CodeQL Scanner Reference

You can scan your code repositories using CodeQL, an analysis engine used by developers to automate security checks, and by security researchers to perform variant analysis.

The following steps outline the basic workflow:

1. Run a CodeQL scan, either externally or as part of a Run step, and publish the results to SARIF.

2. Add the SARIF data to your pipeline. If you ran the scan outside the pipeline, do the following:

   1. In the stage where you ingest the results, go to Overview > Shared Paths and create a folder under /shared such as /shared/customer_artifacts.

   2. Use a Run step to add your scan results to the shared folder.

3. Use a CodeQL step to ingest the results.

   note

   Currently, the CodeQL scanner template is behind the feature flag STO_STEP_PALETTE_CODEQL. Contact Harness Support to enable this feature.

   Alternately, you can use a Custom ingest step to ingest your data instead.

This topic includes an end-to-end YAML pipeline that illustrates this workflow.

Before you begin

Root access requirements

You need to run the scan step with root access if you need to add trusted certificates to your scan images at runtime.

note

You can set up your STO scan images and pipelines to run scans as non-root and establish trust for your own proxies using self-signed certificates. For more information, go to Configure STO to Download Images from a Private Registry.

CodeQL step configuration

The recommended workflow is to add a CodeQL step to a Security Tests or CI Build stage and then configure it as described below. You can also configure CodeQL scans programmatically by copying, pasting, and editing the YAML definition.

note

• UI configuration support is currently limited to a subset of scanners. Extending UI support to additional scanners is on the Harness engineering roadmap.
• Each scanner template shows only the options that apply to a specific scan. If you're setting up a repository scan, for example, the UI won't show Container Image settings.
• Docker-in-Docker is not required for these steps unless you're scanning a container image. If you're scanning a repository using Bandit, for example, you don't need to set up a Background step running DinD.
• Support is currently limited to Kubernetes and Harness Cloud AMD64 build infrastructures only.

CodeQL Step Palette

Scan Mode

• Ingestion Ingestion scans are not orchestrated. The Security step ingest results from a previous scan (for a scan run in an previous step) and then normallizes and compresses the results.

Scan Configuration

The predefined configuration to use for the scan. All scan steps have at least one configuration.

Target

Type

• Repository Scan a codebase repo.

Name

The Identifier that you want to assign to the target you’re scanning in the pipeline. Use a unique, descriptive name such as codebaseAlpha or jsmith/myalphaservice. Using descriptive target names will make it much easier to navigate your scan data in the STO UI.

Variant

An identifier for a specific variant to scan, such as the branch name or image tag. This identifier is used to differentiate or group results for a target. Harness maintains a historical trend for each variant.

You can see the target name, type, and variant in the Test Targets UI:

Ingestion file

The results data file to use when running an Ingestion scan. STO steps can ingest scan data in SARIF and Harness Custom JSON format. Generally an Ingestion scan consists of a scan step (to generate the data file) and an ingestion step (to ingest the data file).

Log Level, CLI flags, and Fail on Severity

Log Level

The minimum severity of the messages you want to include in your scan logs. You can specify one of the following:

• DEBUG
• INFO
• WARNING
• ERROR

Additional CLI flags

You can use this field to customize the scan with specific command-line arguments supported by that scanner.

Fail on Severity

Every Security step has a Fail on Severity setting. If the scan finds any vulnerability with the specified severity level or higher, the pipeline fails automatically. You can specify one of the following:

• CRITICAL
• HIGH
• MEDIUM
• LOW
• INFO
• NONE — Do not fail on severity

The YAML definition looks like this: fail_on_severity : critical # | high | medium | low | info | none

Settings

You can add a tool_args setting to run the CodeQL scanner binary with specific command-line arguments. For example, you can skip certain tests using -skip followed by a list of test IDs: tool_args = -skip testID_1, testID_3, testID_5

Additional Configuration

In the Additional Configuration settings, you can use the following options:

• Privileged
• Image Pull Policy
• Run as User
• Set Container Resources

Advanced settings

In the Advanced settings, you can use the following options:

• Conditional Execution
• Failure Strategy
• Looping Strategy
• Policy Enforcement

CodeQL pipeline example

The following pipeline example is an ingestion workflow. It consists of two steps. A Run step installs CodeQL, scans the repository defined in the Codebase object, and publishes the scan results to a SARIF file. A CodeQL step then ingests the SARIF file.

pipeline:
  projectIdentifier: STO
  orgIdentifier: default
  tags: {}
  properties:
    ci:
      codebase:
        connectorRef: wwdvpwa
        repoName: dvpwa
        build: <+input>
  stages:
    - stage:
        name: codeql
        identifier: codeql
        type: CI
        spec:
          cloneCodebase: true
          execution:
            steps:
              - step:
                  type: Run
                  name: codeql_analyze
                  identifier: codeql_analyze
                  spec:
                    connectorRef: account.harnessImage
                    image: ubuntu:20.04
                    shell: Sh
                    command: |-
                      #!/bin/bash


                      # Change the working directory to the app directory.
                      mkdir /app
                      cd /app

                      # Update and upgrade the apt packages.
                      apt update -y -q
                      apt upgrade -y -q 

                      # Install the wget and tar packages and python3.
                      export DEBIAN_FRONTEND="noninteractive"
                      apt install -y -q wget tar python3.9-venv python3.9 build-essential

                      # Download the CodeQL bundle.
                      wget -q https://github.com/github/codeql-action/releases/latest/download/codeql-bundle-linux64.tar.gz

                      # Extract the CodeQL bundle.
                      tar -xvzf ./codeql-bundle-linux64.tar.gz -C /app/

                      # Set the PATH environment variable to include the CodeQL directory.
                      export PATH="${PATH}:/app/codeql"

                      # Resolve the CodeQL packs
                      codeql resolve qlpacks

                      # Move back to the code folder before scanning
                      cd /harness

                      # Create a CodeQL database.
                      codeql database create python_database --language=python

                      # Run the CodeQL analyzer.
                      codeql database analyze python_database --format=sarif-latest --output=/shared/customer_artifacts/dvpwa-codeql-results.sarif
                    imagePullPolicy: Always
                    resources:
                      limits:
                        memory: 2G
                        cpu: 2000m
              - step:
                  type: CodeQL
                  name: CodeQL_ingest
                  identifier: CodeQL_1
                  spec:
                    mode: ingestion
                    config: default
                    target:
                      name: codeql-dvpwa
                      type: repository
                      variant: test
                    advanced:
                      log:
                        level: info
                      fail_on_severity: critical
                    ingestion:
                      file: /shared/customer_artifacts/dvpwa-codeql-results.sarif
          sharedPaths:
            - /shared/customer_artifacts/
          infrastructure:
            type: KubernetesDirect
            spec:
              connectorRef: hyharnesslegate
              namespace: harness-delegate-ng
              automountServiceAccountToken: true
              nodeSelector: {}
              os: Linux
  identifier: CodeQLv3
  name: CodeQL-v3