131 lines
6.9 KiB
Markdown
131 lines
6.9 KiB
Markdown
# Clair
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[![Build Status](https://api.travis-ci.org/coreos/clair.svg?branch=master "Build Status")](https://travis-ci.org/coreos/clair)
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[![Docker Repository on Quay](https://quay.io/repository/coreos/clair/status "Docker Repository on Quay")](https://quay.io/repository/coreos/clair)
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[![Go Report Card](https://goreportcard.com/badge/coreos/clair "Go Report Card")](https://goreportcard.com/report/coreos/clair)
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[![GoDoc](https://godoc.org/github.com/coreos/clair?status.svg "GoDoc")](https://godoc.org/github.com/coreos/clair)
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[![IRC Channel](https://img.shields.io/badge/freenode-%23clair-blue.svg "IRC Channel")](http://webchat.freenode.net/?channels=clair)
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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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New data sources can be [added programmatically] at compile-time or data can be injected via HTTP API at runtime.
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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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[added programmatically]: #custom-data-sources
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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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### Requirements
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An instance of [PostgreSQL] 9.4+ is required.
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All instructions assume the user has already setup this instance.
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During the first run, Clair will bootstrap its database with vulnerability data from its data sources.
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This can take several minutes.
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[PostgreSQL]: http://postgresql.org
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### Docker
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The easiest way to get an instance of Clair running is to simply pull down the latest copy from Quay.
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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/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 quay.io/coreos/clair -p 6060-6061:6060-6061 -v $HOME/clair_config:/config -config=config.yaml
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```
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### Source
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To build Clair, you need to latest stable version of [Go] and a working [Go environment].
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[Go]: https://github.com/golang/go/releases
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[Go environment]: https://golang.org/doc/code.html
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```sh
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$ 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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## Architecture
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### At a glance
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![Simple Clair Diagram](img/simple_diagram.png)
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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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### 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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Features are 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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By indexing the features of an image into the database, images only need to be rescanned when new features 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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### 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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### Custom Data Sources
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In addition to the default data sources, Clair has been designed in a way that allows extension without forking the project.
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*Fetchers*, which are Go packages that implement the fetching of upstream vulnerability data, are registered in [init()] similar to drivers for Go's standard [database/sql] package.
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A fetcher can live in its own repository and custom versions of clair can contain a small patch that adds the import statements of the desired fetchers in `main.go`.
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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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## 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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