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attack-surface.md

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Attack Surface Analysis for apache/spark

Attack Surface: Unencrypted Network Communication

  • Description: Data transmitted between Spark components (Driver, Executors, Shuffle Service) is intercepted or modified in transit.
  • Spark Contribution: Spark uses RPC and network-based shuffle, creating multiple points for network interception. This is inherent to Spark's distributed nature.
  • Example: An attacker on the same network as the Spark cluster uses a packet sniffer to capture shuffle data containing sensitive customer information.
  • Impact: Data breach, unauthorized data modification, denial of service.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Enable TLS/SSL: Configure Spark to use TLS/SSL for all communication channels (spark.network.crypto.enabled, spark.ssl.* properties). This is mandatory for secure operation.
    • Strong Ciphers: Use strong, up-to-date cipher suites.
    • Network Segmentation: Isolate Spark components on a dedicated network segment, limiting exposure.
    • Authentication: Implement authentication (e.g., SASL with spark.authenticate) to verify the identity of communicating components.

Attack Surface: Unauthenticated/Unauthorized Access to Spark UI/REST API

  • Description: Attackers gain access to the Spark UI or REST API, exposing cluster information, application details, and potentially sensitive data.
  • Spark Contribution: Spark provides a web UI and REST API for monitoring and management, which can be exposed without authentication by default. This is a Spark-provided feature.
  • Example: An attacker discovers the Spark UI is publicly accessible and uses it to view environment variables containing database credentials. They then use the REST API to kill running jobs.
  • Impact: Information disclosure, denial of service, potential credential theft.
  • Risk Severity: High
  • Mitigation Strategies:
    • Enable Authentication: Configure authentication for the Spark UI and REST API (spark.ui.authentication). This is a direct Spark configuration.
    • Strong Authentication: Use a robust authentication mechanism (Kerberos, LDAP, or a custom filter).
    • Access Control: Restrict access to the UI and API to authorized users and networks (firewall rules, reverse proxy).
    • Disable if Unnecessary: If the UI/API is not needed, disable it entirely.

Attack Surface: Malicious Code Injection (UDFs, Dependencies)

  • Description: Attackers inject malicious code into the Spark cluster through user-defined functions (UDFs), custom JARs, or compromised dependencies.
  • Spark Contribution: Spark's ability to execute user-provided code (UDFs) and load external libraries (JARs) creates an avenue for code injection. This is a core feature of Spark's extensibility.
  • Example: An attacker submits a Spark job with a malicious UDF that executes a shell command to download and run malware on the worker nodes.
  • Impact: Arbitrary code execution, system compromise, data exfiltration.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Code Review: Thoroughly review and validate all user-provided code (UDFs, transformations) that will be executed by Spark.
    • Dependency Management: Use a secure dependency management system and verify the integrity of Spark dependencies.
    • Vulnerability Scanning: Regularly scan dependencies for known vulnerabilities.
    • Code Signing: Implement code signing to verify the integrity and authenticity of JARs loaded by Spark.

Attack Surface: Deserialization Vulnerabilities

  • Description: Attackers exploit vulnerabilities in Spark's serialization/deserialization process to achieve remote code execution.
  • Spark Contribution: Spark relies heavily on serialization for data transfer and persistence, making it susceptible to deserialization attacks. This is fundamental to Spark's operation.
  • Example: An attacker sends crafted, malicious serialized data to a Spark application that uses a vulnerable version of a serialization library. Upon deserialization, the attacker's code is executed.
  • Impact: Remote code execution, system compromise.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Keep Spark Updated: Regularly update Spark and its dependencies to the latest versions to patch known deserialization vulnerabilities. This is crucial for mitigating Spark-specific vulnerabilities.
    • Avoid Untrusted Data: Do not deserialize data from untrusted sources within Spark applications.
    • Safer Serialization (If Possible): If feasible, consider using more secure serialization formats (e.g., Avro, Protocol Buffers) that are less prone to deserialization vulnerabilities than Java serialization, specifically within the Spark context.
    • Vulnerability Scanning: Use vulnerability scanning tools to identify libraries with known deserialization issues that are used by Spark.

Attack Surface: SQL Injection in Spark SQL

  • Description: Attackers inject malicious SQL code into Spark SQL queries, leading to unauthorized data access or modification.
  • Spark Contribution: Spark SQL allows users to execute SQL queries against DataFrames, creating a potential vector for SQL injection. This is a Spark-provided feature.
  • Example: A web application allows users to filter data using a text input field. The application constructs a Spark SQL query by directly concatenating the user's input without sanitization. An attacker enters ' OR '1'='1 to bypass filtering and retrieve all data.
  • Impact: Data breach, data modification, unauthorized data access.
  • Risk Severity: High
  • Mitigation Strategies:
    • Parameterized Queries: Use parameterized queries (prepared statements) whenever possible. Spark's DataFrame API provides a safer way to construct queries programmatically, avoiding direct SQL string manipulation.
    • Input Validation: Sanitize and validate all user input before using it in Spark SQL queries.
    • Avoid String Concatenation: Do not build Spark SQL queries by concatenating strings with user input.