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Discover and track internet assets using favicon hashes through search engines.

Project description

Favihunter

Favicons are small icons in modern web applications that could be very useful for us in our day-to-day hunting activities, especially when we combine these icons with modern search engines to find assets on the internet.

This project helps security professionals find assets online using favicon hashes through search engines such as:

🛠️ Installation

Optional - Creating a virtualenv before installing the dependencies

Note: The use of virtual environments is optional, but recommended. In this way, we avoid possible conflicts in different versions of the project's dependencies. Learn how to install and use virtualenv according to your OS here

Via PyPI (Recommended)

You can install FaviHunter directly from PyPI:

pip install favihunter

Via Source (Using Poetry)

Cloning the project:

git clone https://github.com/eremit4/favihunter.git

Installing the dependencies:

poetry install

🕵️‍♂️ Using

Discovering the project capabilities:

favihunter --help

Analyzing a specific URL:

favihunter --url <url address>

Analyzing a file with URLs:

favihunter --urls <file path>

Analyzing a local favicon image:

favihunter --favicon <file path>

Cleaning the favicon local directory:

favihunter --remove-favicons

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

favihunter-1.1.0.tar.gz (8.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

favihunter-1.1.0-py3-none-any.whl (10.4 kB view details)

Uploaded Python 3

File details

Details for the file favihunter-1.1.0.tar.gz.

File metadata

  • Download URL: favihunter-1.1.0.tar.gz
  • Upload date:
  • Size: 8.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.12.3 Linux/6.8.0-48-generic

File hashes

Hashes for favihunter-1.1.0.tar.gz
Algorithm Hash digest
SHA256 04bad289bd8b0e069c0f27f627d1d85fcd57c1115de97db271da8fcf6b4d512f
MD5 1a53707c80e0de749766a9730f567aa9
BLAKE2b-256 7908d6dde00a6b98103a3d7137a199f0df8da9eeb99c5e388c9054a0a67d142f

See more details on using hashes here.

File details

Details for the file favihunter-1.1.0-py3-none-any.whl.

File metadata

  • Download URL: favihunter-1.1.0-py3-none-any.whl
  • Upload date:
  • Size: 10.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.12.3 Linux/6.8.0-48-generic

File hashes

Hashes for favihunter-1.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 8b3dfcd07e8480906148e52dde0ddea43f278534493ae61e593aabdc3c8516c2
MD5 5ccc516ff44dfe59493722bf1b463bfb
BLAKE2b-256 ab9b863f507be8a2b8d4302b01e2242ff2b29deb98853b1945af08501040f037

See more details on using hashes here.

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