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A utility to (re-)import findings and language data into DefectDojo

Project description

dd-import

A utility to (re-)import findings and language data into DefectDojo

Findings and languages can be imported into DefectDojo via an API. To make automated build and deploy pipelines easier to implement, dd-import provides some convenience functions:

  • Products, engagements and tests will be created if they are not existing. This avoids manual preparation in DefectDojo or complicated steps within the pipeline.
  • Product types, products, engagements and tests are referenced by name. This make pipelines more readable than using IDs.
  • Build information for build_id, commit_hash and branch_tag can be updated when uploading findings.
  • No need to deal with curl and its syntax within the pipeline. This makes pipelines shorter and better readable.
  • All parameters are provided via environment variables, which works well with pipeline definitions like GitHub Actions or GitLab CI.

User guide

Installation and commands

Python

dd-import can be installed with pip. Only Python 3.8 and up is suported.

pip install dd-import

The command dd-reimport-findings re-imports findings into DefectDojo. Even though the name suggests otherwise, you do not need to do an initial import first.

The command dd-import-languages imports languages data that have been gathered with the tool cloc, see Languages and lines of code for more details.

Docker

Docker images can be found in https://hub.docker.com/r/maibornwolff/dd-import.

A re-import of findings can be started with

docker run --rm dd-import:latest ./bin/dd-reimport-findings.sh

Importing languages data can be started with

docker run --rm dd-import:latest ./bin/dd-import-languages.sh

Please note you have to set the environment variables as described below and mount a folder containing the file with scan results when running the docker container.

/usr/local/dd-import is the working directory of the docker image, all commands are located in the /usr/local/dd-import/bin folder.

Parameters

All parameters need to be provided as environment variables

Parameter Re-import findings Import languages Remark
DD_URL Mandatory Mandatory Base URL of the DefectDojo instance
DD_API_KEY Mandatory Mandatory Shall be defined as a secret, eg. a protected variable in GitLab or an encrypted secret in GitHub
DD_PRODUCT_TYPE_NAME Mandatory Mandatory A product type with this name must exist
DD_PRODUCT_NAME Mandatory Mandatory If a product with this name does not exist, it will be created
DD_ENGAGEMENT_NAME Mandatory - If an engagement with this name does not exist for the given product, it will be created
DD_TEST_NAME Mandatory - If a test with this name does not exist for the given engagement, it will be created
DD_TEST_TYPE_NAME Mandatory - From DefectDojo's list of test types, eg. Trivy Scan
DD_FILE_NAME Optional Mandatory
DD_ACTIVE Optional - Default: true
DD_VERIFIED Optional - Default: true
DD_MINIMUM_SEVERITY Optional -
DD_PUSH_TO_JIRA Optional - Default: false
DD_CLOSE_OLD_FINDINGS Optional - Default: true
DD_VERSION Optional -
DD_ENDPOINT_ID Optional -
DD_BUILD_ID Optional -
DD_COMMIT_HASH Optional -
DD_BRANCH_TAG Optional -

Usage

This snippet from a GitLab CI pipeline serves as an example how dd-import can be integrated to upload data during build and deploy using the docker image:

variables:
  DD_PRODUCT_TYPE_NAME: "Showcase"
  DD_PRODUCT_NAME: "DefectDojo Importer"
  DD_ENGAGEMENT_NAME: "GitLab"

...

safety:
  stage: test
  image: python:3.9-alpine
  tags:
    - build
  script:
    - pip install safety
    - safety check -r requirements.txt --json --output safety.json
  artifacts:
    paths:
    - safety.json
    when: always
    expire_in: 1 day

cloc:
  stage: test
  image: node:16
  tags:
    - build
  before_script:
    - npm install -g cloc
  script:
    - cloc src --json -out cloc.json
  artifacts:
    paths:
    - cloc.json
    when: always
    expire_in: 1 day

upload-safety:
  image: maibornwolff/dd-import:latest
  needs:
    - job: safety
      artifacts: true  
  stage: upload
  tags:
    - build
  variables:
    DD_TEST_NAME: "Safety"
    DD_TEST_TYPE_NAME: "Safety Scan"
    DD_FILE_NAME: "safety.json"
  script:
    - /usr/local/dd-import/bin/dd-reimport-findings.sh

upload-cloc:
  image: maibornwolff/dd-import:latest
  needs:
    - job: cloc
      artifacts: true  
  stage: upload
  tags:
    - build
  variables:
    DD_FILE_NAME: "cloc.json"
  script:
    - /usr/local/dd-import/bin/dd-import-languages.sh
  • variables - Definition of some environment variables that will be used for several uploads. DD_URL and DD_API_KEY are not defined here because they are protected variables for the GitLab project.
  • safety - Example for a vulnerability scan with safety. Output will be stored in JSON format (safety.json).
  • cloc - Example how to calculate the lines of code with cloc. Output will be stored in JSON format (cloc.json).
  • upload_safety - This step will be executed after the safety step, gets its output file and sets some variables specific for this step. Then the script to import the findings from this scan is executed.
  • upload_cloc - This step will be executed after the cloc step, gets its output file and sets some variables specific for this step. Then the script to import the language data is executed.

Another example, showing how to use dd-import within a GitHub Action, can be found in dd-import_example.yml.

Developer guide

Testing

./bin/runUnitTests.sh - Runs the unit tests and reports the test coverage.

./bin/runDockerUnitTests.sh - First creates the docker image and then starts a docker container in which the unit tests are executed.

License

Licensed under the 3-Clause BSD License

Project details


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