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Credential Digger

Credential Digger is a Github scanning tool that identifies hardcoded credentials (Passwords, API Keys, Secret Keys, Tokens, personal information, etc). Credential Digger has a clear advantage compared to the other Github scanners in terms of False Positive reduction in the scan reports. Credential Digger is using two Machine Learning Models to identify false positivies, especially in Password identification:

Path Model: Identify the portion of code that contains fake credentials used for testing and example purposes (ex. Unit tests).
Snippet Model: Identify the portion of code used to authenticate with passwords, and distinguish between real and fake passwords.

Architecture

Credential Digger finds credentials hardcoded in a repository. The tool is composed of:

Postgres database
Python client
User interface

Database

The database is structured in the following way (arrows point to foreign keys).

DB Structure

Project structure

The project includes 3 components: a db (sql folder), a client (credentialdigger folder), and a user interface (ui folder).

`sql`

create_table.sql defines the db schema.

Note that, given the file_name and commit_hash of a discovery, both the commit and the file can be accessible at addresses:

REPO_URL/commit/COMMIT_HASH
REPO_URL/blob/COMMIT_HASH/file_name

`credentialdigger`

This client can be used to easily interact with the db. It offers a scanner for git repositories, based on Hyperscan (others can be implemented).

Please note that the database must be up and running.

`ui`

The user interface can be used to easily perform scans and flag the discoveries.

Install

Prepare the .env file and edit it with the correct data

cp .env.sample .env
vim .env  # Insert real credentials

Run the db and the ui using docker-compose:
```
sudo docker-compose up --build
```
Consider not to expose the db port in production.

The ui is available at http://localhost:5000/

Build from scratch

Execute step 1. as above

Run the db using docker-compose:

sudo docker-compose up --build postgres

Install the dependencies for the client.

sudo apt install libhyperscan-dev libpq-dev

Install the Python requirements from the requirements.txt file.
```
pip install -r requirements.txt
```
Install the client
```
python setup.py install
```

Pypi

A package is available on pypi. Install the client:

pip install credentialdigger

Please note that the database must be run separately.

Run the db on a different machine

In case the db and the client are run on different machines, then clone this repository on both of them.

Then, execute the steps 1. and 2. as described in the installation section above on the machine running the db, and execute the steps described in the "build from scratch" section on the machine running the client (or install the client with pip).

In case the db and the client/ui run on separate machines, the port of the db must be exposed.

In case the UI must be run on a dedicated machine, update the .env as needed, and run:

sudo docker-compose up --build credential_digger

Use machine learning models

Machine learning models can be downloaded and used to automatically filter false positive discoveries during a scan.

After installing the client, they can be downloaded as follows.

# Export the url into an environment variable
export model_name=https://...
# Download and link the model
python -m credentialdigger download model_name

NB: Don't run the download command from the installation folder of credentialdigger in order to avoid errors in linking.

Refer to the Wiki for further information.

Integrate ML Models in the UI

ML models are not supported yet in the UI "out of the box", but they can be enabled with a little effort.

To use ML models in the docker container running the ui, set their address in the .env file. If the address is correct, the model will be installed at container building time. However, the current server is not executing any model during the scan of a repository. To enable a model, it must be listed as argument in the scan function (as would be done in the client).in the ui/server.py file.

Usage (client)

from credentialdigger.cli import Client
c = Client(dbname='MYDB', dbuser='MYUSER', dbpassword='*****',
           dbhost='localhost', dbport=5432)

Wiki

Refer to the Wiki for further information.

Metadata

Release files for credentialdigger 1.0.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for credentialdigger 1.0.1
File	Size	Uploaded
credentialdigger-1.0.1.tar.gz	37.1 kB	Jun 23, 2020	Details

Built distribution (wheel)

Table of built distributions (wheels) for credentialdigger 1.0.1
File	Interpreter	ABI	Platform	Reset
credentialdigger-1.0.1-py3-none-any.whl 48.8 kB Jun 23, 2020	Python 3	none	any	Details

Total release size: 85.9 kB

Release files / credentialdigger-1.0.1.tar.gz

Download URL	credentialdigger-1.0.1.tar.gz
Size	37.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`904675fde1a6c4efc1524e26569c9ffe9d6f2af128adb5a62b49db756291bc33`
BLAKE2b-256 checksum How to use checksums	`0c7922160548785c920e227d4844a0af7140a22052240dfee12349dda12062e5`
Upload date	Jun 23, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.5.0.1 requests/2.24.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.7.3`

Release files / credentialdigger-1.0.1-py3-none-any.whl

Download URL	credentialdigger-1.0.1-py3-none-any.whl
Size	48.8 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`be749576edbd563420b945f0f47597f4865ac49b474f3768bd85d5d5ad705f15`
BLAKE2b-256 checksum How to use checksums	`0c84c625f91b54a66a7e9b824f993f903f48d2796a5083ca2a014988b0c0c639`
Upload date	Jun 23, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.5.0.1 requests/2.24.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.7.3`