website/docs/tasks/debug-application-cluster/audit.md

reviewers, title

reviewers	title
soltysh sttts ericchiang	Auditing

• TOC {:toc}

{% include feature-state-beta.md %}

Kubernetes auditing provides a security-relevant chronological set of records documenting the sequence of activities that have affected system by individual users, administrators or other components of the system. It allows cluster administrator to answer the following questions:

• what happened?
• when did it happen?
• who initiated it?
• on what did it happen?
• where was it observed?
• from where was it initiated?
• to where was it going?

Kube-apiserver performs auditing. Each request on each stage of its execution generates an event, which is then pre-processed according to a certain policy and written to a backend. You can find more details about the pipeline in the design proposal.

Note, that audit logging feature increases apiserver memory consumption, since some context required for auditing is stored for each request. Additionally, memory consumption depends on the audit logging configuration.

Audit Policy

Audit policy defines rules about what events should be recorded and what data they should include. When an event is processed, it's compared against the list of rules in order. The first matching rule sets the audit level of the event. The audit policy object structure is defined in the audit.k8s.io API group.

You can pass a file with the policy to kube-apiserver using the --audit-policy-file flag. If the flag is omitted, no events are logged. Note: kind and apiVersion fields along with rules must be provided in the audit policy file. A policy with no (0) rules, or a policy that doesn't provide valid apiVersion and kind values is treated as illegal.

Some example audit policy files:

{% include code.html language="yaml" file="audit-policy.yaml" ghlink="/docs/tasks/debug-application-cluster/audit-policy.yaml" %}

You can use a minimal audit policy file to log all requests at the Metadata level:

# Log all requests at the Metadata level.
apiVersion: audit.k8s.io/v1beta1
kind: Policy
rules:
- level: Metadata

The audit profile used by GCE should be used as reference by admins constructing their own audit profiles.

Audit backends

Audit backends implement exporting audit events to an external storage. Kube-apiserver out of the box provides two backends:

• Log backend, which writes events to a disk
• Webhook backend, which sends events to an external API

In both cases, audit events structure is defined by the API in the audit.k8s.io API group. The current version of the API is v1beta1.

Note: In case of patches, request body is a JSON array with patch operations, not a JSON object with an appropriate Kubernetes API object. For example, the following request body is a valid patch request to /apis/batch/v1/namespaces/some-namespace/jobs/some-job-name.

[
  {
    "op": "replace",
    "path": "/spec/parallelism",
    "value": 0
  },
  {
    "op": "remove",
    "path": "/spec/template/spec/containers/0/terminationMessagePolicy"
  }
]

Log backend

Log backend writes audit events to a file in JSON format. You can configure log audit backend using the following kube-apiserver flags:

• --audit-log-path specifies the log file path that log backend uses to write audit events. Not specifying this flag disables log backend. - means standard out
• --audit-log-maxage defined the maximum number of days to retain old audit log files
• --audit-log-maxbackup defines the maximum number of audit log files to retain
• --audit-log-maxsize defines the maximum size in megabytes of the audit log file before it gets rotated

Webhook backend

Webhook backend sends audit events to a remote API, which is assumed to be the same API as kube-apiserver exposes. You can configure webhook audit backend using the following kube-apiserver flags:

• --audit-webhook-config-file specifies the path to a file with a webhook configuration. Webhook configuration is effectively a kubeconfig.
• --audit-webhook-initial-backoff specifies the amount of time to wait after the first failed request before retrying. Subsequent requests are retried with exponential backoff.

The webhook config file uses the kubeconfig format to specify the remote address of the service and credentials used to connect to it.

Batching

Both log and webhook backends support batching. Using webhook as an example, here's the list of available flags. To get the same flag for log backend, replace webhook with log in the flag name. By default, batching is enabled in webhook and disabled in log. Similarly, by default throttling is enabled in webhook and disabled in log.

• --audit-webhook-mode defines the buffering strategy. One of the following:
  • batch - buffer events and asynchronously process them in batches. This is the default.
  • blocking - block API server responses on processing each individual event.

The following flags are used only in the batch mode.

• --audit-webhook-batch-buffer-size defines the number of events to buffer before batching. If the rate of incoming events overflows the buffer, events are dropped.
• --audit-webhook-batch-max-size defines the maximum number of events in one batch.
• --audit-webhook-batch-max-wait defines the maximum amount of time to wait before unconditionally batching events in the queue.
• --audit-webhook-batch-throttle-qps defines the maximum average number of batches generated per second.
• --audit-webhook-batch-throttle-burst defines the maximum number of batches generated at the same moment if the allowed QPS was underutilized previously.

Parameter tuning

Parameters should be set to accommodate the load on the apiserver.

For example, if kube-apiserver receives 100 requests each second, and each request is audited only on ResponseStarted and ResponseComplete stages, you should account for ~200 audit events being generated each second. Assuming that there are up to 100 events in a batch, you should set throttling level at at least 2 QPS. Assuming that the backend can take up to 5 seconds to write events, you should set the buffer size to hold up to 5 seconds of events, i.e. 10 batches, i.e. 1000 events.

In most cases however, the default parameters should be sufficient and you don't have to worry about setting them manually. You can look at the following Prometheus metrics exposed by kube-apiserver and in the logs to monitor the state of the auditing subsystem.

• apiserver_audit_event_total metric contains the total number of audit events exported.
• apiserver_audit_error_total metric contains the total number of events dropped due to an error during exporting.

Multi-cluster setup

If you're extending the Kubernetes API with the aggregation layer, you can also set up audit logging for the aggregated apiserver. To do this, pass the configuration options in the same format as described above to the aggregated apiserver and set up the log ingesting pipeline to pick up audit logs. Different apiservers can have different audit configurations and different audit policies.

Log Collector Examples

Use fluentd to collect and distribute audit events from log file

Fluentd is an open source data collector for unified logging layer. In this example, we will use fluentd to split audit events by different namespaces.

1. install fluentd, fluent-plugin-forest and fluent-plugin-rewrite-tag-filter in the kube-apiserver node

2. create a config file for fluentd

   $ cat <<EOF > /etc/fluentd/config
   # fluentd conf runs in the same host with kube-apiserver
   <source>
       @type tail
       # audit log path of kube-apiserver
       path /var/log/audit
       pos_file /var/log/audit.pos
       format json
       time_key time
       time_format %Y-%m-%dT%H:%M:%S.%N%z
       tag audit
   </source>
   
   <filter audit>
       #https://github.com/fluent/fluent-plugin-rewrite-tag-filter/issues/13
       type record_transformer
       enable_ruby
       <record>
        namespace ${record["objectRef"].nil? ? "none":(record["objectRef"]["namespace"].nil? ?  "none":record["objectRef"]["namespace"])}
       </record>
   </filter>
   
   <match audit>
       # route audit according to namespace element in context
       @type rewrite_tag_filter
       rewriterule1 namespace ^(.+) ${tag}.$1
   </match>
   
   <filter audit.**>
      @type record_transformer
      remove_keys namespace
   </filter>
   
   <match audit.**>
       @type forest
       subtype file
       remove_prefix audit
       <template>
           time_slice_format %Y%m%d%H
           compress gz
           path /var/log/audit-${tag}.*.log
           format json
           include_time_key true
       </template>
   </match>
   

3. start fluentd

   $ fluentd -c /etc/fluentd/config  -vv
   

4. start kube-apiserver with the following options:

   --audit-policy-file=/etc/kubernetes/audit-policy.yaml --audit-log-path=/var/log/kube-audit --audit-log-format=json
   

5. check audits for different namespaces in /var/log/audit-*.log

Use logstash to collect and distribute audit events from webhook backend

Logstash is an open source, server-side data processing tool. In this example, we will use logstash to collect audit events from webhook backend, and save events of different users into different files.

1. install logstash

2. create config file for logstash

   $ cat <<EOF > /etc/logstash/config
   input{
       http{
           #TODO, figure out a way to use kubeconfig file to authenticate to logstash
           #https://www.elastic.co/guide/en/logstash/current/plugins-inputs-http.html#plugins-inputs-http-ssl
           port=>8888
       }
   }
   filter{
       split{
           # Webhook audit backend sends several events together with EventList
           # split each event here.
           field=>[items]
           # We only need event subelement, remove others.
           remove_field=>[headers, metadata, apiVersion, "@timestamp", kind, "@version", host]
       }
       mutate{
           rename => {items=>event}
       }
   }
   output{
       file{
           # Audit events from different users will be saved into different files.
           path=>"/var/log/kube-audit-%{[event][user][username]}/audit"
       }
   }
   

3. start logstash

   $ bin/logstash -f /etc/logstash/config --path.settings /etc/logstash/
   

4. create a kubeconfig file for kube-apiserver webhook audit backend

   $ cat <<EOF > /etc/kubernetes/audit-webhook-kubeconfig
   apiVersion: v1
   clusters:
   - cluster:
       server: http://<ip_of_logstash>:8888
     name: logstash
   contexts:
   - context:
       cluster: logstash
       user: ""
     name: default-context
   current-context: default-context
   kind: Config
   preferences: {}
   users: []
   EOF
   

5. start kube-apiserver with the following options:

   --audit-policy-file=/etc/kubernetes/audit-policy.yaml --audit-webhook-config-file=/etc/kubernetes/audit-webhook-kubeconfig
   

6. check audits in logstash node's directories /var/log/kube-audit-*/audit

Note that in addition to file output plugin, logstash has a variety of outputs that let users route data where they want. For example, users can emit audit events to elasticsearch plugin which supports full-text search and analytics.

Legacy Audit

Note: Legacy Audit is deprecated and is disabled by default since Kubernetes 1.8. Legacy Audit will be removed in 1.12. To fallback to this legacy audit, disable the advanced auditing feature using the AdvancedAuditing feature gate in kube-apiserver:

--feature-gates=AdvancedAuditing=false

In legacy format, each audit log entry contains two lines:

1. The request line containing a unique ID to match the response and request metadata, such as the source IP, requesting user, impersonation information, resource being requested, etc.
2. The response line containing a unique ID matching the request line and the response code.

Example output for admin user listing pods in the default namespace:

2017-03-21T03:57:09.106841886-04:00 AUDIT: id="c939d2a7-1c37-4ef1-b2f7-4ba9b1e43b53" ip="127.0.0.1" method="GET" user="admin" groups="\"system:masters\",\"system:authenticated\"" as="<self>" asgroups="<lookup>" namespace="default" uri="/api/v1/namespaces/default/pods"
2017-03-21T03:57:09.108403639-04:00 AUDIT: id="c939d2a7-1c37-4ef1-b2f7-4ba9b1e43b53" response="200"

Configuration

Kube-apiserver provides the following options which are responsible for configuring where and how audit logs are handled:

• audit-log-path - enables the audit log pointing to a file where the requests are being logged to, '-' means standard out.
• audit-log-maxage - specifies maximum number of days to retain old audit log files based on the timestamp encoded in their filename.
• audit-log-maxbackup - specifies maximum number of old audit log files to retain.
• audit-log-maxsize - specifies maximum size in megabytes of the audit log file before it gets rotated. Defaults to 100MB.

If an audit log file already exists, Kubernetes appends new audit logs to that file. Otherwise, Kubernetes creates an audit log file at the location you specified in audit-log-path. If the audit log file exceeds the size you specify in audit-log-maxsize, Kubernetes will rename the current log file by appending the current timestamp on the file name (before the file extension) and create a new audit log file. Kubernetes may delete old log files when creating a new log file; you can configure how many files are retained and how old they can be by specifying the audit-log-maxbackup and audit-log-maxage options.