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Medium Org.apache.spark Spark-core CVEs

5 CVEs, 1 exploited

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Vendor org.apache.spark✕Product spark-core✕
Overview 5 matches · 0 protected · 1 exploited · 0 CISA KEV · 1 public PoC · 1 EPSS ≥ 0.5
5matches 0protected0.0% 1exploited20.0% 0CISA KEV0.0% 1public PoC20.0% 1EPSS ≥ 0.520.0%
Protected by Waratek 0 0.0% Rule in development 0 0.0% Mitigation candidate 1 20.0% No exploit published 4 80.0%
Critical 0 0.0% High 0 0.0% Medium 5 100.0% Low 0 0.0% None 0 0.0% Unknown 0 0.0%
blocked by ARMR today 0 0.0% partly blocked 0 0.0% not blocked 5 100.0% not established 0 0.0% unrecorded 0 0.0%
Exploited 1 20.0% Working exploit published 0 0.0% Proof of concept only 0 0.0% Forecast only 0 0.0% No public exploit 4 80.0%
split by peak 2 / year
No exploit published: 4Mitigation candidate: 1 Medium: 5 not blocked: 5 No public exploit: 4Exploited: 1
20171 No exploit published1 Medium1 not blocked1 No public exploit1 20182 Mitigation candidate1 No exploit published1 Medium2 not blocked2 Exploited1 No public exploit1 20190 20200 20210 20221 No exploit published1 Medium1 not blocked1 No public exploit1 20230 20240 20250 20261 No exploit published1 Medium1 not blocked1 No public exploit1
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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-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-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-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.