By default, all gates are opened. An easy way to restrict access to the web application is to do it at the network level, or by using SSH tunnels.

It is however possible to switch on authentication by either using one of the supplied backends or creating your own.

Be sure to checkout Experimental Rest API for securing the API.


Airflow uses the config parser of Python. This config parser interpolates ‘%’-signs. Make sure escape any % signs in your config file (but not environment variables) as %%, otherwise Airflow might leak these passwords on a config parser exception to a log.

Web Authentication


One of the simplest mechanisms for authentication is requiring users to specify a password before logging in. Password authentication requires the used of the password subpackage in your requirements file. Password hashing uses bcrypt before storing passwords.

authenticate = True
auth_backend = airflow.contrib.auth.backends.password_auth

When password auth is enabled, an initial user credential will need to be created before anyone can login. An initial user was not created in the migrations for this authentication backend to prevent default Airflow installations from attack. Creating a new user has to be done via a Python REPL on the same machine Airflow is installed.

# navigate to the airflow installation directory
$ cd ~/airflow
$ python
Python 2.7.9 (default, Feb 10 2015, 03:28:08)
Type "help", "copyright", "credits" or "license" for more information.
>>> import airflow
>>> from airflow import models, settings
>>> from airflow.contrib.auth.backends.password_auth import PasswordUser
>>> user = PasswordUser(models.User())
>>> user.username = 'new_user_name'
>>> = ''
>>> user.password = 'set_the_password'
>>> session = settings.Session()
>>> session.add(user)
>>> session.commit()
>>> session.close()
>>> exit()


To turn on LDAP authentication configure your airflow.cfg as follows. Please note that the example uses an encrypted connection to the ldap server as we do not want passwords be readable on the network level.

Additionally, if you are using Active Directory, and are not explicitly specifying an OU that your users are in, you will need to change search_scope to “SUBTREE”.

Valid search_scope options can be found in the ldap3 Documentation

authenticate = True
auth_backend = airflow.contrib.auth.backends.ldap_auth

# set a connection without encryption: uri = ldap://<your.ldap.server>:<port>
uri = ldaps://<your.ldap.server>:<port>
user_filter = objectClass=*
# in case of Active Directory you would use: user_name_attr = sAMAccountName
user_name_attr = uid
# group_member_attr should be set accordingly with *_filter
# eg :
#     group_member_attr = groupMembership
#     superuser_filter = groupMembership=CN=airflow-super-users...
group_member_attr = memberOf
superuser_filter = memberOf=CN=airflow-super-users,OU=Groups,OU=RWC,OU=US,OU=NORAM,DC=example,DC=com
data_profiler_filter = memberOf=CN=airflow-data-profilers,OU=Groups,OU=RWC,OU=US,OU=NORAM,DC=example,DC=com
bind_user = cn=Manager,dc=example,dc=com
bind_password = insecure
basedn = dc=example,dc=com
cacert = /etc/ca/ldap_ca.crt
# Set search_scope to one of them:  BASE, LEVEL , SUBTREE
# Set search_scope to SUBTREE if using Active Directory, and not specifying an Organizational Unit
search_scope = LEVEL

The superuser_filter and data_profiler_filter are optional. If defined, these configurations allow you to specify LDAP groups that users must belong to in order to have superuser (admin) and data-profiler permissions. If undefined, all users will be superusers and data profilers.

Roll your own

Airflow uses flask_login and exposes a set of hooks in the airflow.default_login module. You can alter the content and make it part of the PYTHONPATH and configure it as a backend in airflow.cfg.

authenticate = True
auth_backend = mypackage.auth


You can filter the list of dags in webserver by owner name when authentication is turned on by setting webserver:filter_by_owner in your config. With this, a user will see only the dags which it is owner of, unless it is a superuser.

filter_by_owner = True


Airflow has initial support for Kerberos. This means that airflow can renew kerberos tickets for itself and store it in the ticket cache. The hooks and dags can make use of ticket to authenticate against kerberized services.


Please note that at this time, not all hooks have been adjusted to make use of this functionality. Also it does not integrate kerberos into the web interface and you will have to rely on network level security for now to make sure your service remains secure.

Celery integration has not been tried and tested yet. However, if you generate a key tab for every host and launch a ticket renewer next to every worker it will most likely work.

Enabling kerberos


To enable kerberos you will need to generate a (service) key tab.

# in the kadmin.local or kadmin shell, create the airflow principal
kadmin:  addprinc -randkey airflow/

# Create the airflow keytab file that will contain the airflow principal
kadmin:  xst -norandkey -k airflow.keytab airflow/

Now store this file in a location where the airflow user can read it (chmod 600). And then add the following to your airflow.cfg

security = kerberos

keytab = /etc/airflow/airflow.keytab
reinit_frequency = 3600
principal = airflow

Launch the ticket renewer by

# run ticket renewer
airflow kerberos


If want to use impersonation this needs to be enabled in core-site.xml of your hadoop config.




Of course if you need to tighten your security replace the asterisk with something more appropriate.

Using kerberos authentication

The hive hook has been updated to take advantage of kerberos authentication. To allow your DAGs to use it, simply update the connection details with, for example:

{ "use_beeline": true, "principal": "hive/_HOST@EXAMPLE.COM"}

Adjust the principal to your settings. The _HOST part will be replaced by the fully qualified domain name of the server.

You can specify if you would like to use the dag owner as the user for the connection or the user specified in the login section of the connection. For the login user, specify the following as extra:

{ "use_beeline": true, "principal": "hive/_HOST@EXAMPLE.COM", "proxy_user": "login"}

For the DAG owner use:

{ "use_beeline": true, "principal": "hive/_HOST@EXAMPLE.COM", "proxy_user": "owner"}

and in your DAG, when initializing the HiveOperator, specify:


OAuth Authentication

GitHub Enterprise (GHE) Authentication

The GitHub Enterprise authentication backend can be used to authenticate users against an installation of GitHub Enterprise using OAuth2. You can optionally specify a team whitelist (composed of slug cased team names) to restrict login to only members of those teams.

authenticate = True
auth_backend = airflow.contrib.auth.backends.github_enterprise_auth

host =
client_id = oauth_key_from_github_enterprise
client_secret = oauth_secret_from_github_enterprise
oauth_callback_route = /example/ghe_oauth/callback
allowed_teams = 1, 345, 23


If you do not specify a team whitelist, anyone with a valid account on your GHE installation will be able to login to Airflow.

Setting up GHE Authentication

An application must be setup in GHE before you can use the GHE authentication backend. In order to setup an application:

  1. Navigate to your GHE profile
  2. Select ‘Applications’ from the left hand nav
  3. Select the ‘Developer Applications’ tab
  4. Click ‘Register new application’
  5. Fill in the required information (the ‘Authorization callback URL’ must be fully qualified e.g.
  6. Click ‘Register application’
  7. Copy ‘Client ID’, ‘Client Secret’, and your callback route to your airflow.cfg according to the above example

Using GHE Authentication with

It is possible to use GHE authentication with

  1. Create an Oauth App
  2. Copy ‘Client ID’, ‘Client Secret’ to your airflow.cfg according to the above example
  3. Set host = and oauth_callback_route = /oauth/callback in airflow.cfg

Google Authentication

The Google authentication backend can be used to authenticate users against Google using OAuth2. You must specify the domains to restrict login, separated with a comma, to only members of those domains.

authenticate = True
auth_backend = airflow.contrib.auth.backends.google_auth

client_id = google_client_id
client_secret = google_client_secret
oauth_callback_route = /oauth2callback
domain = ","

Setting up Google Authentication

An application must be setup in the Google API Console before you can use the Google authentication backend. In order to setup an application:

  1. Navigate to
  2. Select ‘Credentials’ from the left hand nav
  3. Click ‘Create credentials’ and choose ‘OAuth client ID’
  4. Choose ‘Web application’
  5. Fill in the required information (the ‘Authorized redirect URIs’ must be fully qualified e.g.
  6. Click ‘Create’
  7. Copy ‘Client ID’, ‘Client Secret’, and your redirect URI to your airflow.cfg according to the above example


SSL can be enabled by providing a certificate and key. Once enabled, be sure to use “https://” in your browser.

web_server_ssl_cert = <path to cert>
web_server_ssl_key = <path to key>

Enabling SSL will not automatically change the web server port. If you want to use the standard port 443, you’ll need to configure that too. Be aware that super user privileges (or cap_net_bind_service on Linux) are required to listen on port 443.

# Optionally, set the server to listen on the standard SSL port.
web_server_port = 443
base_url = http://<hostname or IP>:443

Enable CeleryExecutor with SSL. Ensure you properly generate client and server certs and keys.

ssl_active = True
ssl_key = <path to key>
ssl_cert = <path to cert>
ssl_cacert = <path to cacert>


Airflow has the ability to impersonate a unix user while running task instances based on the task’s run_as_user parameter, which takes a user’s name.

NOTE: For impersonations to work, Airflow must be run with sudo as subtasks are run with sudo -u and permissions of files are changed. Furthermore, the unix user needs to exist on the worker. Here is what a simple sudoers file entry could look like to achieve this, assuming as airflow is running as the airflow user. Note that this means that the airflow user must be trusted and treated the same way as the root user.


Subtasks with impersonation will still log to the same folder, except that the files they log to will have permissions changed such that only the unix user can write to it.

Default Impersonation

To prevent tasks that don’t use impersonation to be run with sudo privileges, you can set the core:default_impersonation config which sets a default user impersonate if run_as_user is not set.

default_impersonation = airflow