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Protection: None✕Product spark-core_2.11✕
Overview 6 matches · 0 protected · 1 exploited · 0 CISA KEV · 1 public PoC · 1 EPSS ≥ 0.5
6matches 0protected0.0% 1exploited16.7% 0CISA KEV0.0% 1public PoC16.7% 1EPSS ≥ 0.516.7%
Protected by Waratek 0 0.0% Rule in development 0 0.0% Mitigation candidate 1 16.7% No exploit published 5 83.3%
Critical 0 0.0% High 2 33.3% Medium 4 66.7% 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 6 100.0% not established 0 0.0% unrecorded 0 0.0%
Exploited 1 16.7% Working exploit published 0 0.0% Proof of concept only 0 0.0% Forecast only 0 0.0% No public exploit 5 83.3%
split by peak 2 / quarter
No exploit published: 5Mitigation candidate: 1 Medium: 4High: 2 not blocked: 6 No public exploit: 5Exploited: 1
Q3 171 No exploit published1 Medium1 not blocked1 No public exploit1 Q4 170 Q1 180 Q2 180 Q3 182 Mitigation candidate1 No exploit published1 Medium2 not blocked2 Exploited1 No public exploit1 Q4 181 No exploit published1 High1 not blocked1 No public exploit1 Q1 190 Q2 190 Q3 191 No exploit published1 High1 not blocked1 No public exploit1 Q4 190 Q1 200 Q2 200 Q3 200 Q4 200 Q1 210 Q2 210 Q3 210 Q4 210 Q1 220 Q2 220 Q3 220 Q4 221 No exploit published1 Medium1 not blocked1 No public exploit1
Q4 17Q2 18Q4 18Q2 19Q4 19Q2 20Q4 20Q2 21Q4 21Q2 22Q4 22
6 matches Sign in to download these CVEs as CSV or JSON, or to follow them in a feed.
CVE Severity Published▾ Status Exploitation Description
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-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-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.