Secure Builds with AWS CodeBuild and LayeredInsight

This post is written by Asif Awan, Chief Technology Officer of Layered Insight, Subin Mathew – Software Development Manager for AWS CodeBuild, and Asif Khan – Solutions Architect

Enterprises adopt containers because they recognize the benefits: speed, agility, portability, and high compute density. They understand how accelerating application delivery and deployment pipelines makes it possible to rapidly slipstream new features to customers. Although the benefits are indisputable, this acceleration raises concerns about security and corporate compliance with software governance. In this blog post, I provide a solution that shows how Layered Insight, the pioneer and global leader in container-native application protection, can be used with seamless application build and delivery pipelines like those available in AWS CodeBuild to address these concerns.

Layered Insight solutions

Layered Insight enables organizations to unify DevOps and SecOps by providing complete visibility and control of containerized applications. Using the industry’s first embedded security approach, Layered Insight solves the challenges of container performance and protection by providing accurate insight into container images, adaptive analysis of running containers, and automated enforcement of container behavior.

AWS CodeBuild

AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages that are ready to deploy. With CodeBuild, you don’t need to provision, manage, and scale your own build servers. CodeBuild scales continuously and processes multiple builds concurrently, so your builds are not left waiting in a queue. You can get started quickly by using prepackaged build environments, or you can create custom build environments that use your own build tools.

Problem Definition

Security and compliance concerns span the lifecycle of application containers. Common concerns include:

Visibility into the container images. You need to verify the software composition information of the container image to determine whether known vulnerabilities associated with any of the software packages and libraries are included in the container image.

Governance of container images is critical because only certain open source packages/libraries, of specific versions, should be included in the container images. You need support for mechanisms for blacklisting all container images that include a certain version of a software package/library, or only allowing open source software that come with a specific type of license (such as Apache, MIT, GPL, and so on). You need to be able to address challenges such as:

·       Defining the process for image compliance policies at the enterprise, department, and group levels.

·       Preventing the images that fail the compliance checks from being deployed in critical environments, such as staging, pre-prod, and production.

Visibility into running container instances is critical, including:

·       CPU and memory utilization.

·       Security of the build environment.

·       All activities (system, network, storage, and application layer) of the application code running in each container instance.

Protection of running container instances that is:

·       Zero-touch to the developers (not an SDK-based approach).

·       Zero touch to the DevOps team and doesn’t limit the portability of the containerized application.

·       This protection must retain the option to switch to a different container stack or orchestration layer, or even to a different Container as a Service (CaaS ).

·       And it must be a fully automated solution to SecOps, so that the SecOps team doesn’t have to manually analyze and define detailed blacklist and whitelist policies.

Solution Details

In AWS CodeCommit, we have three projects:
●     “Democode” is a simple Java application, with one buildspec to build the app into a Docker container (run by build-demo-image CodeBuild project), and another to instrument said container (instrument-image CodeBuild project). The resulting container is stored in ECR repo javatestasjavatest:20180415-layered. This instrumented container is running in AWS Fargate cluster demo-java-appand can be seen in the Layered Insight runtime console as the javatestapplication in us-east-1.
●     aws-codebuild-docker-imagesis a clone of the official aws-codebuild-docker-images repo on GitHub . This CodeCommit project is used by the build-python-builder CodeBuild project to build the python 3.3.6 codebuild image and is stored at the codebuild-python ECR repo. We then manually instructed the Layered Insight console to instrument the image.
●     scan-java-imagecontains just a buildspec.yml file. This file is used by the scan-java-image CodeBuild project to instruct Layered Assessment to perform a vulnerability scan of the javatest container image built previously, and then run the scan results through a compliance policy that states there should be no medium vulnerabilities. This build fails — but in this case that is a success: the scan completes successfully, but compliance fails as there are medium-level issues found in the scan.

This build is performed using the instrumented version of the Python 3.3.6 CodeBuild image, so the activity of the processes running within the build are recorded each time within the LI console.

Build container image

Create or use a CodeCommit project with your application. To build this image and store it in Amazon Elastic Container Registry (Amazon ECR), add a buildspec file to the project and build a container image and create a CodeBuild project.

Scan container image

Once the image is built, create a new buildspec in the same project or a new one that looks similar to below (update ECR URL as necessary):

version: 0.2
phases:
  pre_build:
    commands:
      - echo Pulling down LI Scan API client scripts
      - git clone https://github.com/LayeredInsight/scan-api-example-python.git
      - echo Setting up LI Scan API client
      - cd scan-api-example-python
      - pip install layint_scan_api
      - pip install -r requirements.txt
  build:
    commands:
      - echo Scanning container started on `date`
      - IMAGEID=$(./li_add_image --name <aws-region>.amazonaws.com/javatest:20180415)
      - ./li_wait_for_scan -v --imageid $IMAGEID
      - ./li_run_image_compliance -v --imageid $IMAGEID --policyid PB15260f1acb6b2aa5b597e9d22feffb538256a01fbb4e5a95

Add the buildspec file to the git repo, push it, and then build a CodeBuild project using with the instrumented Python 3.3.6 CodeBuild image at <aws-region>.amazonaws.com/codebuild-python:3.3.6-layered. Set the following environment variables in the CodeBuild project:
●     LI_APPLICATIONNAME – name of the build to display
●     LI_LOCATION – location of the build project to display
●     LI_API_KEY – ApiKey:<key-name>:<api-key>
●     LI_API_HOST – location of the Layered Insight API service

Instrument container image

Next, to instrument the new container image:

1. In the Layered Insight runtime console, ensure that the ECR registry and credentials are defined (click the Setup icon and the ‘+’ sign on the top right of the screen to add a new container registry). Note the name given to the registry in the console, as this needs to be referenced in the li_add_imagecommand in the script, below.
2. Next, add a new buildspec (with a new name) to the CodeCommit project, such as the one shown below. This code will download the Layered Insight runtime client, and use it to instruct the Layered Insight service to instrument the image that was just built:

   version: 0.2
   phases:
   pre_build:
   commands:
   echo Pulling down LI API Runtime client scripts
   git clone https://github.com/LayeredInsight/runtime-api-example-python
   echo Setting up LI API client
   cd runtime-api-example-python
   pip install layint-runtime-api
   pip install -r requirements.txt
   build:
   commands:
   echo Instrumentation started on `date`
   ./li_add_image --registry "Javatest ECR" --name IMAGE_NAME:TAG --description "IMAGE DESCRIPTION" --policy "Default Policy" --instrument --wait --verbose

3. Commit and push the new buildspec file.
4. Going back to CodeBuild, create a new project, with the same CodeCommit repo, but this time select the new buildspec file. Use a Python 3.3.6 builder – either the AWS or LI Instrumented version.
5. Click Continue
6. Click Save
7. Run the build, again on the master branch.
8. If everything runs successfully, a new image should appear in the ECR registry with a -layered suffix. This is the instrumented image.

Run instrumented container image

When the instrumented container is now run — in ECS, Fargate, or elsewhere — it will log data back to the Layered Insight runtime console. It’s appearance in the console can be modified by setting the LI_APPLICATIONNAME and LI_LOCATION environment variables when running the container.

Conclusion

In the above blog we have provided you steps needed to embed governance and runtime security in your build pipelines running on AWS CodeBuild using Layered Insight.