dbcve.org
SparkApplication · Apache

CVE-2018-17190

CRITICAL · 9.8 CVSS v3.0 Published 2018-11-19
Mitigation only
No fix yet — a mitigation exists. There is no fixed release. A documented workaround reduces exposure in the meantime.
See remediation →
100/100
Remediation priority · Urgent
Remotely reachable No privileges 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
In all versions of Apache Spark, its standalone resource manager accepts code to execute on a 'master' host, that then runs that code on 'worker' hosts. The master itself does not, by design, execute user code. A specially-crafted request to the master can, however, cause the master to execute code too. Note that this does not affect standalone clusters with authentication enabled. While the master host typically has less outbound access to other resources than a worker, the execution of code on the master is nevertheless unexpected.

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's standalone resource manager allows arbitrary code execution on the master host through specially-crafted requests, despite the master by design only distributing code to workers without executing it itself. This is a critical remote code execution (RCE) vulnerability affecting all versions of Spark in standalone mode without authentication.

MitigationEnable authentication on Spark standalone clusters, which prevents this vulnerability as noted in the official description.

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:all versions

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
None
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

CVSS:3.0/AV:N/AC:L/PR:N/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 Spark deployment mode
    Check if Spark is running in standalone mode by reviewing process details or configuration files. Look for the Spark master process and verify no cluster manager like YARN or Kubernetes is in use.
    Affected if Spark is running in standalone mode with no external cluster manager
  2. Check authentication configuration
    Review Spark configuration files (such as spark-defaults.conf, spark-env.sh, or custom configuration) for authentication settings. Look for properties like spark.authenticate, spark.master.authenticate, or related security flags.
    Affected if Authentication is not explicitly enabled in the Spark configuration
  3. Verify authentication status via Spark UI
    Access the Spark standalone master's web UI (typically on port 8080) and check if endpoints respond without requiring credentials. Attempt to submit an application or access the REST API without authentication tokens.
    Affected if The Spark master accepts requests without requiring any authentication credentials
  4. Inspect security-related properties
    Search all Spark configuration files for properties beginning with 'spark.authenticate' or containing values related to authentication. Check if these properties are set to 'true' or properly configured.
    Affected if No authentication-related security properties are found or they are set to false/not configured

A user is affected if Apache Spark is deployed in standalone mode and authentication is not enabled, allowing unauthenticated attackers to execute arbitrary code on the master host.

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
Mitigation available No clean upgrade yet — mitigate in the meantime
Mitigation

Enable authentication on Spark standalone clusters, which prevents this vulnerability as noted in the official description.

Recommended fix Moderate confidence

Apache Spark 2.3.3 or later, or Apache Spark 2.4.0 or later

  1. Enable authentication on Spark standalone clusters by configuring spark.authenticate=true and setting spark.authenticate.secret to a strong value
  2. Alternatively, upgrade to a fixed version of Apache Spark that addresses this vulnerability
  3. If upgrade is not immediately possible, ensure network ACLs restrict access to the Spark master port (7077) to trusted hosts only
Caveat After enabling authentication, all clients must provide the authentication secret when connecting to the cluster

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

Fix this in Spark Scoped from the published advisory
  • Consultation4.0 h
  • Implementation4.0 h
  • Testing4.0 h
  • Review / QA2.0 h
14.0 hours of engineering $2,480
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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-2018-17190 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
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  • Version or environment caveats, and links to real fixes
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