threat-modeling.md

Threat Model Analysis for apache/spark

Threat: Arbitrary Code Execution via Malicious Serialized Object in UDF

Description: An attacker crafts a malicious serialized Java object and passes it as input to a User-Defined Function (UDF). When Spark deserializes this object on an Executor, it triggers the execution of arbitrary code contained within the malicious object. This is possible if the UDF processes data from an untrusted source without proper validation, and Spark's default Java serialization is used. Impact: Complete compromise of the Executor, allowing the attacker to steal data, modify processing results, launch further attacks within the cluster, or disrupt the Spark application. Potentially leads to full cluster compromise. Affected Component: Executor (specifically, the deserialization process within UDF execution). Also affects any downstream components that rely on the output of the compromised Executor. Risk Severity: Critical Mitigation Strategies: * Avoid Java Serialization: Strongly prefer alternative serialization formats like JSON, Avro, or Protocol Buffers, which are less susceptible to deserialization vulnerabilities. * Input Validation: Implement rigorous input validation before any data is passed to a UDF. This includes type checking, length limits, and whitelisting allowed values. * Sandboxing (if feasible): Explore sandboxing techniques for UDF execution, although this can be complex and may impact performance. * Security Manager: Use a Java Security Manager with a restrictive policy to limit the permissions of deserialized code. This is a defense-in-depth measure. * Code Review: Thoroughly review all UDF code for potential security vulnerabilities. * Dependency Management: Keep all dependencies, including those used within UDFs, up-to-date to address known vulnerabilities.

Threat: Data Exfiltration via Unencrypted Shuffle Data

Description: An attacker with network access (e.g., on the same network segment as the Spark cluster) sniffs network traffic between Executors during the shuffle phase. If shuffle data is not encrypted, the attacker can capture sensitive data being exchanged. This directly exploits Spark's data transfer mechanism. Impact: Leakage of sensitive data processed by the Spark application, potentially violating privacy regulations or exposing confidential business information. Affected Component: Spark's shuffle service (communication between Executors). Risk Severity: High Mitigation Strategies: * Enable Spark Shuffle Encryption: Configure Spark to encrypt shuffle data using spark.shuffle.encryption.enabled=true. This requires setting up a shared secret or using a key provider. * Network Segmentation: Isolate the Spark cluster on a dedicated network segment with restricted access. While this is a general network security practice, it's crucial for mitigating this Spark-specific threat. * TLS for RPC: Enable TLS encryption for all Spark RPC communication (which includes shuffle data transfer) using spark.ssl.enabled=true and related configuration options.

Threat: Denial of Service via Resource Exhaustion on Driver

Description: An attacker submits a specially crafted Spark job (or a large number of jobs) designed to consume excessive resources (CPU, memory) on the Driver node. This could involve a job that performs a very large collect() operation, pulling a massive amount of data from Executors to the Driver, exploiting Spark's data aggregation capabilities. Impact: The Driver becomes unresponsive, halting all Spark processing and causing the application to fail. This disrupts the availability of the Spark application. Affected Component: Spark Driver. Risk Severity: High Mitigation Strategies: * Resource Limits: Set resource limits (CPU, memory) for the Driver process using the cluster manager's capabilities (e.g., YARN, Kubernetes). * spark.driver.maxResultSize: Limit the size of results that can be returned to the Driver using the spark.driver.maxResultSize configuration property. This is a direct Spark configuration to mitigate this. * Job Submission Quotas: Implement quotas on the number of jobs or the amount of resources that a user or application can consume (often managed by the cluster manager, but directly impacting Spark). * Monitoring: Monitor the Driver's resource usage and set up alerts for unusual activity. * Rate Limiting: If the Driver exposes an API for job submission, implement rate limiting to prevent an attacker from flooding the Driver with requests.

Threat: Unauthorized Job Submission via Unsecured Spark UI

Description: If the Spark UI is exposed without authentication and authorization, an attacker can access the UI and submit arbitrary Spark jobs. This directly exploits the Spark UI, a core component of Spark. Impact: Unauthorized code execution, data access, and potential cluster compromise, depending on the privileges of the Spark application. Affected Component: Spark UI (and potentially the entire Spark cluster). Risk Severity: High Mitigation Strategies: * Enable Authentication: Configure authentication for the Spark UI using Spark's built-in authentication mechanisms (e.g., shared secret, Kerberos) or by integrating with an external authentication provider. This is a direct Spark UI configuration. * Authorization: Implement authorization policies to control which users can access the UI and submit jobs. This can be done using Spark's ACLs (Access Control Lists) or by integrating with a more comprehensive authorization system. * Network Access Control: Restrict access to the Spark UI to authorized networks or IP addresses using firewalls or network security groups. * Reverse Proxy: Place the Spark UI behind a reverse proxy (e.g., Nginx, Apache) that handles authentication and authorization.

Threat: Credential Exposure via Spark Event Logs

Description: Spark event logs, if not properly secured, may contain sensitive information such as access keys, passwords, or other credentials used to connect to external data sources. An attacker gaining access to these logs could extract these credentials. This is a direct vulnerability of how Spark logs information. Impact: Compromise of credentials, leading to unauthorized access to external data sources. Affected Component: Spark History Server, Spark event logs (stored on the filesystem or in a database). Risk Severity: High Mitigation Strategies: * Redaction: Configure Spark to redact sensitive information from event logs using spark.redaction.regex. This allows you to define regular expressions to match and replace sensitive patterns. This is a direct Spark configuration. * Secure Storage: Store event logs in a secure location with restricted access (e.g., encrypted storage, access control lists). * Short Retention: Configure a short retention period for event logs to minimize the window of exposure. * Avoid Logging Credentials: Never hardcode credentials in Spark configurations or code. Use secure methods for managing credentials, such as environment variables or a secrets management system. * History Server Security: Secure the Spark History Server with authentication and authorization, similar to the Spark UI.

Threat: Unauthorized Access to Data via Misconfigured Spark Permissions (when interacting with HDFS/object storage)

Description: While the underlying storage (HDFS, S3, etc.) has its own permissions, Spark's interaction with it can be misconfigured. If Spark is configured to use overly permissive credentials or impersonation settings, it could allow unauthorized access to data, even if the underlying storage has some level of protection. This is about how Spark itself is configured to access the data. Impact: Data leakage or data tampering. Affected Component: Spark's connection/interaction with the data source (e.g., how Spark uses Hadoop delegation tokens, or how it authenticates to cloud storage). Risk Severity: High Mitigation Strategies: * Principle of Least Privilege (Spark Side): Ensure Spark is configured to use credentials/identities that have only the minimum necessary permissions on the data source. This is about the Spark configuration, not just the data source configuration. * Proper Impersonation: If using user impersonation, ensure it's correctly configured and restricted to authorized users. * Credential Management: Use secure methods for managing the credentials Spark uses to access the data source (secrets management, not hardcoding). * Review Spark Configuration: Carefully review Spark configuration related to data source access (e.g., spark.hadoop.* properties).