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Apache Spark uses the standard process outlined by the Apache Security Team for reporting vulnerabilities. Note that vulnerabilities should not be publicly disclosed until the project has responded.
To report a possible security vulnerability, please email security@apache.org. This is a
non-public list that will reach the Apache Security team, as well as the Spark PMC.
Severity: Medium
Vendor: The Apache Software Foundation
Versions Affected:
- Spark versions from 1.3.0, running standalone master with REST API enabled, or running Mesos master with cluster mode enabled
Description:
From version 1.3.0 onward, 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.
Mitigation:
For standalone masters, disable the REST API by setting spark.master.rest.enabled to false if it is unused,
and/or ensure that all network access to the REST API (port 6066 by default) is restricted to hosts that are
trusted to submit jobs. Mesos users can stop the MesosClusterDispatcher, though that will prevent them
from running jobs in cluster mode. Alternatively, they can ensure access to the MesosRestSubmissionServer
(port 7077 by default) is restricted to trusted hosts.
Credit:
- Imran Rashid, Cloudera
- Fengwei Zhang, Alibaba Cloud Security Team
Severity: Medium
Versions Affected:
- Spark 2.1.0 through 2.1.2
- Spark 2.2.0 through 2.2.1
- Spark 2.3.0
Description:
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.
Mitigation:
- 2.1.x users should upgrade to 2.1.3 or newer
- 2.2.x users should upgrade to 2.2.2 or newer
- 2.3.x users should upgrade to 2.3.1 or newer
Credit:
- Spencer Gietzen, Rhino Security Labs
Severity: High
Vendor: The Apache Software Foundation
Versions affected:
- Spark versions through 2.1.2
- Spark 2.2.0 to 2.2.1
- Spark 2.3.0
Description:
In Apache Spark up to and including 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.
Mitigation:
- 1.x, 2.0.x, and 2.1.x users should upgrade to 2.1.3 or newer
- 2.2.x users should upgrade to 2.2.2 or newer
- 2.3.x users should upgrade to 2.3.1 or newer
- Otherwise, affected users should avoid using PySpark and SparkR in multi-user environments.
Credit:
- Nehmé Tohmé, Cloudera, Inc.
JIRA: SPARK-20922
Severity: Medium
Vendor: The Apache Software Foundation
Versions Affected:
- Versions of Apache Spark from 1.6.0 until 2.1.1
Description:
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 2.1.2, 2.2.0 or later.
Mitigation:
Update to Apache Spark 2.1.2, 2.2.0 or later.
Credit:
- Aditya Sharad, Semmle
JIRA: SPARK-20393
Severity: Medium
Vendor: The Apache Software Foundation
Versions Affected:
- Versions of Apache Spark before 2.1.2, 2.2.0
Description:
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.
Mitigation:
Update to Apache Spark 2.1.2, 2.2.0 or later.
Example:
Request:
GET /app/?appId=Content-Type:%20multipart/related;%20boundary=_AppScan%0d%0a--
_AppScan%0d%0aContent-Location:foo%0d%0aContent-Transfer-
Encoding:base64%0d%0a%0d%0aPGh0bWw%2bPHNjcmlwdD5hbGVydCgiWFNTIik8L3NjcmlwdD48L2h0bWw%2b%0d%0a
HTTP/1.1
Excerpt from response:
<div class="row-fluid">No running application with ID Content-Type: multipart/related;
boundary=_AppScan
--_AppScan
Content-Location:foo
Content-Transfer-Encoding:base64
PGh0bWw+PHNjcmlwdD5hbGVydCgiWFNTIik8L3NjcmlwdD48L2h0bWw+
</div>
Result: In the above payload the BASE64 data decodes as:
<html><script>alert("XSS")</script></html>
Credit:
- Mike Kasper, Nicholas Marion
- IBM z Systems Center for Secure Engineering