According to the latest SOC Prime’s Detection as Code Innovation report, proactive detection of vulnerability exploitation remains one of the top 3 security use cases throughout 2021-2022, which resonates with a growing number of revealed vulnerabilities affecting open-source products. The cybersecurity researcher has recently revealed a new vulnerability in Apache Spark, an open-source unified analytics engine for large-scale data processing. The newly discovered vulnerability is tracked as CVE-2022-33891 with the proof-of-concept (PoC) exploit already available on GitHub. On July 18, 2022, Apache Spark issued the security bulletin detailing this vulnerability, which is considered critical. The revealed flaw affects Apache Spark versions 3.0.3 and earlier, enabling attackers to execute an arbitrary shell command.
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Apache Spark provides high-level APIs in multiple programming languages, including Scala, Java, and Python. Additionally, it supports a variety of high-level toolings, such as Spark SQL for SQL and DataFrames, MLlib for machine learning, and more.
The recently-revealed flaw in Apache Spark (CVE-2022-33891) was reported by Kostya Kortchinsky, the cybersecurity researcher from Databricks. This flaw with a critical severity rating enables adversaries to perform arbitrary shell command execution as a current Spark user. The security issue stems from the Spark UI ability to enable Active Control Lists (ACLs) via the sparks.acls.enable option. In case ACLs are enabled, a HttpSecurityFilter code path provides the ability to impersonate by serving an arbitrary user name. In case of success, an adversary can reach a permission check function to launch a Unix shell command. This will eventually result in arbitrary shell command execution. Since the PoC exploit is already available via GitHub, Spark users are urged to upgrade their instances as soon as possible.
The glitch impacts the Apache Spark version 3.0.3 and earlier, as well as 3.1.1 to 3.1.2 and 3.2.0 to 3.2.1. To ensure your instances are protected from possible exploitation attempts, it is highly recommended to upgrade to Apache Spark 3.1.3, 3.2.2, or 3.3.0 maintenance release.
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