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Actively exploited in the wild. This CVE is on the CISA Known Exploited Vulnerabilities list — treat remediation as urgent. Federal remediation due by 28 Mar 2023.
SparkApplication · Apache

CVE-2022-33891

HIGH · 8.8 CVSS v3.1 Published 2022-07-18
Fix available
A fix is available. Upgrade to after 3.2.1 or later.
See remediation →
100/100
Remediation priority · Urgent
In the wild High EPSS Public exploit Remotely reachable Zero-click

Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.

NVD · unedited
The Apache Spark UI offers the possibility to enable ACLs via the configuration option spark.acls.enable. With an authentication filter, this checks whether a user has access permissions to view or modify the application. If ACLs are enabled, a code path in HttpSecurityFilter can allow someone to perform impersonation by providing an arbitrary user name. A malicious user might then be able to reach a permission check function that will ultimately build a Unix shell command based on their input, and execute it. This will result in arbitrary shell command execution as the user Spark is currently running as. This affects Apache Spark versions 3.0.3 and earlier, versions 3.1.1 to 3.1.2, and versions 3.2.0 to 3.2.1.

Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.

dbcve analysis · high confidence

Apache Spark UI has an authentication bypass in HttpSecurityFilter where ACL enforcement can be circumvented by providing an arbitrary username, allowing user impersonation that reaches a permission check function constructing OS commands from user input, resulting in arbitrary shell command execution as the Spark service account.

MitigationIf ACLs are not required, disable spark.acls.enable configuration; otherwise upgrade to Apache Spark 3.2.2, 3.1.3, or 3.0.4 or later which contain the fix.

Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.

Affected products & versions What the vendor confirmedThe version ranges the vendor confirmed as vulnerable. If your version sits inside a range here, treat yourself as exposed until you have upgraded.

NVD · CPE data
SparkApplication
Affected:<= 3.0.3>= 3.1.1, <= 3.1.2>= 3.2.0, <= 3.2.1

CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.

From the vector
Attack vector
Network
Complexity
Low
Privileges
Low
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.

dbcve checks

Work through these to decide whether this CVE applies to you.

  1. Identify Apache Spark version
    Run 'spark-submit --version' or check the spark-core JAR file name in your Spark installation directory (typically under $SPARK_HOME/jars/). Compare the version number to the affected ranges: <= 3.0.3, >= 3.1.1 and <= 3.1.2, >= 3.2.0 and <= 3.2.1.
    Affected if The installed version falls within any of the affected version ranges.
  2. Verify ACLs configuration
    Check your Spark configuration file (spark-defaults.conf, spark-site.xml, or programmatically via SparkConf) for the property 'spark.acls.enable'. You can also run 'spark.sparkContext.getConf().get("spark.acls.enable")' in a Spark shell if you have access.
    Affected if spark.acls.enable is set to 'true'.
  3. Confirm Spark UI is accessible
    Check if the Spark UI port (default 4040, or as configured via spark.ui.port) is exposed to network access or if the application is running in a way that allows external HTTP requests to reach the UI.
    Affected if The Spark UI is network-accessible from untrusted sources.
  4. Check for custom filter configurations
    Inspect any custom security filter configurations in spark-acls or similar configuration files. Look for HttpSecurityFilter-related settings that might control authentication behavior.
    Affected if HttpSecurityFilter is in use and ACLs are enabled.

You are affected if you are running a vulnerable Apache Spark version (3.0.3 or earlier, 3.1.1-3.1.2, or 3.2.0-3.2.1) AND have spark.acls.enable set to true, with the Spark UI accessible to potential attackers.

Generated from the published advisory. Verify against your own configuration.

Check your environment

Paste your version and any relevant configuration and it will be compared against the affected criteria above. Do not include secrets or credentials.

AI-assisted, checked against the advisory. Informational, not a guarantee.

Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.

dbcve · scoped
Upgrade available Upgrade to a release after 3.2.1
Interim mitigation

If ACLs are not required, disable spark.acls.enable configuration; otherwise upgrade to Apache Spark 3.2.2, 3.1.3, or 3.0.4 or later which contain the fix.

Recommended fix High confidence

Apache Spark 3.2.2 (or 3.1.3, 3.0.4 depending on your major version branch)

  1. 1. Determine the current Apache Spark version by running: spark-submit --version or checking the Spark UI
  2. 2. Plan for upgrade during a maintenance window
  3. 3. Download Apache Spark 3.2.2 or later for Spark 3.2.x users, 3.1.3 or later for Spark 3.1.x users, or 3.0.4 or later for Spark 3.0.x users from https://spark.apache.org/downloads.html
  4. 4. Backup existing Spark configuration files (spark-defaults.conf, spark-env.sh)
  5. 5. Deploy the new Spark version to all nodes in the cluster
  6. 6. Verify spark.acls.enable setting in your configuration - if not explicitly needed, set it to false as a defense-in-depth measure
  7. 7. Restart Spark services (Spark Master, Workers, and History Server)
  8. 8. Validate that Spark applications run correctly post-upgrade
Caveat Minor point releases typically have backward compatibility, but test in staging first; review release notes for any behavior changes in Spark UI or security features

Generated from the published advisory — verify against the referenced sources before acting.

Fix this in Spark Exploited in the wild — priority engagement
  • Consultation3.0 h
  • Implementation2.0 h
  • Testing4.0 h
  • Review / QA2.0 h
11.0 hours of engineering $1,920
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References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.

Primary sources

Practitioner notes

Contributed

Peer-ranked notes from engineers who’ve handled CVE-2022-33891 in production — separate from our analysis above.

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What this is

A place for practitioners to share what actually worked: a mitigation you’ve tested, a configuration change, a version- or environment-specific caveat, or a link to a verified patch. The most useful notes rise to the top as peers upvote them, so the signal stays high.

What belongs here
  • Verified mitigations, workarounds, and config changes
  • Version or environment caveats, and links to real fixes
  • No weaponised exploit code, or anything meant to cause harm
  • No spam, self-promotion, credentials, or personal data