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redhuntlabs / repository
An AI-powered Personal Identifiable Information (PII) scanner.
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
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⠀⠀⠀⠀⠀⠀ ⠀O C T O P I I⠀⠀⠀⠀
Copyright © 2023 RedHunt Labs Private Limited
Octopii is a Personally Identifiable Information (PII) scanner that uses Optical Character Recognition (OCR), regular expression lists and Natural Language Processing (NLP) to search public-facing locations for Government ID, addresses, emails etc in images, PDFs and documents.
PII leaks are often overlooked in the cybersecurity space. At RedHunt Labs, we always look for different and innovative ways to come up with cybersecurity solutions that organizations and services need. We've encountered a substantial number of organizations that have their servers configured incorrectly. This causes employee and customer PII to leak all the time, giving malicious parties sensitive information about their origins, ID numbers, contact information and their location.
This is why we created Octopii, a tool to demonstrate and detect how easy it is to automate the discovery and extraction of leaked PII and sensitive documents on the Internet.
pip install -r requirements.txt.sudo apt install tesseract-ocr -y on Ubuntu or sudo pacman -Syu tesseract on Arch Linux.python -m spacy download en_core_web_sm.Once you've installed the above, you're all set.
To run Octopii, type
python3 octopii.py <location to scan>
where <location to scan> is a file or a directory.
Octopii currently supports local scanning via filesystem path, S3 URLs and Apache open directory listings. You can also provide individual image URLs or files as an argument.
We've provided a dummy-pii/ folder containing sample PII for you to test Octopii with. Pass it as an argument and you'll get the following output
owais@artemis ~ $ python3 octopii.py dummy-pii/
Searching for PII in dummy-pii/dummy-drivers-license-nebraska-us.jpg
{
"file_path": "dummy-pii/dummy-drivers-license-nebraska-us.jpg",
"pii_class": "Nebraska Driver's License",
"country_of_origin": "United States",
"faces": 1,
"identifiers": [],
"emails": [],
"phone_numbers": [
"4000002170"
],
"addresses": [
"Nebraska"
]
}
Searching for PII in dummy-pii/dummy-PAN-India.jpg
{
"file_path": "dummy-pii/dummy-PAN-India.jpg",
"pii_class": "Permanent Account Number",
"country_of_origin": "India",
"faces": 0,
"identifiers": [],
"emails": [],
"phone_numbers": [],
"addresses": [
"INDIA"
]
}
...
A file named output.txt is created, containing output from the tool. This file is appended to sequentially in real-time.
Octopii uses Tesseract for Optical Character Recognition (OCR) and NLTK for Natural Language Processing (NLP) to detect for strings of personal identifiable information. This is done via the following steps:
Octopii scans for images (jpg and png) and documents (pdf, doc, txt etc). It supports 3 sources:
Images are detected via Python Imaging Library (PIL) and are opened with OpenCV. PDFs are converted into a list of images and are scanned via OCR. Text-based file types are read into strings and are scanned without OCR.
A binary classification image detection technique - known as a "Haar cascade" - is used to detect faces within images. A pre-trained cascade model is supplied in this repo, which contains cascade data for OpenCV to use. Multiple faces can be detected within the same PII image, and the number of faces detected is returned.
Images are then "cleaned" for text extraction with the following image transformation steps:
Since these steps strip away image data (including colors in photographs), this image cleaning process occurs after attempting face detection.
Tesseract is used to grab all text strings from an image/file. It is then tokenized into a list of strings, split by newline characters ('\n') and spaces (' '). Garbled text, such as null strings and single characters are discarded from this list, resulting in an 'intelligible' list of potential words.
This list of words is then fed into a similarity checker function. This function uses Gestalt pattern matching to compare each word extracted from the PII document with a list of keywords, present in definitions.json. This check happens once per cleaning. The number of times a word occurs from the keywords list is counted and this is used to derive a confidence score. When a particular definition's keywords appear repeatedly in these scans, that definition gets the highest score and is picked as the predicted PII class.
Octopii also checks for sensitive PII substrings such as emails, phone numbers and common government ID unique identifiers using regular expressions. It can also extract geolocation data such as addresses and countries using Natural Language Processing.
The output consists of the following:
file_path: Where the file containing PII can be foundpii_class: The type of PII this file containscountry_of_origin: Where this PII originates from.identifiers: Unique identifiers, codes or numbers that may be used to target the individual mentioned in the PII.emails and phone_numbers: Contact information in the file.addresses: Any form of geolocation data in the PII. This may be used to triangulate an individual's location.Click here to read about how you can contribute to Octopii.
...and countless others
This tool is intended for research and educational purposes only. RedHunt Labs and other contributors to this project take no responsibility for malicious usage of this tool.
Copyright © 2023 RedHunt Labs Private Limited.
By Owais Shaikh