Apache Spark uses the standard process outlined by the Apache Security Team for reporting vulnerabilities. Note that vulnerabilities should not be publicly disclosed until the project has responded.

To report a possible security vulnerability, please email [email protected]. This is a non-public list that will reach the Apache Security team, as well as the Spark PMC.

This CVE is only an update to CVE-2022-33891 to clarify that version 3.1.3 is also affected. It is otherwise not a new vulnerability. Note that Apache Spark 3.1.x is EOL now.

Severity: Medium

Vendor: The Apache Software Foundation

Versions Affected:

• Versions prior to 3.4.0

Description:

In Apache Spark versions prior to 3.4.0, applications using spark-submit can specify a 'proxy-user' to run as, limiting privileges. The application can execute code with the privileges of the submitting user, however, by providing malicious configuration-related classes on the classpath. This affects architectures relying on proxy-user, for example those using Apache Livy to manage submitted applications.

This issue is being tracked as SPARK-41958

Mitigation:

• Update to Apache Spark 3.4.0 or later, and ensure that spark.submit.proxyUser.allowCustomClasspathInClusterMode is set to its default of "false", and is not overridden by submitted applications.

Credit:

• Hideyuki Furue (finder)
• Yi Wu (Databricks) (remediation developer)

Severity: Medium

Vendor: The Apache Software Foundation

Versions Affected:

• 3.2.1 and earlier
• 3.3.0

Description:

A stored cross-site scripting (XSS) vulnerability in Apache Spark 3.2.1 and earlier, and 3.3.0, allows remote attackers to execute arbitrary JavaScript in the web browser of a user, by including a malicious payload into the logs which would be returned in logs rendered in the UI.

Mitigation:

• Upgrade to Spark 3.2.2, or 3.3.1 or later

Credit:

• Florian Walter (Veracode)

Severity: Important

Vendor: The Apache Software Foundation

Versions Affected:

• 3.1.3 and earlier (previously, this was marked as fixed in 3.1.3; this change is tracked as CVE-2023-32007)
• 3.2.0 to 3.2.1

Description:

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.

Mitigation

• Update to Spark 3.2.2, or 3.3.0 or later

Credit:

• Kostya Torchinsky (Databricks)

Severity: Medium

Vendor: The Apache Software Foundation

Versions Affected:

• Apache Spark 3.1.2 and earlier

Description:

Apache Spark supports end-to-end encryption of RPC connections via spark.authenticate and spark.network.crypto.enabled. In versions 3.1.2 and earlier, it uses a bespoke mutual authentication protocol that allows for full encryption key recovery. After an initial interactive attack, this would allow someone to decrypt plaintext traffic offline. Note that this does not affect security mechanisms controlled by spark.authenticate.enableSaslEncryption, spark.io.encryption.enabled, spark.ssl, spark.ui.strictTransportSecurity.

Mitigation:

• Update to Spark 3.1.3 or later

Credit:

• Steve Weis (Databricks)

Severity: Important

Vendor: The Apache Software Foundation

Versions Affected:

• Apache Spark 2.4.5 and earlier

Description:

In Apache Spark 2.4.5 and earlier, a standalone resource manager's master may be configured to require authentication (spark.authenticate) via a shared secret. When enabled, however, a specially-crafted RPC to the master can succeed in starting an application's resources on the Spark cluster, even without the shared key. This can be leveraged to execute shell commands on the host machine.

This does not affect Spark clusters using other resource managers (YARN, Mesos, etc).

Mitigation:

• Users should update to Spark 2.4.6 or 3.0.0.
• Where possible, network access to the cluster machines should be restricted to trusted hosts only.

Credit:

• Ayoub Elaassal

Severity: Important

Vendor: The Apache Software Foundation

Versions affected:

• All Spark 1.x, Spark 2.0.x, Spark 2.1.x, and 2.2.x versions
• Spark 2.3.0 to 2.3.2

Description:

Prior to Spark 2.3.3, in certain situations Spark would write user data to local disk unencrypted, even if spark.io.encryption.enabled=true. This includes cached blocks that are fetched to disk (controlled by spark.maxRemoteBlockSizeFetchToMem); in SparkR, using parallelize; in Pyspark, using broadcast and parallelize; and use of python udfs.

Mitigation:

• 1.x, 2.0.x, 2.1.x, 2.2.x, 2.3.x users should upgrade to 2.3.3 or newer, including 2.4.x

Credit:

• This issue was reported by Thomas Graves of NVIDIA.

Severity: Important

Vendor: The Apache Software Foundation

Versions affected:

• All Spark 1.x, Spark 2.0.x, and Spark 2.1.x versions
• Spark 2.2.0 to 2.2.2
• Spark 2.3.0 to 2.3.1

Description:

When using PySpark, it's possible for a different local user to connect to the Spark application and impersonate the user running the Spark application. This affects versions 1.x, 2.0.x, 2.1.x, 2.2.0 to 2.2.2, and 2.3.0 to 2.3.1.

Mitigation:

• 1.x, 2.0.x, 2.1.x, and 2.2.x users should upgrade to 2.2.3 or newer
• 2.3.x users should upgrade to 2.3.2 or newer
• Otherwise, affected users should avoid using PySpark in multi-user environments.

Credit:

• Luca Canali and Jose Carlos Luna Duran, CERN

Severity: Low

Vendor: The Apache Software Foundation

Versions Affected:

• All versions of Apache Spark

Description:

Spark's 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.

Mitigation:

Enable authentication on any Spark standalone cluster that is not otherwise secured from unwanted access, for example by network-level restrictions. Use spark.authenticate and related security properties described at https://spark.apache.org/docs/latest/security.html

Credit:

• Andre Protas, Apple Information Security

Severity: Low

Vendor: The Apache Software Foundation

Versions Affected

• 2.1.x release branch and earlier
• 2.2.x release branch before Spark 2.2.3
• 2.3.x release branch before Spark 2.3.3

Description:

Spark's Apache Maven-based build includes a convenience script, 'build/mvn', that downloads and runs a zinc server to speed up compilation. This server will accept connections from external hosts by default. A specially-crafted request to the zinc server could cause it to reveal information in files readable to the developer account running the build. Note that this issue does not affect end users of Spark, only developers building Spark from source code.

Mitigation:

• Spark users are not affected, as zinc is only a part of the build process.
• Spark developers may simply use a local Maven installation's 'mvn' command to build, and avoid running build/mvn and zinc.
• Spark developers building actively-developed branches (2.2.x, 2.3.x, 2.4.x, master) may update their branches to receive mitigations already patched onto the build/mvn script
• Spark developers running zinc separately may include "-server 127.0.0.1" in its command line, and consider additional flags like "-idle-timeout 30m" to achieve similar mitigation.

Credit:

• Andre Protas, Apple Information Security

Severity: Medium

Vendor: The Apache Software Foundation

Versions Affected:

• Spark versions from 1.3.0, running standalone master with REST API enabled, or running Mesos master with cluster mode enabled; suggested mitigations resolved the issue as of Spark 2.4.0.

Description:

From version 1.3.0 onward, Spark's standalone master exposes a REST API for job submission, in addition to the submission mechanism used by spark-submit. In standalone, the config property spark.authenticate.secret establishes a shared secret for authenticating requests to submit jobs via spark-submit. However, the REST API does not use this or any other authentication mechanism, and this is not adequately documented. In this case, a user would be able to run a driver program without authenticating, but not launch executors, using the REST API. This REST API is also used by Mesos, when set up to run in cluster mode (i.e., when also running MesosClusterDispatcher), for job submission. Future versions of Spark will improve documentation on these points, and prohibit setting spark.authenticate.secret when running the REST APIs, to make this clear. Future versions will also disable the REST API by default in the standalone master by changing the default value of spark.master.rest.enabled to false.

Mitigation:

For standalone masters, disable the REST API by setting spark.master.rest.enabled to false if it is unused, and/or ensure that all network access to the REST API (port 6066 by default) is restricted to hosts that are trusted to submit jobs. Mesos users can stop the MesosClusterDispatcher, though that will prevent them from running jobs in cluster mode. Alternatively, they can ensure access to the MesosRestSubmissionServer (port 7077 by default) is restricted to trusted hosts.

Credit:

• Imran Rashid, Cloudera
• Fengwei Zhang, Alibaba Cloud Security Team

Severity: Medium

Versions Affected:

• Spark 2.1.0 through 2.1.2
• Spark 2.2.0 through 2.2.1
• Spark 2.3.0

Description:

In Apache Spark 2.1.0 to 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, it's possible for a malicious user to construct a URL pointing to a Spark cluster's UI's job and stage info pages, and if a user can be tricked into accessing the URL, can be used to cause script to execute and expose information from the user's view of the Spark UI. While some browsers like recent versions of Chrome and Safari are able to block this type of attack, current versions of Firefox (and possibly others) do not.

Mitigation:

• 2.1.x users should upgrade to 2.1.3 or newer
• 2.2.x users should upgrade to 2.2.2 or newer
• 2.3.x users should upgrade to 2.3.1 or newer

Credit:

• Spencer Gietzen, Rhino Security Labs

Severity: High

Vendor: The Apache Software Foundation

Versions affected:

• Spark versions through 2.1.2
• Spark 2.2.0 to 2.2.1
• Spark 2.3.0

Description:

In Apache Spark up to and including 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, when using PySpark or SparkR, it's possible for a different local user to connect to the Spark application and impersonate the user running the Spark application.

Mitigation:

• 1.x, 2.0.x, and 2.1.x users should upgrade to 2.1.3 or newer
• 2.2.x users should upgrade to 2.2.2 or newer
• 2.3.x users should upgrade to 2.3.1 or newer
• Otherwise, affected users should avoid using PySpark and SparkR in multi-user environments.

Credit:

• Nehmé Tohmé, Cloudera, Inc.

JIRA: SPARK-20922

Severity: Medium

Vendor: The Apache Software Foundation

Versions Affected:

• Versions of Apache Spark from 1.6.0 until 2.1.1

Description:

In Apache Spark 1.6.0 until 2.1.1, the launcher API performs unsafe deserialization of data received by its socket. This makes applications launched programmatically using the launcher API potentially vulnerable to arbitrary code execution by an attacker with access to any user account on the local machine. It does not affect apps run by spark-submit or spark-shell. The attacker would be able to execute code as the user that ran the Spark application. Users are encouraged to update to version 2.1.2, 2.2.0 or later.

Mitigation:

Update to Apache Spark 2.1.2, 2.2.0 or later.

Credit:

• Aditya Sharad, Semmle

JIRA: SPARK-20393

Severity: Medium

Vendor: The Apache Software Foundation

Versions Affected:

• Versions of Apache Spark before 2.1.2, 2.2.0

Description:

It is possible for an attacker to take advantage of a user's trust in the server to trick them into visiting a link that points to a shared Spark cluster and submits data including MHTML to the Spark master, or history server. This data, which could contain a script, would then be reflected back to the user and could be evaluated and executed by MS Windows-based clients. It is not an attack on Spark itself, but on the user, who may then execute the script inadvertently when viewing elements of the Spark web UIs.

Mitigation:

Update to Apache Spark 2.1.2, 2.2.0 or later.

Example:

Request:

GET /app/?appId=Content-Type:%20multipart/related;%20boundary=_AppScan%0d%0a--
_AppScan%0d%0aContent-Location:foo%0d%0aContent-Transfer-
Encoding:base64%0d%0a%0d%0aPGh0bWw%2bPHNjcmlwdD5hbGVydCgiWFNTIik8L3NjcmlwdD48L2h0bWw%2b%0d%0a
HTTP/1.1

Excerpt from response:

<div class="row-fluid">No running application with ID Content-Type: multipart/related;
boundary=_AppScan
--_AppScan
Content-Location:foo
Content-Transfer-Encoding:base64
PGh0bWw+PHNjcmlwdD5hbGVydCgiWFNTIik8L3NjcmlwdD48L2h0bWw+
</div>

Result: In the above payload the BASE64 data decodes as:

<html><script>alert("XSS")</script></html>

Credit:

• Mike Kasper, Nicholas Marion
• IBM z Systems Center for Secure Engineering