7 ways to build slimmer/lighter (Linux) containers

The goal of this ticket is to present a few ways to obtain lighter container images. But why it’s so important to build and use lighter containers ?

Lighter containers means :

  • less disk space used to store the images
  • faster transfer (pull/push) of the images to/from the container registry,
  • faster build process of images and easier to update them (because it contains less components)
  • better security posture (less components, less vulnerabilities, smaller attack surface).

The hints that I will present could be sorted in two different categories: what to put into an image (to be lighter) and how to build an image (to be lighter).

What to put into your image

1. Use the lighter base image as possible

Choose the base image based of your needs of you application and try to use the minimal base image. If for example your application is Java based then choose as base image something like openjdk:19-slim-buster not a base image containing Java + other components that you don’t need. Following this approach is almost effortless but you will depend of the (base) image maintainer for any updates.

A better, but more difficult and more time consuming approach is to start from a bare minimal image like Alpine or Red Hat Universal Base Image 8 Minimal and install on top whatever components/packages you need. Following this approach will give you much more flexibility because you will be able to patch the needed components as the pace of their update; the drawback is that you have to spend some time creating the Dockerfile that builds the needed image.

2. Use multi-stage build

With multi-stage builds you can use multiple FROM statements in your Dockerfile. Each FROM instruction can use a different base, and each of them begins a new stage of the build. For a very good explanation of this feature you can see the Docker documentation.

The example given in the Docker documentation is around compiling a Go application into a stage and just copy the desired artifacts into another stage that will be used in the final image.

To illustrate the multi-stage build I will use as example Java 9 and the jlink tool that generates a custom Java runtime image that contains only the platform modules that are required for a given application:

FROM openjdk:11.0.14-jdk AS initial_jdk

# build a custom JRE
RUN jlink --add-modules java.management,java.base,java.logging,java.naming,java.sql,java.xml \
 --output ./customJre/ --strip-debug --no-man-pages --no-header-files \
 --compress=2

# use as base image the ubi minimal
FROM registry.access.redhat.com/ubi8/ubi-minimal:8.5-230

# copy the custom JRE into the final image
COPY --from=initial_jdk ./customJre /opt/java/openjdk

ENV JAVA_HOME=/opt/java/openjdk \
    PATH="/opt/java/openjdk/bin:$PATH"

3. Deactivate the package manager cache

Different package managers are copying the installed dependencies also in cache folders so it’s not needed to re-download a dependency if is necessary to be re-installed. Obviously, in the case of containers the cache feature should be deactivated or the cache folders should be deleted after the dependencies installation.

A few examples of package managers and how to deactivate or delete the cache:

  • pip cache purge – Remove all items from the cache.
  • dnf clean – Performs cleanup of temporary files kept for repositories. This includes any such data left behind from disabled or removed repositories as well as for different distribution release versions.
  • microndnf clean
  • yum clean – Same definition as dnf clean

Here is an example of a Dockerfile with and without the usage of the cache clean:

#No dnf Clean
FROM registry.access.redhat.com/ubi8/ubi-minimal:8.5-230

RUN microdnf install fontconfig \
&& microdnf install libXtst
#With dnf clean
FROM registry.access.redhat.com/ubi8/ubi-minimal:8.5-230

RUN microdnf install fontconfig \
&& microdnf install libXtst \
&& microdnf clean all

And here are the size of the two images:

The usage of deactivation of package manager cache should be combined with either hint number 4 (Minimize the number of RUN, COPY, ADD instructions) or hint number 5 (Use the squash flag of docker/podman build).

How to build a lighter image

This hints are around the container UnionFS (Union File System) and will explain how to create less or smaller image layers.

4. Minimize the number of RUN, COPY, ADD instructions

Only the instructions RUN, COPY, ADD create layers; each usage of one of this instructions will create a new layer into the final image. Minimizing the number of this instructions will minimize the number of image layers which will minimize the size of the final image.

Let’s use the following Dockerfile as (faulty) example:

FROM registry.access.redhat.com/ubi8/ubi-minimal:8.5-230
# call twice the RUN instruction
RUN microdnf install fontconfig 
RUN microdnf install libXtst

In this Dockerfile we called twice the RUN instruction; the image (having an id starting with 14e7) will have 4 layers:

docker inspect --format '{{join .RootFS.Layers "\n "}}' 14e7

sha256:44f62afd0479b4c2059f2a01b61a33a6e47b0a903b17a9fd65a8df8d4cfe806c
sha256:87cd41b1f9f880f62765bc510b9f241c5532cb919182ba453d87a28783b24d5b
sha256:acf320641a3c8165491b3b022d088ce7170820dbcaf31789db9b9b8a55568594
sha256:9c29e387846f1413e91046c9c194c9556ee4a66d993aa56a7ad7ecbe78304dbd

Now let’s minimize the number of RUN instructions; we will have a single RUN instruction containing multiple install commands:

FROM registry.access.redhat.com/ubi8/ubi-minimal:8.5-230
# call RUN only once
RUN microdnf install fontconfig && \ 
    microdnf install libXtst

The new image (having the id starting with d73) will have 3 layers:

docker inspect --format '{{join .RootFS.Layers "\n "}}' d73
sha256:44f62afd0479b4c2059f2a01b61a33a6e47b0a903b17a9fd65a8df8d4cfe806c
sha256:87cd41b1f9f880f62765bc510b9f241c5532cb919182ba453d87a28783b24d5b
sha256:5ad98570b3807cbd9dd51fd981e2c15d2fc7793061441ea395d3f332b722af35

5. Use the squash flag of docker/podman build

The squash flag is a flag of the docker build command which is still experimental that will squash newly built layers into a single new layer.

Podman build command also have a similar flag; Podman also have a squash-all flag that will squash all of the new image’s layers (including those inherited from a base image) into a single new layer.

6. Use .dockerignore to filter the content of Docker build context

The .dockerignore file is used to filter the content that will be used by the Docker build context to create an image.

The goal of this feature is just to have a faster build process ( because less files will be present in the build context) but it can help also in the case when accidentally the Dockerfile defines more files than needed.

7. Use external tools

I have to admit using external tools to obtain a slimmer image should not be the default or preferred solution especially after docker and podman implemented the squash flags. But if is not possible to use the existing solutions then here are some free tools that you could try:

  • jwilder/docker-squash
    • docker-squash is a utility to squash multiple docker layers into one in order to create an image with fewer and smaller layers.
    • it looks very similar to the docker build and podman build squash flags
    • project looks not active anymore
  • goldmann/docker-squash
    • can squash last n layers from an image
    • can squash from a selected layer to the end
    • project looks still active
  • docker-slim/docker-slim
    • docker-slim try to figure it out what files are useful from the target image by running a container of the target image.
    • docker-slim is capable to run static or dynamic analysis; it also capable to probe the running container using http requests.
    • docker-slim contains also a linter for Dockerfiles; Running the linter on the “No dnf Clean” Dockerfile from the hint nr3 (Deactivate the package manager cache) give the following results:
docker-slim lint

Conclusion

As you could see there are a few ways to create lighter images; some of the hints are “low hanging fruits” and can be applied systematically, like the usage of the squash flag (hint nr. 5) and the minimization of RUN commands (hint nr. 4); some others demand a little bit of thinking and try and error, like the usage of the right base image (hint nr. 1) or the usage of multi-stage builds (hint nr. 2).

Book Review: Container Security

This is the review of the Container Security book.

(My) Conclusion

I have mixed feelings about this book; to a scale of 1 to 10 I would give it a 7.

What I appreciated about it:

  • You can have a free (digital) copy of the book from here Aqua Container Security.
  • All the Linux security mechanisms that are used under the hood by containers are very well explained with multiple (valuable) examples; namespaces, cgroups, capabilities, system calls, AppArmor, SecComp. At the end of the day, container security is just a subset of Linux security.
  • No hidden (or un-hidden) publicity to any commercial tools, despite the fact that the author is working for AquaSecurity company.
  • A lot of references towards Internet accessible resources; unfortunately, the author is using url shortening so I wish you good luck to copy them into a browser if you have the paper version of the book.
  • Clear and concise writing style.

What I think could have been done better:

  • Even if the book is about security of/in containers, there is no general introduction of the container notion or the actual container landscape.
  • A lot of forward references in different chapters; usually in technical books you find backward references because (very often) the knowledge is build on top of the knowledge of previous chapters.
  • There are a few chapters which are very thin, especially toward the end; the last chapter (chapter 14) for example is just 2 pages long.
  • There is a companion website (https://containersecurity.tech/) but it contains just a single page.

1. Container Security Threats

This chapter defines different attack vectors for the containers and the infrastructure that they are running on. This attack vectors specifically linked to containers are:

  • Application code vulnerabilities
  • Badly configured images
  • Badly configured containers
  • Build Image attack
  • Supply chain attack
  • Vulnerable hosts
  • Exposed secrets
  • Insecure networking
  • Container runtime vulnerabilities
Containers attack vectors

The containers very often are deployed on cloud infrastructures very often using a multi-tenant model which brings new threats and new attack vectors on top of previous ones.

After presenting and explaining the problems that usage of containers will bring the author is focusing on (security) general guidelines that should be used when implementing different mitigations controls:

  • least privilege
    • each container should have a minimum set of permissions to fulfill it’s function.
  • defense in depth
  • reducing the attack surface
    • split the monolithic application in smaller, simpler microservices that would imply a less complex architecture that would reduce the attack surface.
  • limiting the blast radius
    • if one container is compromised some controls should be put in place to not affect the others software components
  • segregation of duties
    • permissions and credentials can be passed only into the containers that need them

2. Linux System Calls, Permissions and Capabilities

This chapter it presents the basics of Linux System calls, the Linux file permissions (an extensive explanation is done on the usage of of setuid and getuid) and the Linux Capabilities. For each of this Linux features some examples are given and the author emphasizes that this capabilities are heavily used by the containers and the containers run-times because at the end of the day, a container is just a Linux process running on a host.

3. Control Groups

This chapter is very similar with the previous one in the sense that it does not speak about containers but about a Linux security feature that is heavily used by the containers. This chapter is dedicated to Linux control groups (a.k.a cgroups) which have as goal to limit the resources, such as memory, CPU, network input/output, that a process or a group of processes can use.

Containers runtimes are using cgroups behind the scene to limit resources used by containers, so cgroups provides protection against a class of attacks that attempt to disrupt running applications by consuming excessive resources, thereby starving legitimate applications.

4. Container Isolation

This chapter treats another Linux feature that is cornerstone for container security: Linux namespaces.

Linux namespaces are a feature of the Linux kernel that partitions kernel resources such that one or more processes sees one set of resources while another set of processes sees a different set of resources. If cgroups control the resources that a process can use, namespaces control what it can see.

For each of the existing namespaces (Unix Timesharing System, Process IDs, Mount Points, Network, Users and Group Ids, Inter-Process Communications) the author shows how can be created from command line. For some namespaces a comparison is done between the isolation implemented by a container runtime and the isolation offered just using the tools offered out of the box by Linux.

5. Virtual Machines

This chapter is an introduction to virtual machines. It is explained different types of hypervisors (a.k.a VMM – Virtual Machine Monitor):

  • Type1 – the hypervisor is installed directly on top of the hardware with no operating system underneath (ex: Hyper-V, Xen)
  • Type2 – the hypervisor is installed on top of a Host Os (ex: VirtualBox, Parallels, QEMU)
  • Kernel Based Virtual Machines – this is a kind of hybrid type because it consists in a hypervisor running within the kernel of the hos Os (ex: Linux KVM).
Different types of hypervisors

After describing the types of hypervisors the author explained how the hypervisors are achieving the virtualization via a mechanism called “trap and emulate“. When an OS is running as a virtual machine in a hypervisor, some of its instructions may conflict with the host operation system. So the hypervisor will emulates the effect of that specific instruction or action without carrying it out. In this way, the host OS is not effected by the guest’s actions.

The chapter is concluded with the advantages of hypervisors for process isolation compared with the kernel processes (which are the cornerstone of containers) and the main drawbacks of hypervisors.

From the process isolation point of view the hypervisors are offering a greater isolation and the difference is that hypervisors have a simpler job to fulfill comparing with OS kernels. In a kernel, user space processes are allowed some visibility of each other, but there is no sharing of memory or sharing of processes in the case of hypervisors.

On the drawback side, the VMs have start-up times that are several orders of magnitude greater than a container, containers give developers a convenient ability to “build once, run anywhere” quickly and efficiently, each virtual machine has the overhead of running a whole kernel compared with containers that are sharing a kernel so containers can be very efficient in both resource use and performance.

6. Container Images

This chapter is focusing on the images; it starts by explaining the OCI standards covering the image specification. In this chapter you will be able to see how different topics from previous chapters (namespaces, capabilities, control groups, root file system) are fitting together so the end user can define, build and execute a container.

The second par of the chapter is focusing on different attack vectors on an image:

Image Attack Vectors

Some of this attack vectors are not really linked to container technology (tamper source code, vulnerable dependencies, attack deployment via build machine) but others are container specific attack vectors (tamper the docker file, usage of vulnerable base images, modify images during build).

7. Software Vulnerabilities in Images

The chapter is dedicated to vulnerabilities managements in general and also in the context of containers. For the general/generic part, the author explains what is the workflow when a vulnerability is discovered:

after the discovery the person the new issue will get a unique identifier that begins with “CVE” (Common Vulnerabilities and Exposures) , followed by the year and an unique id.

  • A responsible security disclosure is agreed between the entity that found the vulnerability and the entity that “owns” the software. Both parties agree on a timeframe after which the researcher can publish their findings.
  • The entity that “owns” the software is fixing the vulnerability and delivers a patch.
  • Once the vulnerability can be disclosed, it receive a unique identifier that begins with “CVE,” which stands for Common Vulnerabilities and Exposures.

Strangely enough, the author does not mention the usage of CVSS (Common Vulnerability Scoring System) score of a vulnerability. Usually CVSS score is used to judge the impact of the vulnerability.

The second part of the chapter is focusing on ways to handle the vulnerability management in the context of containers. A few interesting and valuable ideas:

  • (always) use immutable containers :
    • If containers are downloading code at runtime, different instances of the container could be running different versions of that code, but it would be difficult to know which instance is running what version.
    • It’s harder to control and ensure the provenance of the software running in each container if it could be downloaded at any time and from anywhere.
    • Building a container image and storing it in a registry is very simple to automate in a CI/CD pipeline.
  • regular scan of images.
    • Regularly re-scanning container images allows the scanning tool to check the contents against its most up-to-date knowledge about vulnerabilities. A very common approach is to re-scan all deployed images every 24 hours, in addition to scanning new images as they are built, as part of an automated CI/CD pipeline.
  • use a tool that can do more than scanning for vulnerabilities (if possible). A (non-exhaustive) list of extra features that the scanner could have:
    • Known malware within the image
    • Executables with the setuid bit
    • Images configured to run as root
    • Secret credentials such as tokens or passwords
    • Sensitive data in the form of credit card or Social Security numbers or something similar

8. Strengthening Container Isolation

This chapter is an extension of the Chapter 4 (Container Isolation); it presents other ways to extend the container isolation using mechanisms and framework beyond the Linux kernel features.

The first part of the chapter presents mechanisms already present in Linux ecosystem that can be used in other contexts than containers, namely:

  • Seccomp
    • Seccomp is a mechanism for restricting the set of system calls that an application is allowed to make.
    • The Docker default seccomp profile blocks more than 40 of the 300+ syscalls (including all the examples just listed) without ill effects on the vast majority of containerized applications. Unless you have a reason not to do so, it’s a good default profile to use.
  • AppArmor
    • In AppArmor, a profile can be associated with an executable file, determining what that file is allowed to do in terms of capabilities and file access permissions.
    • AppArmor implement mandatory access controls. A mandatory access control is set by a central administrator, and once set, other users do not have any ability to modify the control or pass it on to another user.
    • There is a default Docker AppArmor profile
  • SELinux
    • SElinux lets you constrain what a process is allowed to do in terms of its interactions with files and other processes. Each process runs under an SELinux domain and every file has a type.
    • Every file on the machine has to be labeled with its SELinux information before you can enforce policies. These policies can dictate what access a process of a particular domain has to files of a particular type.

In the second part of the chapter the author presents container specific technologies that could be used to enforce the containers isolation:

  • gVisor
    • gVisor provides a virtualized environment in order to sandbox containers. The system interfaces normally implemented by the host kernel are moved into a distinct, per-sandbox application kernel in order to minimize the risk of a container escape exploit.
    • To do this, a component of gVisor called the Sentry intercepts syscalls from the application. Sentry is heavily sandboxed using seccomp, such that it is unable to access filesystem resources itself. When it needs to make systemcalls related to file access, it off-loads them to an entirely separate process called the Gofer. Even those system calls that are unrelated to filesystem access are not passed through to the host kernel directly but instead are reimplemented within the Sentry. Essentially it’s a guest kernel, operating in user space.
  • Kata Containers
    • The idea with Kata Containers is to run containers within a separate virtual machine. This approach gives the ability to run applications from regular OCI format container images, with all the isolation of a virtual machine.
    • Kata uses a proxy between the container runtime and a separate target host where the application code runs. The runtime proxy creates a separate virtual machine using QEMU to run the container on its behalf.
  • Firecracker
    • Is a virtual machine offering the benefits of secure isolation through a hypervisor and no shared kernel, but with startup times around 100ms.
    • Firecracker designers have stripped out functionality that is generally included in a kernel but that isn’t required in a container like enumerating devices. The main saving comes from a minimal device model that strips out all but the essential devices.

9. Breaking Container Isolation

After explaining in previous chapters what can be done to enhance the container isolation, this chapter is focusing on how a container could be misconfigured so this isolation is broken.

The following misconfigurations are explained:

Run containers using the default (root) user.

Unless your container image specifies a non-root user or you specify a non default user when you run a container, by default the container will run as root.

The best option is to define a custom user inside the container but if this option is not available then a few other options are presented:

  • override the user id; this is possible in Docker using the –user flag of the docker run command.
  • use user namespaces (covered in chapter 2) within the container, so that root inside the container is not the same as root on the host. You can enable the use of user namespaces in Docker, but it’s not turned on by default. If you’re interested about how to do it please take a look to Isolate containers with a user namespace

The use of —priviledged flag

The usage of priviledged flag give extended (Linux) capabilities to the process representing the running container. Docker introduced the –privileged flag to enable DinD (Docker in Docker) which can be used by build tools(very often in the CI/CD context) running as containers, which need access to the Docker daemon in order to use Docker to build container images.

Mounting sensitive directories

Mounting inside the containers the root file system or specific host folders is not a very good idea. List of folders to avoid mounting:

  • Mounting /etc would permit modifying the host’s /etc/passwd file from within the container.
  • Mounting /bin, /usr/bin or /usr/sbin would allow the container to write executables into the host directory.
  • Mounting host log directories into a container could enable an attacker to modify or erase the logs.

Mounting the Docker Socket

In a Docker environment, there is a Docker daemon process that essentially does all the work. When you run the docker command-line utility, this sends instructions to the daemon over the Docker socket that lives at /var/run/docker.sock . Any entity that can write to that socket can also send instructions to the Docker daemon. The daemon runs as root and will happily build and run any software of your choosing on your behalf.

Accessing the Docker Daemon via REST API with no authentication

This in not really mentioned in the book (even that I think that it should) but it’s very similar with the previous paragraph. The docker daemon can be also accessed via a REST API; by default the API is accessible with no authentication.

Sharing namespaces between the container and the host

Containerized processes are all visible from the host; thus, sharing the process namespace to a container lets that container see the other containerized processes.

10. Container Network Security

The chapter starts with an introduction to ISO/OCI networking model and this model is used during the chapter to explain different topics related to network security. The author is focusing on explaining the networking model for containers running under Kubernetes orchestrator but even if you’re not interested on K8s it is still possible to find some technology agnostic best practices:

  • Default Deny Ingress: define a network policy that denies ingress traffic by default and then add policies to permit traffic only where you expect it
  • Default Deny Egress: Same as the Ingress part.
  • Restricts ports: Restrict traffic so that it is accepted only to specific ports for each application.

11. Securely Connecting Components with TLS

Most of the chapter content have noting to do with containers (this is highlighted even by the author itself) and is treating the history of SSL/TLS protocol and the basics of PKI : Public/Private Key, X509 certificates, Certificate Signing Requests, Certificate Revocation and Certificate Authorities.

The only piece of information linked to containers that I found important is the that rather than writing your own code to set up secure connections, you can choose to use a service mesh to do it for you.

12. Passing Secrets to Containers

The chapter starts by enumerating properties that a secret must have:

  • it should be stored in encrypted form so that it’s not accessible to every user or entity.
  • it should never be written to disk unencrypted (and even better just held it in memory and never write it on disk).
  • it should be revocable (make them invalid in the event that the secret should no longer be trusted).
  • it should be able to rotate it.
  • it should be independent of the lifecycle of the consumers.
  • only software components that need the access to it should be able to read the secret

Next paragraph enumerates different ways of injecting information (secrets included) into containers:

  • store the information into the image
    • obviously this is not a very good idea for secrets because can be accessed by anyone having the image and it cannot be changed unless the images are rebuild.
  • use environment variables as part of the configuration that goes along with the image
    • same problems as the hard-coded secrets
  • pass the secret over the network
    • the running container will make the appropriate network calls to retrieve or receive the information.
    • in this case the date(secret) in transit should be encrypted, most probably using a service mesh
    • the principal drawback of this approach is how the container will be able to authenticate to this service offering the secret; the author does not offer any solution
  • pass the secrets at runtime using the environment variables.
    • environment variables defined for the container can be seen using different commands like docker inspect.
  • pass the secrets through files.
    • This option consists in write the secrets into files that the container can access through a mounted volume.
    • Combining this with a secure secrets store ensures that secrets are never stored “at rest” unencrypted.

I found this chapter rather strange because it explains how to not pass secrets to containers instead of presenting the good practices. Speaking about good practices, this are very briefly mentioned like the usage of a third-party (commercial) solution for secret storage. I would have preferred to have more insights on how this tools are working.

13. Container Runtime Protection

This chapter treats the controls to put in place in order to assure the protection of the running containers.

The first idea is to compute a container profile. This profile should be computed prior to the deployment of the container in live and should contains the normal behavior of the container. Once this profile is known, then at runtime a (container security)tool would be able to compare the profile with the real behavior of the container and detect any discrepancy.

This container profile could contains the following information:

  • network traffic – the other containers and or hosts that the container normally communicates with.
  • executable – what kind of commands the normal cunning container is executing. In this case the author suggests to use eBPF (which stands for extended Berkeley Packet Filter) technology.
  • file access – what files from the container file system are usually accessed.
  • user IDs – as a general rule, if the container is doing one job, it probably needs to operate under only one user identity.
  • (Linux) capabilities – the (minimal) list of capabilities the container needs in order to execute properly; any attempt to use a capability not present in the list should raise a red flag.

The second idea presented is the drift prevention. It’s considered best practice to treat containers as immutable. The container is instantiated from its image, and then the contents of the container should not change. In the case of drift prevention the (container security) tool will be able to make the difference between the software that came from the image, and the software that is running in the workload so it gives the ability to immediately stop any software that doesn’t belong to the (original) image.

14. Containers and the OWASP Top 10

This sounds a very interesting topic but unfortunately the author it expedite it very fast. In some of the cases the author is even confessing that the type of risk is not linked to containers and could be applied to non containers world also.

Same OWASP Top 10 (2017) have direct applicability in the container word:

  • Broken Authentication.
    • This can be linked with the usage of secrets in container word. These secrets need to be stored with care and passed into containers at runtime, as discussed in Chapter 12.
    • The containerized applications that must communicate between them would need to identify each other using certificates, and communicate using secure connections. This can be handled directly by containers, or you can use a service mesh
  • Broken Access Control
    • Some container-specific approaches to mitigate least privilege the abuse of privileges that may be granted unnecessarily to users or components:
      • Don’t run containers as root.
      • Limit the capabilities granted to each container.
      • Use seccomp, AppArmor, or SELinux.
      • Use immutable containers
  • Insufficient Logging and Monitoring
    • Following container events should be logged:
      • Container start/stop events
      • Access to secrets
      • Any modification of privileges
      • Modification of container payload
      • Inbound and outbound network connections
      • Volume mounts
      • Failed actions such as attempts to open network connections, write to files, or change user permissions.
  • Failed actions such as attempts to open network connections, write
  • to files

How to fill-in programmatically ProForma forms into Jira Issues

The goal of this ticket is to present how to programmatically fill-in ProForma form fields that are part of a Jira issue.

Context

Jira is a very popular issue tracking product. The Jira functionalities can be extended via different plugins that can be found on Jira Marketplace. One of this plug-ins is ProForma Forms that is a plug-in for building forms and checklists directly into Jira.

Problem

Most of the Jira functionalities are also accessible programmatically via a REST APIs and ProForma also offers a REST API to programmatically manipulate forms. But taking a closer look to the ProForma API, there is no entry point for filling-in the fields of a form:

ProForma API for interacting with forms into an Jira Issue

Solution

The (logical) workflow of (programmatically) add a form to an issue is to :

  1. Create the issue (using Jira API)
  2. Attach the desired form/s to the issue (using the ProForma API entry point /rest/proforma/1/issue/{issueKey}/form )
  3. Fill in the form/s fields (no API to do this)
  4. Submit the form/s (using the ProForma API entry point /rest/proforma/1/issue/{issueKey}/form/{formId}/submit)

To fill in the fields programmatically it is possible to :

  • During the form design add a default answer so the form will be pre-fill with this default answer
  • During the form design link each of the form fields to Jira fields and fill in this Jira fields during the creation of the Jira issue:

For more information about how to link ProForma fields to Jira fields you can see also this: Linking ProForma Fields to Jira Fields

Containers landscape: seen through OCI and CNCF standards lens

Introduction

Today, the container landscape is rather crowded and Docker is not the predominant player anymore.

The goal of this ticket is to present different products and/or projects and/or vendors that are part of the containers landscape and classify them using the existing standards.

For this classification I will use the standards from Open Container Initiative (OCI) and Cloud Native Computing Foundation (CNCF).

Open Container Initiative

The goal of the Open Container Initiative (OCI) is to promote a set of common, minimal, open standards and specifications around container technology more precisely container formats and runtime . At the moment of this writing it offers the following standards:

Cloud Native Computing Foundation

The goal of Cloud Native Computing Foundation (CNCF) is to drive adoption of cloud native technologies (Containers, service meshes, microservices, immutable infrastructure) by fostering and sustaining an ecosystem of open source, vendor-neutral projects. In the specific case of containers we will focus on the Container Runtime Interface (CRI).

If you wonder if there is any link between OCI and CNCF, the answer is that both initiatives are operating under the Linux Foundation umbrella, the OCI focusing only on the container formats and runtime.

OCI Image Specification

The image specification defines the structure of an OCI Image which should contain a manifest, an image index (optional), a set of filesystem layers, and a configuration. The goal of this specification is to enable the creation of interoperable tools for building, transporting, and preparing a container image to run.

In order to see the content of an OCI image the following command could be used (for a nginx image in this example):

docker save nginx > nginx.tar
tar -xvf nginx.tar

The content of the tar file will look like:

71388...b14.json
c827b...b2014e92/
c827b...b2014e92/VERSION
c827b...b2014e92/json
c827b...b2014e92/layer.tar
manifest.json
repositories

On the tooling side here are a few tools that are able to generate OCI compliant images and this list is far from being exhaustive:

  • Kaniko – tool to build container images from a Dockerfile, inside a container or Kubernetes cluster. Kaniko doesn’t depend on a Docker daemon and executes each command within a Dockerfile completely in userspace. This enables building container images in environments that can’t easily or securely run a Docker daemon, such as a standard Kubernetes cluster. The tool was created by Google.
  • Jib – tool to build Docker and OCI images for your Java applications without a Docker daemon. It is available as plugins for Maven and Gradle and as a Java library. The tool was created by Google.
  • Buildah – tool to build OCI images from a Dockerfile that is daemonless and rootless. It is also capable to generate a pod file from one or more images and also mimic the execution of a pod.
Tools to build OCI compliant images

OCI Runtime Specification

The runtime specification goal is to specify the configuration, execution environment, and lifecycle of a container. A container’s configuration is specified as the config.json for the supported platforms and details the fields that enable the creation of a container.

The execution environment is specified to ensure that applications running inside a container have a consistent environment between runtimes along with common actions defined for the container’s lifecycle.

From the implementation point of view we can distinguish three types of runtimes.

Native runtimes are using the host kernel to run the containers. The isolation of containers which are sharing the same kernel can be improved by using “out of the box” security mechanisms like seccomp, AppArmor or SELinux.

The most used native container runtimes projects today are (a few other existed in the past but now are deprecated: railcar, rkt):

  • runC – the de-facto standard container runtime maintained by Docker under Apache 2 license
  • cRun – created and maintained by RedHat is part of the Podman/Buildah family tools.

Sandboxed runtimes instead of sharing the host kernel, the containerized process runs on a kernel proxy layer, which then interacts with the host kernel on the container’s behalf. Because of this increased isolation, these runtimes have a reduced attack surface and make it less likely that a containerized process can “escape” from the original container.

  • gVisor
    • gVisor provides a virtualized environment in order to sandbox containers. The system interfaces normally implemented by the host kernel are moved into a distinct, per-sandbox application kernel in order to minimize the risk of a container escape exploit.
    • To do this, a component of gVisor called the Sentry intercepts syscalls from the application. Sentry is heavily sandboxed using seccomp, such that it is unable to access filesystem resources itself. When it needs to make systemcalls related to file access, it off-loads them to an entirely separate process called the Gofer. Even those system calls that are unrelated to filesystem access are not passed through to the host kernel directly but instead are reimplemented within the Sentry. Essentially it’s a guest kernel, operating in user space.
  • Nabla
    • A containerized application can avoid making a Linux system call if it links to a library OS component that implements the system call functionality. Nabla containers use library OS (aka unikernel) techniques, specifically those from the Solo5 project, to avoid system calls and thereby reduce the attack surface. Nabla containers only use 7 system calls; all others are blocked via a Linux seccomp policy.

Virtualized runtimes takes a different approach to achieve container isolation; the goal of virtualized runtimes is to create a lightweight virtual machines on which the host kernel and application will run.

  • Kata Containers
    • The idea with Kata Containers is to run containers within a separate virtual machine. This approach gives the ability to run applications from regular OCI format container images, with all the isolation of a virtual machine.
    • Kata uses a proxy between the container runtime and a separate target host where the application code runs. The runtime proxy creates a separate virtual machine using QEMU to run the container on its behalf.
  • Firecracker
    • Is a virtual machine offering the benefits of secure isolation through a hypervisor and no shared kernel, but with startup times around 100ms.
    • Firecracker designers have stripped out functionality that is generally included in a kernel but that isn’t required in a container like enumerating devices. The main saving comes from a minimal device model that strips out all but the essential devices.
Tools to run OCI images

OCI Distribution Specification

The Open Container Initiative Distribution Specification (a.k.a. “OCI Distribution Spec”) defines an API protocol to facilitate and standardize the distribution of content. The specification is designed to be agnostic of content types, OCI Image types being currently the most prominent.

The specification is strongly inspired from the Docker Registry HTTP API V2.

The Github repository notes four potential use cases:

  • Artifact Verification: Help enable trust between registries.
  • Resumable Push: Improve communication to help reduce re-uploading for transfers interrupted midway.
  • Resumable Pull: Similarly, it could help reduce unneeded downloads on the incoming side.
  • Layer Upload Deduplication: The spec could help container registries avoid duplicating layers.

On the tooling side, most probably any tool/vendor that offers support for Docker Registry Api will also support the new specification.

CNCF Container Runtime Interface (CRI)

In the first versions of Kubernetes (prior to 1.5) Docker was used as (default and only) container runtime engine; the usage of Docker was hardcoded into the Kubelet (k8s component that is installed and running on each cluster node having as goal to ensure that the containers described by Pod specifications are running and healthy).

The goal of CRI was to define an API that any container runtime should implement in order to be used by Kubelet.

The most important tools implementing CRI are :

  • containerd is Docker high-level container runtime, able to push and pull images, manage storage and define network capabilities. It is also capable of managing the lifecycle of running containers by passing corresponding commands to a low-level container runtime like runc.
  • CRI-O is the RedHat implementation of CRI and is the default container runtime for OpenShift since version 4. CRI-O has less features compared to containerd and delegates to components from “Container Tools” project for image management and storage. Most probably as a low level runtime container it’s using cRun.
Tools implementing CRI

Most probably my previous list is not exhaustive and other projects are implementing the CRI specification; for example PouchContainer which is container engine open sourced by Alibaba. If you are interested in knowing how the CRI implementation is done you could read this: Design and Implementation of PouchContainer CRI.

Book Review: API Security In Action

This is the review of the API Security in action book.

(My) Conclusion

This book is doing a very good job in covering different mechanisms that could be used in order to build secure (RESTful) APIs. For each security control the author explains what kind of attacks the respective control is able to mitigate.

The reader should be comfortable with Java and Maven because most of the code examples of the book (and there are a lot) are implemented in Java.

The diagram of all the security mechanism presented:

Part 1: Foundations

The goal of the first part is to learn the basics of securing an API. The author starts by explaining what is an API from the user and from developer point of view and what are the security properties that any software component (APIs included) should fill in:

  • Confidentiality – Ensuring information can only be read by its intended audience
  • Integrity – Preventing unauthorized creation, modification, or destruction of information
  • Availability – Ensuring that the legitimate users of an API can access it when they need to and are not prevented from doing so.

Even if this security properties looks very theoretical the author is explaining how applying specific security controls would fulfill the previously specified security properties. The following security controls are proposed:

  • Encryption of data in transit and at rest – Encryption prevents data being read or modified in transit or at rest
  • Authentication – Authentication is the process of verifying whether a user is who they say they are.
  • Authorization/Access Control – Authorization controls who has access to what and what actions they are allowed to perform
  • Audit logging – An audit log is a record of every operation performed using an API. The purpose of an audit log is to ensure accountability
  • Rate limiting – Preserves the availability in the face of malicious or accidental DoS attacks.

This different controls should be added into a specific order as shown in the following figure:

Different security controls that could/should be applied for any API

To illustrate each control implementation, an example API called Natter API is used. The Natter API is written in Java 11 using the Spark Java framework. To make the examples as clear as possible to non-Java developers, they are written in a simple style, avoiding too many Java-specific idioms. Maven is used to build the code examples, and an H2 in-memory database is used for data storage.

The same API is also used to present different types of vulnerabilities (SQL Injection, XSS) and also the mitigations.

Part 2: Token-based Authentication

This part presents different techniques and approaches for the token-based authentication.

Session cookie authentication

The first authentication technique presented is the “classical” HTTP Basic Authentication. HTTP Basic Authentication have a few drawbacks like there is no obvious way for the user to ask the browser to forget the password, the dialog box presented by browsers for HTTP Basic authentication cannot be customized.

But the most important drawback is that the user’s password is sent on every API call, increasing the chance of it accidentally being exposed by a bug in one of those operations. This is not very practical that’s why a better approach for the user is to login once then be trusted for a specific period of time. This is basically the definition of the Token-Based authentication:

Token Based authentication

The first presented example of Token-Based authentication is using the HTTP Base Authentication for the dedicated login endpoint (step number 1 from the previous figure) and the session cookies for moving the generated token between the client and the API server.

The author take the opportunity to explain how session cookies are working and what are the different attributes but especially he presents the attacks that are possible in the case of using session cookies. The session fixation attack and the Cross-Site Request Forgery attack (CSRF) are presented in details with different options to avoid or mitigate those attacks.

Tokens whiteout cookies

The usage of session cookies is tightly linked to a specific domain and/or sub-domains. In case you want to make requests cross domains then the CORS (Cross-Origin Resource Sharing) mechanism can be used. The last part of the chapter treating the usage of session cookies contains detailed explanations of CORS mechanism.

Using the session cookies as a mechanism to store the authentication tokens have a few drawbacks like the difficulty to share cookies between different distinguished domains or the usage of API clients that do not understand the web standards (mobile clients, IOT clients).

Another option that is presented are the tokens without cookies. On the client side the tokens are stored using the WebStorage API. On the server side the tokens are stored into a “classical” relational data base. For the authentication scheme the Bearer authentication is used (despite the fact that the Bearer authentication scheme was created in the context of OAuth 2.0 Authorization framework is rather popular in other contexts also).

In case of this solution the least secure component is the storage of the authentication token into the DB. In order to mitigate the risk of the tokens being leaked different hardening solutions are proposed:

  • store into the DB the hash of tokens
  • store into the DB the HMAC of the tokens and the (API) client will then send the bearer token and the HMAC of the token

This authentication scheme is not vulnerable to session fixation attacks or CSRF attacks (which was the case of the previous scheme) but an XSS vulnerability on the client side that is using the WebStorage API would defeat any kind of mitigation control put in place.

Self-contained tokens and JWTs

The last chapter of this this (second) part of the book treats the self-contained or stateless tokens. Rather than store the token state in the database as it was done in previous cases, you can instead encode that state directly into the token ID and send it to the client.

The most client-side tokens used are the Json Web Token/s (JWT). The main features of a JWT token are:

  • A standard header format that contains metadata about the JWT, such as which MAC or encryption algorithm was used.
  • A set of standard claims that can be used in the JSON content of the JWT, with defined meanings, such as exp to indicate the expiry time and sub for the subject.
  • A wide range of algorithms for authentication and encryption, as well as digital signatures and public key encryption.

A JWT token can have three parts:

  • Header – indicates the algorithm of how the JWT was produced, the key used to authenticate the JWT to or an ID of the key used to authenticate. Some of the header values:
    • alg: Identifies which algorithm is used to generate the signature
    • kid: Key Id; as the key ID is just a string identifier, it can be safely looked up in server-side set of keys.
    • jwk: The full key. This is not a safe header to use; Trusting the sender to give you the key to verify a message loses all security properties.
    • jku: An URL to retrieve the full key. This is not a safe header to use. The intention of this header is that the recipient can retrieve the key from a HTTPS endpoint, rather than including it directly in the message, to save space.
  • Payload/Claims – pieces of information asserted about a subject. The list of standard claims:
    • iss (issuer): Issuer of the JWT
    • sub (subject): Subject of the JWT (the user)
    • aud (audience): Recipient for which the JWT is intended
    • exp (expiration time): Time after which the JWT expires
    • nbf (not before time): Time before which the JWT must not be accepted for processing
    • iat (issued at time): Time at which the JWT was issued; can be used to determine age of the JWT
    • jti (JWT ID): Unique identifier; can be used to prevent the JWT from being replayed (allows a token to be used only once)
  • Signature – Securely validates the token. The signature is calculated by encoding the header and payload using Base64url Encoding and concatenating the two together with a period separator. That string is then run through the cryptographic algorithm specified in the header.
Example of JWT token

Even if the JWT could be used as self-contained token by adding the algorithm and the signing key into the header, this is a very bad idea from the security point of view because you should never trust a token sign by an external entity. A better solution is to store the algorithm as metadata associated with a key on the server.

Storing the algorithm and the signing key on the server side it also helps to implement a way to revoke tokens. For example changing the signing key it can revoke all the tokens using the specified key. Another way to revoke tokens more selectively would be to add to the DB some token metadata like token creation date and use this metadata as revocation criteria.

Part 3: Authorization

OAuth2 and OpenID Connect

A way to implement authorization using JWT tokens is by using scoped tokens. Typically, the scope of a token is represented as one or more string labels stored as an attribute of the token. Because there may be more than one scope label associated with a token, they are often referred to as scopes. The scopes (labels) of a token collectively define the scope of access it grants.

A scoped token limits the operations that can be performed with that token. The set of operations that are allowed is known as the scope of the token. The scope of a token is specified by one or more scope labels, which are often referred to collectively as scopes.

Scopes allow a user to delegate part of their authority to a third-party app, restricting how much access they grant using scopes. This type of control is called discretionary access control (DAC) because users can delegate some of their permissions to other users.

Another type of control is the mandatory access control (MAC), in this case the user permissions are set and enforced by a central authority and cannot be granted by users themselves.

OAuth2 is a standard to implement the DAC. OAuth uses the following specific terms:

  • The authorization server (AS) authenticates the user and issues tokens to clients.
  • The user also known as the resource owner (RO), because it’s typically their resources that the third-party app is trying to access.
  • The third-party app or service is known as the client.
  • The API that hosts the user’s resources is known as the resource server (RS).

To access an API using OAuth2, an app must first obtain an access token from the Authorization Server (AS). The app tells the AS what scope of access it requires. The AS verifies that the user consents to this access and issues an access token to the app. The app can then use the access token to access the API on the user’s behalf.

One of the advantages of OAuth2 is the ability to centralize authentication of users at the AS, providing a single sign-on (SSO) experience. When the user’s client needs to access an API, it redirects the user to the AS authorization endpoint to get an access token. At this point the AS authenticates the user and asks for consent for the client to be allowed access.

OAuth can provide basic SSO functionality, but the primary focus is on delegated third-party access to APIs rather than user identity or session management. The OpenID Connect (OIDC) suite of standards extend OAuth2 with several features:

  • A standard way to retrieve identity information about a user, such as their name, email address, postal address, and telephone number.
  • A way for the client to request that the user is authenticated even if they have an existing session, and to ask for them to be authenticated in a particular way, such as with two-factor authentication.
  • Extensions for session management and logout, allowing clients to be notified when a user logs out of their session at the AS, enabling the user to log out of all clients at once.

Identity-based access control

In this chapter the author introduces the notion of users, groups, RBAC (Role-Based Access Control) and ABAC (Access-Based Access Control). For each type of access control the author propose an ad-hoc implementation (no specific framework is used) for the Natter API (which is the API used all over the book to present different security controls.)

Capability-based security and macaroons

A capability is an unforgeable reference to an object or resource together with a set of permissions to access that resource. Compared with the more dominant identity-based access control techniques like RBAC and ABAC capabilities have several differences:

  • Access to resources is via unforgeable references to those objects that also grant authority to access that resource. In an identity-based system, anybody can attempt to access a resource, but they might be denied access depending on who they are. In a capability-based system, it is impossible to send a request to a resource if you do not have a capability to access it.
  • Capabilities provide fine-grained access to individual resources.
  • The ability to easily share capabilities can make it harder to determine who has access to which resources via your API.
  • Some capability-based systems do not support revoking capabilities after they have been granted. When revocation is supported, revoking a widely shared capability may deny access to more people than was intended.

The way to use capability-based security in the context of a REST API is via capabilities URIs. A capability URI (or capability URL) is a URI that both identifies a resource and conveys a set of permissions to access that resource. Typically, a capability URI encodes an unguessable token into some part of the URI structure. To create a capability URI, you can combine a normal URI with a security token.

The author adds the capability URI to the Netter API and implements this with the token encoded
into the query parameter because this is simple to implement. To mitigate any threat from tokens leaking in log files, a short-lived tokens are used.

But putting the token representing the capability in the URI path or query parameters is less than ideal because these can leak in audit logs, Referer headers, and through the browser history. These risks are limited when capability URIs are used in an API but can be a real problem when these URIs are directly exposed to users in a web browser client.

One approach to this problem is to put the token in a part of the URI that is not usually sent to the server or included in Referer headers.

The capacities URIs can be also be mixed with identity for handling authentication and authorization.There are a few ways to communicate identity in a capability-based system:

  • Associate a username and other identity claims with each capability token. The permissions in the token are still what grants access, but the token additionally authenticates identity claims about the user that can be used for audit logging or additional access checks. The major downside of this approach is that sharing a capability URI lets the recipient impersonate you whenever they make calls to the API using that capability.
  • Use a traditional authentication mechanism, such as a session cookie, to identify the user in addition to requiring a capability token. The cookie would no longer be used to authorize API calls but would instead be used to identify the user for audit logging or for additional checks. Because the cookie is no longer used for access control, it is less sensitive and so can be a long-lived persistent cookie, reducing the need for the user to frequently log in

The last part of the chapter is about macaroons which is a technology invented by Google (https://research.google/pubs/pub41892/). The macaroons are extending the capabilities based security by adding more granularity.

A macaroon is a type of cryptographic token that can be used to represent capabilities and other authorization grants. Anybody can append new caveats to a macaroon that restrict how it can be used

For example is possible to add new capabilities that allows only read access to a message created after a specific date. This new added extensions are called caveats.

Part 4: Microservice APIs in Kubernetes

Microservice APIs in K8S

This chapter is an introduction to Kubernetes orchestrator. The introduction is very basic but if you are interested in something more complete then Kubernetes in Action, Second Edition is the best option. The author also is deploying on K8S a (H2) database, the Natter API (used as demo through the entire book) and a new API called Linked-Preview service; as K8S “cluster” the Minikube is used.

Having an application with multiple components is helping him to show how to secure communication between these components and how to secure incoming (outside) requests. The presented solution for securing the communication is based on the service mesh idea and K8s network policies.

A service mesh works by installing lightweight proxies as sidecar containers into every pod in your network. These proxies intercept all network requests coming into the pod (acting as a reverse proxy) and all requests going out of the pod.

Securing service-to-service APIs

The goal of this chapter is to apply the authentication and authorization techniques already presented in previous chapters but in the context of service-to-service APIs. For the authentication the API’s keys, the JWT are presented. To complement the authentication scheme, the mutual TLS authentication is also used.

For the authorization the OAuth2 is presented. A more flexible alternative is to create and use service accounts which act like regular user accounts but are intended for use by services. Service accounts should be protected with strong authentication mechanisms because they often have elevated privileges compared to normal accounts.

The last part of the chapter is about managing service credentials in the context of K8s. Kubernetes includes a simple method for distributing credentials to services, but it is not very secure (the secrets are Base64 encoded and can be leaked by cluster administrator).

Secret vaults and key management services provide better security but need an initial credential to access. Using secret vaults have the following benefits:

  • The storage of the secrets is encrypted by default, providing better protection of secret data at rest.
  • The secret management service can automatically generate and update secrets regularly (secret rotation).
  • Fine-grained access controls can be applied, ensuring that services only have access to the credentials they need.
  • The access to secrets can be logged, leaving an audit trail.

Part 5: APIs for the Internet of Things

Securing IoT communications

This chapter is treating how different IoT devices could communicate securely with an API running on a classical system. The IoT devices, compared with classical computer systems have a few constraints:

  • An IOT device has significantly reduced CPU power, memory, connectivity, or energy availability compared to a server or traditional API client machine.
  • For efficiency, devices often use compact binary formats and low-level networking based on UDP rather than high-level TCP-based protocols such as HTTP and TLS.
  • Some commonly used cryptographic algorithms are difficult to implement securely or efficiently on devices due to hardware constraints or threats from physical attackers.

In order to cope with this constraints new protocols have been created based on the existing protocols and standards:

  • Datagram Transport Layer Security (DTLS). DTLS is a version of TLS designed to work with connectionless UDP-based protocols rather than TCP based ones. It provides the same protections as TLS, except that packets may be reordered or replayed without detection.
  • JOSE (JSON Object Signing and Encryption) standards. For IoT applications, JSON is often replaced by more efficient binary encodings that make better use of constrained memory and network bandwidth and that have compact software implementations.
  • COSE (CBOR Object Signing and Encryption) provides encryption and digital signature capabilities for CBOR and is loosely based on JOSE.

In the case when the devices needs to use public key cryptography then the key distribution became a complex problem. This problem could be solved by generating random keys during manufacturing of the IOT device (device-specific keys will be derived from a master key and some device-specific information) or through the use of key distribution servers.

Securing IoT APIs

The last chapter of the book is focusing on how to secure access to APIs in Internet of Things (IoT) environments meaning APIs provided by the devices or cloud APIs which are consumed by devices itself.

For the authentication part, the IoT devices could be identified using credentials associated with a device profile. These credentials could be an encrypted pre-shared key or a certificate containing a public key for the device.

For the authorization part, the IoT devices could use the OAuth2 for IoTwhich is a new specification that adapts the OAuth2 specification for constrained environments .