core_2.11 CVEs | Waratek

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Overview 17 matches · 15 in Waratek scope · 2 protected · 2 exploited · 1 CISA KEV · 4 public PoC · 2 EPSS ≥ 0.5

17matches 15in Waratek scope88.2% 2protected11.8% 2exploited11.8% 1CISA KEV5.9% 4public PoC23.5% 2EPSS ≥ 0.511.8%

Protected by Waratek 2 11.8% Rule in development 0 0.0% Mitigation candidate 3 17.6% No exploit published 10 58.8% Outside Waratek scope 0 0.0% Not applicable 2 11.8% Withdrawn CVE 0 0.0%

Critical 1 5.9% High 11 64.7% Medium 5 29.4% Low 0 0.0% None 0 0.0% Unknown 0 0.0%

blocked by ARMR today 2 11.8% partly blocked 0 0.0% not blocked 15 88.2% not established 0 0.0% unrecorded 0 0.0%

Exploited 2 11.8% Working exploit published 2 11.8% Proof of concept only 1 5.9% Forecast only 0 0.0% No public exploit 12 70.6%

split by StatusSeverityProtectionExploit maturity peak 6 / year

20173 Protected by Waratek1 No exploit published2 High2 Medium1 blocked by ARMR today1 not blocked2 No public exploit3 20186 Mitigation candidate1 No exploit published3 Not applicable2 Critical1 High2 Medium3 not blocked6 Exploited1 Working exploit published1 No public exploit4 20191 No exploit published1 High1 not blocked1 No public exploit1 20201 No exploit published1 High1 not blocked1 No public exploit1 20212 Mitigation candidate1 No exploit published1 High2 not blocked2 Working exploit published1 No public exploit1 20221 No exploit published1 Medium1 not blocked1 No public exploit1 20232 Mitigation candidate1 No exploit published1 High2 not blocked2 Exploited1 Proof of concept only1 20240 20250 20261 Protected by Waratek1 High1 blocked by ARMR today1 No public exploit1

2017201820192020202120222023202420252026

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CVE	Severity	Published▾	Status	Exploitation	Description
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-2023-44487	High	2023-10-10	Mitigation candidate	Exploited	The HTTP/2 protocol allows a denial of service (server resource consumption) because request cancellation can reset many streams quickly, as exploited in the wild in August through October 2023.
CVE-2023-22465	High	2023-01-04	No exploit published	Proof of concept only	Http4s is a Scala interface for HTTP services. Starting with version 0.1.0 and prior to versions 0.21.34, 0.22.15, 0.23.17, and 1.0.0-M38, the `User-Agent` and `Server` header parsers are susceptible to a fatal error on certain inputs. In http4s, modeled headers are lazily parsed, so this only applies to services that explicitly request these typed headers. Fixes are released in 0.21.34, 0.22.15, 0.23.17, and 1.0.0-M38. As a workaround, use the weakly typed header interface.
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-2021-42697	High	2021-11-02	Mitigation candidate	Working exploit published	Akka HTTP 10.1.x before 10.1.15 and 10.2.x before 10.2.7 can encounter stack exhaustion while parsing HTTP headers, which allows a remote attacker to conduct a Denial of Service attack by sending a User-Agent header with deeply nested comments.
CVE-2021-21293	High	2021-02-02	No exploit published	No public exploit	blaze is a Scala library for building asynchronous pipelines, with a focus on network IO. All servers running blaze-core before version 0.14.15 are affected by a vulnerability in which unbounded connection acceptance leads to file handle exhaustion. Blaze, accepts connections unconditionally on a dedicated thread pool. This has the net effect of amplifying degradation in services that are unable to handle their current request load, since incoming connections are still accepted and added to an unbounded queue. Each connection allocates a socket handle, which drains a scarce OS resource. This can also confound higher level circuit breakers which work based on detecting failed connections. The vast majority of affected users are using it as part of http4s-blaze-server <= 0.21.16. http4s provides a mechanism for limiting open connections, but is enforced inside the Blaze accept loop, after the connection is accepted and the socket opened. Thus, the limit only prevents the number of connections which can be simultaneously processed, not the number of connections which can be held open. The issue is fixed in version 0.14.15 for "NIO1SocketServerGroup". A "maxConnections" parameter is added, with a default value of 512. Concurrent connections beyond this limit are rejected. To run unbounded, which is not recommended, set a negative number. The "NIO2SocketServerGroup" has no such setting and is now deprecated. There are several possible workarounds described in the refrenced GitHub Advisory GHSA-xmw9-q7x9-j5qc.
CVE-2020-7780	High	2020-11-27	No exploit published	No public exploit	This affects the package com.softwaremill.akka-http-session:core_2.13 before 0.5.11; the package com.softwaremill.akka-http-session:core_2.12 before 0.5.11; the package com.softwaremill.akka-http-session:core_2.11 before 0.5.11. For older versions, endpoints protected by randomTokenCsrfProtection could be bypassed with an empty X-XSRF-TOKEN header and an empty XSRF-TOKEN cookie.
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-2018-17190	Critical	2018-11-19	Not applicable	No public exploit	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.
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-2018-16131	High	2018-08-30	No exploit published	No public exploit	The decodeRequest and decodeRequestWith directives in Lightbend Akka HTTP 10.1.x through 10.1.4 and 10.0.x through 10.0.13 allow remote attackers to cause a denial of service (memory consumption and daemon crash) via a ZIP bomb.
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-2018-8024	Medium	2018-07-12	Not applicable	Working exploit published	In Apache Spark 2.1.0 to 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, it's possible for a malicious user to construct a URL pointing to a Spark cluster's UI's job and stage info pages, and if a user can be tricked into accessing the URL, can be used to cause script to execute and expose information from the user's view of the Spark UI. While some browsers like recent versions of Chrome and Safari are able to block this type of attack, current versions of Firefox (and possibly others) do not.
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-2017-1000118	High	2017-10-05	No exploit published	No public exploit	Akka HTTP versions <= 10.0.5 Illegal Media Range in Accept Header Causes StackOverflowError Leading to Denial of Service
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

Severity

Published▾

Status

Exploitation

Description

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-2023-44487

High

2023-10-10

Mitigation candidate

Exploited

The HTTP/2 protocol allows a denial of service (server resource consumption) because request cancellation can reset many streams quickly, as exploited in the wild in August through October 2023.

CVE-2023-22465

High

2023-01-04

No exploit published

Proof of concept only

Http4s is a Scala interface for HTTP services. Starting with version 0.1.0 and prior to versions 0.21.34, 0.22.15, 0.23.17, and 1.0.0-M38, the `User-Agent` and `Server` header parsers are susceptible to a fatal error on certain inputs. In http4s, modeled headers are lazily parsed, so this only applies to services that explicitly request these typed headers. Fixes are released in 0.21.34, 0.22.15, 0.23.17, and 1.0.0-M38. As a workaround, use the weakly typed header interface.

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-2021-42697

High

2021-11-02

Mitigation candidate

Working exploit published

Akka HTTP 10.1.x before 10.1.15 and 10.2.x before 10.2.7 can encounter stack exhaustion while parsing HTTP headers, which allows a remote attacker to conduct a Denial of Service attack by sending a User-Agent header with deeply nested comments.

CVE-2021-21293

High

2021-02-02

No exploit published

No public exploit

blaze is a Scala library for building asynchronous pipelines, with a focus on network IO. All servers running blaze-core before version 0.14.15 are affected by a vulnerability in which unbounded connection acceptance leads to file handle exhaustion. Blaze, accepts connections unconditionally on a dedicated thread pool. This has the net effect of amplifying degradation in services that are unable to handle their current request load, since incoming connections are still accepted and added to an unbounded queue. Each connection allocates a socket handle, which drains a scarce OS resource. This can also confound higher level circuit breakers which work based on detecting failed connections. The vast majority of affected users are using it as part of http4s-blaze-server <= 0.21.16. http4s provides a mechanism for limiting open connections, but is enforced inside the Blaze accept loop, after the connection is accepted and the socket opened. Thus, the limit only prevents the number of connections which can be simultaneously processed, not the number of connections which can be held open. The issue is fixed in version 0.14.15 for "NIO1SocketServerGroup". A "maxConnections" parameter is added, with a default value of 512. Concurrent connections beyond this limit are rejected. To run unbounded, which is not recommended, set a negative number. The "NIO2SocketServerGroup" has no such setting and is now deprecated. There are several possible workarounds described in the refrenced GitHub Advisory GHSA-xmw9-q7x9-j5qc.

CVE-2020-7780

High

2020-11-27

No exploit published

No public exploit

This affects the package com.softwaremill.akka-http-session:core_2.13 before 0.5.11; the package com.softwaremill.akka-http-session:core_2.12 before 0.5.11; the package com.softwaremill.akka-http-session:core_2.11 before 0.5.11. For older versions, endpoints protected by randomTokenCsrfProtection could be bypassed with an empty X-XSRF-TOKEN header and an empty XSRF-TOKEN cookie.

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-2018-17190

Critical

2018-11-19

Not applicable

No public exploit

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.

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-2018-16131

High

2018-08-30

No exploit published

No public exploit

The decodeRequest and decodeRequestWith directives in Lightbend Akka HTTP 10.1.x through 10.1.4 and 10.0.x through 10.0.13 allow remote attackers to cause a denial of service (memory consumption and daemon crash) via a ZIP bomb.

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-2018-8024

Medium

2018-07-12

Not applicable

Working exploit published

In Apache Spark 2.1.0 to 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, it's possible for a malicious user to construct a URL pointing to a Spark cluster's UI's job and stage info pages, and if a user can be tricked into accessing the URL, can be used to cause script to execute and expose information from the user's view of the Spark UI. While some browsers like recent versions of Chrome and Safari are able to block this type of attack, current versions of Firefox (and possibly others) do not.

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-2017-1000118

High

2017-10-05

No exploit published

No public exploit

Akka HTTP versions <= 10.0.5 Illegal Media Range in Accept Header Causes StackOverflowError Leading to Denial of Service

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.