Spark
Vulnerabilities
29
Known exploited
1
Max CVSS
9.9
Top EPSS
0.92984
Severity breakdown
Critical
6
High
10
Medium
13
Low
0
Affected version ranges
1.0.2–1.6.31.3.0–2.2.31.3.0–2.4.02.0.0–3.3.42.1.0–2.1.2< 3.1.3< 3.2.2< 3.4.0< 3.4.4< 3.5.7≤ 2.1.1≤ 2.1.2≤ 2.4.5≤ 3.0.3
Also matched as (raw): spark,kafka,nifi,hive
Top vulnerabilities
CVE-2023-22946In 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.
BDU:2024-01784Уязвимость функции spark-submit фреймворка Apache Spark связана с небезопасным управлением привилегиями. Эксплуатация уязвимости может позволить нарушителю, действующему удаленно, выполнить произвольный код
CVE-2020-9480In 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).
CVE-2018-17190In 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.
BDU:2025-09903Уязвимость компонента Analytics Server программной платформы Oracle Business Intelligence Enterprise Edition связана с недостатками процедуры аутентификации. Эксплуатация уязвимости может позволить нарушителю, действующему удалённо, вызвать отказ в обслуживании
BDU:2020-00063Уязвимость фреймворка Apache Spark связана с недостатками процедуры аутентификации. Эксплуатация уязвимости может позволить нарушителю, действующему удалённо, оказать воздействие на конфиденциальность, целостность и доступность защищаемой информации
CVE-2025-54920This 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.
CVE-2023-32007** 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.
CVE-2022-33891The 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.
CVE-2017-12612In 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 2.2.0 or later.
CVE-2021-38296Apache 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
CVE-2019-20445HttpObjectDecoder.java in Netty before 4.1.44 allows a Content-Length header to be accompanied by a second Content-Length header, or by a Transfer-Encoding header.
CVE-2019-10099Prior 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.
CVE-2018-11804Spark'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.
BDU:2022-04514Уязвимость интерфейса фреймворка Apache Spark связана с недостаточной проверкой аргументов, передаваемых в команду. Эксплуатация уязвимости может позволить нарушителю, действующему удалённо, выполнить произвольную команду
CVE-2018-11760When 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.
CVE-2025-55039This 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.
CVE-2018-11770From 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 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'.
BDU:2026-04646Уязвимость функции spark-submit фреймворка Apache Spark связана с недостатками механизма десериализации. Эксплуатация уязвимости может позволить нарушителю, действующему удаленно, выполнять произвольные команды
CVE-2017-7678In 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.
CVE-2024-23945Signing 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
CVE-2019-10172A flaw was found in org.codehaus.jackson:jackson-mapper-asl:1.9.x libraries. XML external entity vulnerabilities similar CVE-2016-3720 also affects codehaus jackson-mapper-asl libraries but in different classes.
BDU:2025-00249Уязвимость класса CookieSigner фреймворка Apache Spark и СУБД Apache Hive связана с раскрытием цифровой подписи к данным cookie в результате некорректного механизма формирования отчетов об ошибках. Эксплуатация уязвимости может позволить нарушителю, действующему удалённо, получить несанкционированный доступ к защищаемой информации
BDU:2020-00135Уязвимость интерфейса PySpark фреймворка Apache Spark связана с недостатками разграничения доступа. Эксплуатация уязвимости может позволить нарушителю повысить свои привилегии
CVE-2022-31777A 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.