190 lines
10 KiB
Markdown
190 lines
10 KiB
Markdown
# Clair
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[](https://travis-ci.org/coreos/clair)
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[](https://quay.io/repository/coreos/clair)
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[](https://goreportcard.com/report/coreos/clair)
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[](https://godoc.org/github.com/coreos/clair)
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[](http://webchat.freenode.net/?channels=clair)
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**Note**: The `master` branch may be in an *unstable or even broken state* during development.
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Please use [releases] instead of the `master` branch in order to get stable binaries.
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Clair is an open source project for the static analysis of vulnerabilities in [appc] and [docker] containers.
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Vulnerability data is continuously imported from a known set of sources and correlated with the indexed contents of container images in order to produce lists of vulnerabilities that threaten a container.
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When vulnerability data changes upstream, the previous state and new state of the vulnerability along with the images they affect can be sent via webhook to a configured endpoint.
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All major components can be [customized programmatically] at compile-time without forking the project.
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Our goal is to enable a more transparent view of the security of container-based infrastructure.
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Thus, the project was named `Clair` after the French term which translates to *clear*, *bright*, *transparent*.
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[appc]: https://github.com/appc/spec
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[docker]: https://github.com/docker/docker/blob/master/image/spec/v1.md
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[customized programmatically]: #customization
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[releases]: https://github.com/coreos/clair/releases
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## Common Use Cases
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### Manual Auditing
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You're building an application and want to depend on a third-party container image that you found by searching the internet.
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To make sure that you do not knowingly introduce a new vulnerability into your production service, you decide to scan the container for vulnerabilities.
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You `docker pull` the container to your development machine and start an instance of Clair.
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Once it finishes updating, you use the [local image analysis tool] to analyze the container.
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You realize this container is vulnerable to many critical CVEs, so you decide to use another one.
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[local image analysis tool]: https://github.com/coreos/clair/tree/master/contrib/analyze-local-images
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### Container Registry Integration
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Your company has a continuous-integration pipeline and you want to stop deployments if they introduce a dangerous vulnerability.
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A developer merges some code into the master branch of your codebase.
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The first step of your continuous-integration pipeline automates the testing and building of your container and pushes a new container to your container registry.
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Your container registry notifies Clair which causes the download and indexing of the images for the new container.
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Clair detects some vulnerabilities and sends a webhook to your continuous deployment tool to prevent this vulnerable build from seeing the light of day.
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## Hello Heartbleed
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During the first run, Clair will bootstrap its database with vulnerability data from its data sources.
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It can take several minutes before the database has been fully populated.
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**NOTE:** These setups are not meant for production workloads, but as a quick way to get started.
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### Kubernetes
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An easy way to run Clair is with Kubernetes 1.2+.
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If you are using the [CoreOS Kubernetes single-node instructions][single-node] for Vagrant you will be able to access the Clair's API at http://172.17.4.99:30060/ after following these instructions.
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```
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git clone https://github.com/coreos/clair
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cd clair/contrib/k8s
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kubectl create secret generic clairsecret --from-file=./config.yaml
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kubectl create -f clair-kubernetes.yaml
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```
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[single-node]: https://coreos.com/kubernetes/docs/latest/kubernetes-on-vagrant-single.html
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### Docker Compose
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Another easy way to get an instance of Clair running is to use Docker Compose to run everything locally.
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This runs a PostgreSQL database insecurely and locally in a container.
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This method should only be used for testing.
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```sh
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$ curl -L https://raw.githubusercontent.com/coreos/clair/master/docker-compose.yml -o $HOME/docker-compose.yml
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$ mkdir $HOME/clair_config
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$ curl -L https://raw.githubusercontent.com/coreos/clair/master/config.example.yaml -o $HOME/clair_config/config.yaml
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$ $EDITOR $HOME/clair_config/config.yaml # Edit database source to be postgresql://postgres:password@postgres:5432?sslmode=disable
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$ docker-compose -f $HOME/docker-compose.yml up -d
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```
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Docker Compose may start Clair before Postgres which will raise an error.
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If this error is raised, manually execute `docker start clair_clair`.
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### Docker
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This method assumes you already have a [PostgreSQL 9.4+] database running.
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This is the recommended method for production deployments.
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[PostgreSQL 9.4+]: http://postgresql.org
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```sh
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$ mkdir $HOME/clair_config
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$ curl -L https://raw.githubusercontent.com/coreos/clair/master/config.example.yaml -o $HOME/clair_config/config.yaml
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$ $EDITOR $HOME/clair_config/config.yaml # Add the URI for your postgres database
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$ docker run -d -p 6060-6061:6060-6061 -v $HOME/clair_config:/config quay.io/coreos/clair -config=/config/config.yaml
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```
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### Source
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To build Clair, you need to latest stable version of [Go] and a working [Go environment].
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In addition, Clair requires that [bzr], [rpm], and [xz] be available on the system [$PATH].
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[Go]: https://github.com/golang/go/releases
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[Go environment]: https://golang.org/doc/code.html
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[bzr]: http://bazaar.canonical.com/en
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[rpm]: http://www.rpm.org
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[xz]: http://tukaani.org/xz
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[$PATH]: https://en.wikipedia.org/wiki/PATH_(variable)
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```sh
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$ go get github.com/coreos/clair
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$ go install github.com/coreos/clair/cmd/clair
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$ $EDITOR config.yaml # Add the URI for your postgres database
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$ ./$GOBIN/clair -config=config.yaml
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```
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## Documentation
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Documentation can be found in a `README.md` file located in the directory of the component.
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- [Notifier](https://github.com/coreos/clair/blob/master/notifier/README.md)
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- [v1 API](https://github.com/coreos/clair/blob/master/api/v1/README.md)
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### Architecture at a Glance
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### Terminology
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- *Image* - a tarball of the contents of a container
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- *Layer* - an *appc* or *Docker* image that may or maybe not be dependent on another image
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- *Detector* - a Go package that identifies the content, *namespaces* and *features* from a *layer*
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- *Namespace* - a context around *features* and *vulnerabilities* (e.g. an operating system)
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- *Feature* - anything that when present could be an indication of a *vulnerability* (e.g. the presence of a file or an installed software package)
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- *Fetcher* - a Go package that tracks an upstream vulnerability database and imports them into Clair
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### Vulnerability Analysis
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There are two major ways to perform analysis of programs: [Static Analysis] and [Dynamic Analysis].
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Clair has been designed to perform *static analysis*; containers never need to be executed.
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Rather, the filesystem of the container image is inspected and *features* are indexed into a database.
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By indexing the features of an image into the database, images only need to be rescanned when new *detectors* are added.
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[Static Analysis]: https://en.wikipedia.org/wiki/Static_program_analysis
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[Dynamic Analysis]: https://en.wikipedia.org/wiki/Dynamic_program_analysis
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### Default Data Sources
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| Data Source | Versions | Format |
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|-------------------------------|--------------------------------------------------------|--------|
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| [Debian Security Bug Tracker] | 6, 7, 8, unstable | [dpkg] |
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| [Ubuntu CVE Tracker] | 12.04, 12.10, 13.04, 14.04, 14.10, 15.04, 15.10, 16.04 | [dpkg] |
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| [Red Hat Security Data] | 5, 6, 7 | [rpm] |
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[Debian Security Bug Tracker]: https://security-tracker.debian.org/tracker
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[Ubuntu CVE Tracker]: https://launchpad.net/ubuntu-cve-tracker
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[Red Hat Security Data]: https://www.redhat.com/security/data/metrics
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[dpkg]: https://en.wikipedia.org/wiki/dpkg
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[rpm]: http://www.rpm.org
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### Customization
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The major components of Clair are all programmatically extensible in the same way Go's standard [database/sql] package is extensible.
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Custom behavior can be accomplished by creating a package that contains a type that implements an interface declared in Clair and registering that interface in [init()]. To expose the new behavior, unqualified imports to the package must be added in your [main.go], which should then start Clair using `Boot(*config.Config)`.
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The following interfaces can have custom implementations registered via [init()] at compile time:
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- `Datastore` - the backing storage
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- `Notifier` - the means by which endpoints are notified of vulnerability changes
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- `Fetcher` - the sources of vulnerability data that is automatically imported
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- `MetadataFetcher` - the sources of vulnerability metadata that is automatically added to known vulnerabilities
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- `DataDetector` - the means by which contents of an image are detected
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- `FeatureDetector` - the means by which features are identified from a layer
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- `NamespaceDetector` - the means by which a namespace is identified from a layer
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[init()]: https://golang.org/doc/effective_go.html#init
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[database/sql]: https://godoc.org/database/sql
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[main.go]: https://github.com/coreos/clair/blob/master/cmd/clair/main.go
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## Related Links
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- [Talk](https://www.youtube.com/watch?v=PA3oBAgjnkU) and [Slides](https://docs.google.com/presentation/d/1toUKgqLyy1b-pZlDgxONLduiLmt2yaLR0GliBB7b3L0/pub?start=false&loop=false&slide=id.p) @ ContainerDays NYC 2015
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- [Quay](https://quay.io): the first container registry to integrate with Clair
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- [Dockyard](https://github.com/containerops/dockyard): an open source container registry with Clair integration
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