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Vuln ID | Summary | CVSS Severity |
---|---|---|
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. Published: May 02, 2023; 5:15:10 AM -0400 |
V4.0:(not available) V3.1: 8.8 HIGH V2.0:(not available) |
CVE-2023-22946 |
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. Published: April 17, 2023; 4:15:07 AM -0400 |
V4.0:(not available) V3.1: 9.9 CRITICAL V2.0:(not available) |
CVE-2022-31777 |
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. Published: November 01, 2022; 12:15:13 PM -0400 |
V4.0:(not available) V3.1: 5.4 MEDIUM V2.0:(not available) |
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. Published: July 18, 2022; 3:15:07 AM -0400 |
V4.0:(not available) V3.1: 8.8 HIGH V2.0:(not available) |
CVE-2021-38296 |
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 Published: March 10, 2022; 4:15:07 AM -0500 |
V4.0:(not available) V3.1: 7.5 HIGH V2.0: 5.0 MEDIUM |
CVE-2020-9480 |
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). Published: June 23, 2020; 6:15:14 PM -0400 |
V4.0:(not available) V3.1: 9.8 CRITICAL V2.0: 9.3 HIGH |
CVE-2019-10099 |
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. Published: August 07, 2019; 1:15:12 PM -0400 |
V4.0:(not available) V3.1: 7.5 HIGH V2.0: 4.3 MEDIUM |
CVE-2018-11760 |
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. Published: February 04, 2019; 12:29:00 PM -0500 |
V4.0:(not available) V3.0: 5.5 MEDIUM V2.0: 2.1 LOW |
CVE-2018-17190 |
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. Published: November 19, 2018; 9:29:00 AM -0500 |
V4.0:(not available) V3.0: 9.8 CRITICAL V2.0: 7.5 HIGH |
CVE-2018-1334 |
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. Published: July 12, 2018; 9:29:00 AM -0400 |
V4.0:(not available) V3.0: 4.7 MEDIUM V2.0: 1.9 LOW |
CVE-2017-7678 |
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. Published: July 12, 2017; 9:29:00 AM -0400 |
V4.0:(not available) V3.0: 6.1 MEDIUM V2.0: 4.3 MEDIUM |