threat-modeling.md

Threat Model Analysis for apache/spark

Threat: Deserialization of Untrusted Data leading to Remote Code Execution (RCE)

• Description: An attacker crafts a malicious serialized object and injects it into the Spark application's data stream. When Spark workers or the driver deserialize this object, it executes arbitrary code defined by the attacker. This could be achieved by intercepting network traffic, exploiting vulnerable APIs that accept serialized data, or through compromised data sources.
• Impact: Remote Code Execution on Spark executors and/or driver. This allows the attacker to gain full control of the affected Spark nodes, potentially leading to data breaches, data manipulation, denial of service, and lateral movement within the infrastructure.
• Affected Spark Component: Spark Core (Serialization/Deserialization mechanisms, RPC framework), Spark Executors, Spark Driver.
• Risk Severity: Critical
• Mitigation Strategies:
  • Use Kryo serialization instead of Java serialization.
  • Implement strict input validation and sanitization for all data being deserialized.
  • Restrict network access to Spark endpoints handling serialized data to trusted sources.
  • Keep Spark and Java versions updated with security patches.
  • Consider alternative data formats like JSON or Avro for data exchange.
  • Implement object whitelisting for deserialization if using Java serialization is unavoidable.

Threat: Unsecured Spark UI leading to Information Disclosure

• Description: An attacker gains unauthorized access to the Spark UI, which is exposed without authentication or proper network restrictions. The attacker can then browse the UI to gather sensitive information about the Spark cluster configuration, application details, environment variables (potentially containing credentials), job execution plans, and data lineage.
• Impact: Information Disclosure of sensitive data, cluster configuration details, application logic, and potentially credentials. This information can be used to plan further attacks, gain deeper insights into the application, or directly access sensitive data.
• Affected Spark Component: Spark UI (Web UI component).
• Risk Severity: High
• Mitigation Strategies:
  • Enable authentication for the Spark UI (e.g., HTTP Basic Authentication, Kerberos).
  • Restrict network access to the Spark UI to trusted networks or users using firewalls and network policies.
  • Disable the Spark UI in production environments if not actively required.
  • Redact sensitive information from logs and environment variables displayed in the UI.
  • Regularly audit the information exposed by the Spark UI and minimize data leakage.

Threat: Unauthorized Job Submission leading to Resource Abuse or Malicious Code Execution

• Description: An attacker, without proper authorization, submits a Spark job to the cluster. This could be achieved by exploiting unsecured job submission endpoints (e.g., Livy without authentication, direct SparkContext access without authorization), or by compromising credentials of authorized users. The malicious job could be designed to consume excessive resources, causing denial of service, or to execute malicious code within the cluster.
• Impact: Denial of Service (DoS) due to resource exhaustion, execution of malicious code within the Spark cluster, unauthorized access to data processed by the job, data corruption, and potential compromise of the Spark cluster's resources.
• Affected Spark Component: Spark Submit, Livy (if used), Spark Master, Cluster Manager (YARN, Kubernetes, Standalone).
• Risk Severity: High
• Mitigation Strategies:
  • Implement strong authentication and authorization for job submission mechanisms (e.g., Livy with authentication, secure SparkContext configuration).
  • Restrict access to job submission ports and endpoints to authorized users and systems only.
  • Implement resource quotas and limits for submitted jobs to prevent resource exhaustion.
  • Validate and sanitize job parameters and configurations to prevent injection attacks.
  • Use secure cluster managers like YARN or Kubernetes with built-in security features.

Threat: Malicious User-Defined Functions (UDFs) or Code in Jobs

• Description: An attacker injects malicious code into a Spark job, often through User-Defined Functions (UDFs) or by manipulating job parameters to include malicious scripts. When the job is executed, this malicious code runs within the Spark executors, potentially gaining access to data, system resources, or allowing for further attacks. This could be achieved by exploiting vulnerabilities in job submission processes or by social engineering to get malicious jobs submitted.
• Impact: Execution of arbitrary code within Spark executors, leading to data breaches, system compromise, resource abuse, potential elevation of privileges within the Spark environment, and data corruption.
• Affected Spark Component: Spark SQL (UDF execution), Spark Core (Job execution), Spark Executors.
• Risk Severity: High
• Mitigation Strategies:
  • Implement strict code review processes for all user-submitted code, especially UDFs.
  • Restrict the capabilities of UDFs and user-submitted code to the minimum necessary.
  • Consider sandboxing or containerization to isolate the execution of user-submitted code.
  • Implement input validation and sanitization within UDFs to prevent injection vulnerabilities.
  • Employ static code analysis tools to detect potential security vulnerabilities in user-submitted code.
  • Principle of least privilege for job execution permissions.

Threat: Injection Attacks through Data Source Interactions

• Description: An attacker exploits vulnerabilities in how Spark applications interact with external data sources. This could involve SQL injection if Spark queries are constructed using unsanitized user input when accessing databases, command injection if Spark interacts with shell commands based on external data, or path traversal if file paths are constructed insecurely. The attacker manipulates input data or parameters to inject malicious commands or queries that are then executed by the data source.
• Impact: Data breaches from external databases, unauthorized access to file systems, command execution on backend systems, potential compromise of external data sources, and data corruption in external systems.
• Affected Spark Component: Spark SQL (Data source connectors, JDBC), Spark Core (File system interactions).
• Risk Severity: High
• Mitigation Strategies:
  • Apply input validation and sanitization to all data received from external sources before using it in Spark operations.
  • Use parameterized queries or prepared statements when interacting with databases to prevent SQL injection.
  • Follow secure coding practices when constructing file paths and commands based on external input to prevent path traversal and command injection.
  • Implement proper authentication and authorization when connecting to external data sources.
  • Minimize the privileges granted to Spark applications when accessing external data sources.

Threat: Exploitation of Vulnerabilities in Spark Dependencies

• Description: An attacker identifies and exploits known security vulnerabilities in third-party libraries and dependencies used by Spark or the Spark application. This could be achieved by targeting publicly known vulnerabilities in outdated dependencies or by discovering zero-day vulnerabilities. Exploitation can occur if vulnerable dependencies are present in the Spark classpath or application dependencies.
• Impact: Impacts vary depending on the specific vulnerability, ranging from Remote Code Execution, Denial of Service, Information Disclosure, to Elevation of Privilege. This can lead to full compromise of Spark nodes and the application.
• Affected Spark Component: Spark Core, Spark SQL, Spark Streaming, and any Spark components relying on vulnerable dependencies.
• Risk Severity: High to Critical (depending on the vulnerability)
• Mitigation Strategies:
  • Regularly update Spark and all its dependencies to the latest versions, including security patches.
  • Use dependency scanning tools to identify known vulnerabilities in project dependencies.
  • Monitor security advisories and vulnerability databases for Spark and its dependencies.
  • Implement a vulnerability management process to promptly address identified vulnerabilities.
  • Use Software Composition Analysis (SCA) tools to manage and monitor dependencies.

Threat: Insecure Spark Configuration leading to Security Weaknesses

• Description: An administrator or developer misconfigures Spark settings, disabling critical security features, using weak or default authentication, or exposing sensitive ports without proper access control. For example, disabling authentication for RPC communication or using default passwords. This creates significant security loopholes that attackers can exploit to gain unauthorized access or disrupt operations.
• Impact: Unauthorized access to Spark components, data breaches, denial of service, weakening of the overall security posture, and potential for further exploitation of the compromised system. Critical misconfigurations can lead to immediate and widespread compromise.
• Affected Spark Component: Spark Core, Spark UI, Spark Security features, Cluster Manager configuration, RPC framework.
• Risk Severity: High to Critical (depending on the specific misconfiguration)
• Mitigation Strategies:
  • Follow security best practices and hardening guidelines for Spark configuration.
  • Enable and enforce strong authentication and authorization for all Spark components and RPC communication.
  • Avoid using default passwords and ensure strong password policies are in place where applicable.
  • Minimize the number of exposed ports and services, and restrict access to necessary ports only.
  • Regularly review and audit Spark configurations to identify and rectify misconfigurations.
  • Use configuration management tools to ensure consistent and secure configurations across the Spark cluster.
  • Implement automated configuration checks to detect deviations from security baselines.