Apache Spark Exposure Scanner

Apache Spark is widely used by data engineers and data scientists for processing large volumes of data. It's an open-source unified analytics engine for big data processing, with built-in modules for SQL, streaming, machine learning, and graph processing. The software is utilized in various industries, including finance, telecommunications, and healthcare, to perform complex data analyses and generate insights. Developers and analysts leverage Spark's ability to process data in-memory to achieve high performance for both batch and real-time processing. The system's web-based user interface facilitates monitoring and managing Spark applications, making it easier to optimize performance. Organizations benefit from Spark's scalability, allowing data processing tasks to be executed over large clusters seamlessly.

The vulnerability in question relates to the Exposure of Apache Spark's environment variables and application information through its Web UI. When left unsecured, this can lead to unauthorized access to sensitive data, such as configuration settings, application versions, and data about the runtime environment. This exposure typically occurs when explicit access controls are not implemented for the web interface. Consequently, attackers can gather crucial information, including the structure and parameters of Spark applications, which can then be exploited in a targeted attack. Ensuring that authentication measures are in place can help mitigate this risk and prevent unauthorized data access. Proper network segregation and configuration management are critical for protecting against this type of exposure.

The vulnerability details indicate that the exposed endpoint is accessible via HTTP GET requests, specifically targeting the URI paths '/api/v1/applications' and '/environment/'. This unprotected access allows leakages of environment-specific variables and configurations. Certain parameters, such as 'sparkProperties' and 'spark.app.name', become visible to unauthorized users. Additionally, available runtime information and Spark properties provide excessive insights into the operations and state of the Spark infrastructure. The parameter 'sparkUser' might reveal usernames used in the infrastructure, potentially facilitating further unauthorized endeavors. Such vulnerabilities necessitate employing robust access controls to safeguard sensitive data and prevent unauthorized exposure.

If successfully exploited, this vulnerability can lead to a host of security concerns. Unauthorized users might exploit the exposed information to craft and deploy more sophisticated attacks, leveraging the insights gained into the configuration and operational details of the Spark instances. Configuration leaks can result in data breaches, as sensitive information is made accessible to potential attackers. Furthermore, an adversary could potentially disrupt Spark operations, leading to denial of service scenarios either by overwhelming the system or by manipulating runtime parameters. These scenarios could significantly disrupt business operations, incur financial losses, and damage an organization's reputation.

REFERENCES

• https://spark.apache.org/docs/latest/monitoring.html