Severity: low
Affected versions:
- Apache Spark (org.apache.spark:) 3.0.0 before 3.5.8
Description:
There is a lack of XSS escaping in the Spark History Server prior to 3.5.8 which allows a malicious Spark job to generate arbitrary unescaped frontend code which could lead to a minimal privilege escalation in browser. Users are encouraged to upgrade to Spark 3.5.8 or later.
This CVE is marked as "low" since the path to exploit requires both relatively high permissions (ability to launch a Spark job) and requires tricking a user with higher permissions to log in and visit the Spark history web page.
Users are encouraged to upgrade their Spark history servers to Spark 3.5.8 or later.
This issue is being tracked as SPARK-53747
Credit:
Yann Gourio (finder)
References:
https://spark.apache.org/ https://www.cve.org/CVERecord?id=CVE-2026-32773 https://issues.apache.org/jira/browse/SPARK-53747
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.
Severity: low
Affected versions:
- Apache Spark (org.apache.spark:spark-core2.13, org.apache.spark:spark-core2.12) before 3.5.7 - Apache Spark (org.apache.spark:spark-core2.13, org.apache.spark:spark-core2.12) 4.0.0 before 4.0.1
Description:
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.
This issue is being tracked as SPARK-52381
Credit:
Alexandre Pujol (Linagora) (finder)
References:
https://github.com/apache/spark/pull/51312 https://github.com/apache/spark/pull/51323 https://issues.apache.org/jira/browse/SPARK-52381 https://spark.apache.org/ https://www.cve.org/CVERecord?id=CVE-2025-54920 https://issues.apache.org/jira/browse/SPARK-52381
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.
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.
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.
https://lists.apache.org/thread/60mgbswq2lsmrxykfxpqq13ztkm2ht6q http://www.openwall.com/lists/oss-security/2022/11/01/14
A stored cross-site scripting (XSS) flaw was found in Apache Spark. This issue allows an attacker to execute arbitrary JavaScript in the web browser of a user, including a malicious payload into the logs which are returned in logs rendered in the UI.
Apache Spark contains a command injection vulnerability via Spark User Interface (UI) when Access Control Lists (ACLs) are enabled.
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
Eclipse Jetty is vulnerable to a denial of service, caused by an error when handling a request containing multiple Accept headers with a large number of quality parameters. By sending a specially-crafted request, a remote attacker could exploit this vulnerability to exhaust minutes of CPU time.
Impact If GZIP request body inflation is enabled and requests from different clients are multiplexed onto a single connection and if an attacker can send a request with a body that is received entirely by not consumed by the application, then a subsequent request on the same connection will see that body prepended to it's body.
The attacker will not see any data, but may inject data into the body of the subsequent request
CVE score is 4.8 AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:L
Workarounds The problem can be worked around by either: - Disabling compressed request body inflation by GzipHandler. - By always fully consuming the request content before sending a response. - By adding a Connection: close to any response where the servlet does not fully consume request content.
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).
A flaw was found in Netty before version 4.1.44, where it accepted multiple Content-Length headers and also accepted both Transfer-Encoding, as well as Content-Length headers where it should reject the message under such circumstances. In circumstances where Netty is used in the context of a server, it could result in a viable HTTP smuggling vulnerability.
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.
A flaw was found in org.codehaus.jackson:jackson-mapper-asl:1.9.x libraries such that an XML external entity (XXE) vulnerability affects codehaus's jackson-mapper-asl libraries. This vulnerability is similar to CVE-2016-3720. The primary threat from this flaw is data integrity.
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.
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.
Spark's Apache Maven-based build includes a convenience script, 'build/mvn', that downloads and runs a zinc server to speed up compilation. It has been included in release branches since 1.3.x, up to and including master. 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.
From version 1.3.0 onward, Apache 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 2.4.x 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'.
In Apache Spark 1.0.0 to 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.
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.
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.
In Apache Spark before 2.2.0, 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.
A path traversal issue was found in Spark version 2.5 and potentially earlier versions. The vulnerability resides in the functionality to serve static files where there's no protection against directory traversal attacks. This could allow attackers access to private files including sensitive data.
External References:
http://seclists.org/fulldisclosure/2016/Nov/13