, New Delhi.

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