Spark-core CVEs
13 CVEs, 1 exploited, 4 protected by Waratek
Overview 13 matches · 4 protected · 1 exploited · 0 CISA KEV · 1 public PoC · 1 EPSS ≥ 0.5
Protected by Waratek
4
30.8%
Rule in development
0
0.0%
Mitigation candidate
1
7.7%
No exploit published
8
61.5%
blocked by ARMR today
4
30.8%
partly blocked
0
0.0%
not blocked
9
69.2%
not established
0
0.0%
unrecorded
0
0.0%
Exploited
1
7.7%
Working exploit published
0
0.0%
Proof of concept only
0
0.0%
Forecast only
0
0.0%
No public exploit
12
92.3%
split by
peak 4 / year
20161
Protected by Waratek1
High1
blocked by ARMR today1
No public exploit1
20172
Protected by Waratek1
No exploit published1
High1
Medium1
blocked by ARMR today1
not blocked1
No public exploit2
20184
Protected by Waratek1
Mitigation candidate1
No exploit published2
High1
Medium3
blocked by ARMR today1
not blocked3
Exploited1
No public exploit3
20191
No exploit published1
High1
not blocked1
No public exploit1
20200
20210
20222
No exploit published2
High1
Medium1
not blocked2
No public exploit2
20231
No exploit published1
Critical1
not blocked1
No public exploit1
20240
20250
20262
Protected by Waratek1
No exploit published1
High1
Medium1
blocked by ARMR today1
not blocked1
No public exploit2
20162017201820192020202120222023202420252026
| CVE | Severity | Published▾ | Status | Exploitation | Description |
|---|---|---|---|---|---|
| CVE-2026-32773 | Medium | 2026-09-02 | No exploit published | No public exploit | 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. |
| CVE-2025-54920 | High | 2026-03-16 | Protected by Waratek | No public exploit | 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. |
| CVE-2023-22946 | Critical | 2023-04-17 | No exploit published | No public exploit | 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. |
| CVE-2022-31777 | Medium | 2022-11-01 | No exploit published | No public exploit | 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. |
| CVE-2021-38296 | High | 2022-03-10 | No exploit published | No public exploit | 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 |
| CVE-2019-10099 | High | 2019-08-07 | No exploit published | No public exploit | 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. |
| CVE-2018-11804 | High | 2018-10-24 | No exploit published | No public exploit | 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. |
| CVE-2018-11770 | Medium | 2018-08-13 | Mitigation candidate | Exploited | 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 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'. |
| CVE-2018-1334 | Medium | 2018-07-12 | No exploit published | No public exploit | 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. |
| CVE-2018-9159 | Medium | 2018-03-31 | Protected by Waratek | No public exploit | In Spark before 2.7.2, a remote attacker can read unintended static files via various representations of absolute or relative pathnames, as demonstrated by file: URLs and directory traversal sequences. NOTE: this product is unrelated to Ignite Realtime Spark. |
| CVE-2017-12612 | High | 2017-09-13 | Protected by Waratek | No public exploit | 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 2.2.0 or later. |
| CVE-2017-7678 | Medium | 2017-07-12 | No exploit published | No public exploit | 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. |
| CVE-2016-9177 | High | 2016-11-04 | Protected by Waratek | No public exploit | Directory traversal vulnerability in Spark 2.5 allows remote attackers to read arbitrary files via a .. (dot dot) in the URI. |