Apache Spark Exposure Scanner
This scanner detects the use of Apache Spark Exposure in digital assets. It identifies the presence of exposed PySparkShell Application UI by Apache Spark accessible over the internet, which can leak sensitive job and cluster information.
Short Info
Level
Single Scan
Single Scan
Can be used by
Asset Owner
Estimated Time
10 seconds
Time Interval
11 days 1 hour
Scan only one
URL
Toolbox
Apache Spark is a powerful open-source unified analytics engine designed to handle large-scale data processing. Widely adopted in industries like finance, healthcare, and tech, it facilitates big data processing with ease. Apache Spark supports multiple programming languages, making it versatile for developers. It is typically deployed in distributed computing environments to process huge datasets efficiently. Enterprises use Spark for data processing, machine learning, and real-time data analysis. Apache Spark's application UI offers visual insights into job execution and cluster management.
The vulnerability detected by the scanner involves the exposure of the PySparkShell Application UI by Apache Spark, which is accessible remotely. This exposure can occur when the application UI is not properly secured, allowing unauthorized access. Exposing the UI can lead to the leakage of sensitive information about ongoing jobs and the configuration of the Spark cluster. The scanner identifies such exposures to prevent unauthorized individuals from gaining insights into the job data or cluster operations. Proper configuration and restricted access are crucial to mitigate this vulnerability.
The vulnerability arises from an exposed endpoint at port 4040 that hosts the PySparkShell Application UI. Attackers can exploit this if the UI is accessible without authentication controls. The target path is often "/jobs/" or the specific port URL ":4040/jobs/". If accessed, the UI may display details such as job performance metrics, executed task information, and cluster configuration data. These details are useful for maintenance and debugging but should remain limited to authorized personnel.
Exploiting the exposed Apache Spark UI can result in sensitive information disclosure. Unauthorized users may gain insights into job processing, current workloads, and cluster configurations, potentially leading to information misuse. Malicious actors could use this information to further compromise the system or plan targeted attacks. The exposure also increases the risk of unauthorized changes or interruptions to ongoing data processing activities.
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