credentialdigger 2.2.0

Credential Digger

Credential Digger

Credential Digger is a GitHub scanning tool that identifies hardcoded credentials (Passwords, API Keys, Secret Keys, Tokens, personal information, etc), filtering the false positive data through machine learning models.

• Why
• Requirements
• Install
• Quick launch with UI
• Advanced Install
  • Install from source
  • Download machine learning models
  • Configure the regular expressions Scanner
• Usage
  • Scan a repository
  • Fine-tuning
• Wiki
• Contributing
• News

Why

In data protection, one of the most critical threats is represented by hardcoded (or plaintext) credentials in open-source projects. Several tools are already available to detect leaks in open-source platforms, but the diversity of credentials (depending on multiple factors such as the programming language, code development conventions, or developers' personal habits) is a bottleneck for the effectiveness of these tools. Their lack of precision leads to a very high number of pieces of code incorrectly detected as leaked secrets. Data wrongly detected as a leak is called false positive data, and compose the huge majority of the data detected by currently available tools.

The goal of Credential Digger is to reduce the amount of false positive data on the output of the scanning phase by leveraging machine learning models.

Requirements

Credential Digger supports Python >= 3.6 and < 3.8, and works only with LINUX systems.

Docker and Docker Compose are needed if you want run an image of Credential Digger as a container, as discussed here.

Install

You can either install the module using pip or build it from source

pip install credentialdigger

Please make sure to add the scanning rules to the database before scanning a repo.

Scan your first repo for leaks

python -m credentialdigger scan https://github.com/user/repo --sqlite /path/to/data.db

Quick Launch

To have a ready-to-use instance of Credential Digger, with the UI:

git clone https://github.com/SAP/credential-digger.git
cd credential-digger
cp .env.sample .env
sudo docker-compose up --build

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

The docker container for Credential Digger uses a local sqlite database.

Quick Install with an external database

Another ready-to-use instance of Credential Digger with the UI, but using a dockerized postgres database instead of a local sqlite one:

git clone https://github.com/SAP/credential-digger.git
cd credential-digger
cp .env.sample .env
vim .env  # set credentials for postgres
sudo docker-compose -f docker-compose.postgres.yml up --build

Differently from the sqlite version, here we need to configure the .env file with the credentials for postgres (by modifying POSTGRES_USER, POSTGRES_PASSWORD and POSTGRES_DB).

Most advanced users may also wish to use an external postgres database instead of the dockerized one we provide in our docker-compose.postgres.yml.

Advanced Install

First, you need to install the regular expression matching library Hyperscan, where you can find the complete installation process for all platforms here. Be sure to have build-essential and python3-dev too.

sudo apt install libhyperscan-dev
sudo apt install build-essential
sudo apt install python3-dev

Install from source

Configure a virtual environment for Python 3 (optional) and clone the main branch

virtualenv --system-site-packages -p python3 ./venv
source ./venv/bin/activate

git clone https://github.com/SAP/credential-digger.git
cd credential-digger

Install the requirements from requirements.txt file and install the library:

pip install -r requirements.txt
python setup.py install

Download machine learning models

Credential Digger leverages machine learning models to filter false positives, especially in the identification of passwords:

• Path Model: A lot of fake credentials reside in example files such as documentation, examples or test files, since it is very common for developers to provide test code for their projects. The Path Model analyzes the path of each discovery and classifies it as false positive when needed.

• Snippet Model: Identify the portion of code used to authenticate with passwords, and distinguish between real and dummy passwords. This model is composed of a pre-processing step (Extractor) and a classification step (Classifier).

Download the binaries:

export path_model=https://github.com/SAP/credential-digger/releases/download/PM-v1.0.1/path_model-1.0.1.tar.gz
export snippet_model=https://github.com/SAP/credential-digger/releases/download/SM-v1.0.0/snippet_model-1.0.0.tar.gz

python -m credentialdigger download path_model
python -m credentialdigger download snippet_model

WARNING: If you build the code from scratch (i.e., you don't install the client via pip), don't run the download command from the installation folder of credentialdigger in order to avoid errors in linking.

WARNING: We provide the pre-trained models, but we do not guarantee the efficiency of these models. If you want more accurate machine learning models, you can train your own models (just replace the binaries with your own models) or use the fine-tuning option.

Configure the regular expressions Scanner

One of the core components of Credential Digger is the regular expression scanner. You can choose the regular expressions rules you want (just follow the template here). We provide a list of patterns in the rules.yml file, that are included in the UI.

When following the advanced user steps, you need to set your own rules. In a Python terminal:

from credentialdigger import SqliteClient

c = SqliteClient(path='/path/to/data.db')

c.add_rules_from_file('/path/to/rules.yml')

WARNING: These instructions are valid for the SqliteClient.

Usage

When using docker-compose, use the UI available at http://localhost:5000/

When installing credentialdigger from pip or from source, you can instantiate the client and scan a repository.

Instantiate a client:

from credentialdigger import SqliteClient

c = SqliteClient(path='/path/to/data.db')

Scan a repository

new_discoveries = c.scan(repo_url='https://github.com/user/repo',
                         models=['PathModel', 'SnippetModel'],
                         debug=True)

WARNING: Make sure you added rules before scanning a repository.

WARNING: Make sure you download the models before using them in a scan.

Please refer to the Wiki for further information on the arguments.

CLI - Command Line Interface

Credential Digger also offers a simple CLI to scan a repository. The CLI supports both sqlite and postgres databases. In case of postgres, the user needs to export the credentials (the same appearing in the .env file) as environment variables. In case of sqlite, the path of the db must be passed as argument.

# Scan using SqliteClient
python -m credentialdigger scan https://github.com/user/repo --sqlite cdigger.db

# Scan using PgClient
export POSTGRES_USER=...
export ...
python -m credentialdigger scan https://github.com/user/repo

Since rules are needed to scan a repository, the CLI also offers the possibility to add rules from a file.

# Add the rules to the database
python -m credentialdigger add_rules /path/to/rules.yml --sqlite cdigger.db

Fine-tuning

Credential Digger offers the possibility to fine-tune the snippet model, by retraining a model on each repository scanned. If you want to activate this option, set generate_snippet_extractor=True. You need to donwload the snippet model before using the fine-tuning option.

Wiki

For further information, please refer to the Wiki

Contributing

We invite your participation to the project through issues and pull requests. Please refer to the Contributing guidelines for how to contribute.

News

• Credential Digger announcement
• Credential Digger is now supporting Keras machine learning models