Skip to content

Latest commit

 

History

History
130 lines (94 loc) · 11.3 KB

mitigations.md

File metadata and controls

130 lines (94 loc) · 11.3 KB

Mitigation Strategies Analysis for apache/flink

Mitigation Strategy: Strict Access Control (RBAC/ABAC) within Flink

Mitigation Strategy: Implement Role-Based Access Control (RBAC) or Attribute-Based Access Control (ABAC) using Flink's configuration and (if necessary) custom authorizers.

  • Description:

    1. Authentication Integration: Configure Flink to integrate with an authentication provider (e.g., Kerberos) using Flink's configuration options (security.kerberos.* properties in flink-conf.yaml). This establishes user identity.
    2. Authorization Configuration: If Flink's built-in authorization mechanisms are sufficient, configure them in flink-conf.yaml. This might involve setting properties related to authorized users or groups.
    3. Custom Authorizer (If Needed): If Flink's built-in mechanisms are insufficient, develop a custom authorizer. This is a Java class that implements Flink's authorization interface. This class intercepts requests and determines if the authenticated user has the required permissions. You'd configure Flink to use this custom authorizer in flink-conf.yaml.
    4. Define Roles and Permissions within Flink: Within your custom authorizer (or using Flink's built-in mechanisms), define roles and the specific Flink operations each role is allowed to perform (e.g., submit jobs, cancel jobs, view job status, access specific resources).
    5. Configure Flink: Ensure all relevant settings in flink-conf.yaml are correctly configured to enable authentication, authorization, and (if applicable) your custom authorizer.

  • Threats Mitigated:

    • Unauthorized Job Submission (Severity: Critical): Prevents unauthorized users from submitting jobs via the Flink REST API or CLI.
    • Unauthorized Job Modification (Severity: Critical): Prevents unauthorized users from modifying or canceling running jobs.
    • Information Disclosure (Severity: Medium): Limits access to sensitive information exposed through the Flink REST API or web UI.

  • Impact:

    • Unauthorized Job Submission: Risk reduced significantly (e.g., 90-95%, depending on the strength of the authentication and authorization implementation).
    • Unauthorized Job Modification: Risk reduced significantly (similar to job submission).
    • Information Disclosure: Risk reduced significantly (e.g., 80-90%).

  • Currently Implemented: [Example: We have basic Kerberos authentication configured in flink-conf.yaml. We have a rudimentary custom authorizer that allows only users in a specific Kerberos group to submit jobs.]

  • Missing Implementation: [Example: Our custom authorizer is very basic. We need to implement finer-grained permissions (e.g., allowing users to only view their own jobs, not all jobs).] We also need to add authorization checks for more REST API endpoints.]

Mitigation Strategy: TLS/SSL Encryption for Flink Communication

Mitigation Strategy: Enable TLS/SSL encryption for all internal Flink communication and the REST API using Flink's configuration.

  • Description:

    1. Certificate Management: Obtain or generate SSL certificates (self-signed for testing, CA-signed for production).
    2. Flink Configuration: Modify flink-conf.yaml to enable SSL:
      • security.ssl.enabled: true
      • security.ssl.keystore: Path to the keystore file.
      • security.ssl.truststore: Path to the truststore file.
      • security.ssl.keystore-password: Keystore password.
      • security.ssl.truststore-password: Truststore password.
      • security.ssl.key-password: Key password (if different from keystore password).
      • security.ssl.protocols: Specify allowed TLS protocols (e.g., TLSv1.2,TLSv1.3).
      • security.ssl.algorithms: Specify allowed cipher suites (use strong, modern ciphers).
      • Configure similar options for the REST API (rest.ssl.*).
    3. Restart Flink: Restart the Flink cluster for the changes to take effect.

  • Threats Mitigated:

    • Man-in-the-Middle Attacks (Severity: High): Prevents attackers from intercepting and modifying communication between Flink components (JobManager, TaskManagers) and the REST API.
    • Information Disclosure (Severity: Medium): Protects sensitive data transmitted between Flink components.

  • Impact:

    • Man-in-the-Middle Attacks: Risk reduced very significantly (e.g., 95-99%).
    • Information Disclosure: Risk reduced significantly (e.g., 80-90%).

  • Currently Implemented: [Example: TLS/SSL is enabled for internal communication and the REST API using self-signed certificates. Configuration is in flink-conf.yaml.]

  • Missing Implementation: [Example: We need to replace the self-signed certificates with certificates signed by a trusted CA.]

Mitigation Strategy: Secure State Backend Configuration

Mitigation Strategy: Configure the chosen state backend (e.g., RocksDB, filesystem) securely using Flink's configuration options.

  • Description:

    1. Choose State Backend: Select the appropriate state backend for your needs (RocksDB, filesystem, etc.).
    2. RocksDB Configuration (If Using RocksDB):
      • Directory Permissions: Ensure the directory where RocksDB stores its data has restricted permissions, accessible only by the Flink user. This is operating system level, but driven by Flink's choice of directory.
      • Flink Configuration: Use Flink's configuration options (state.backend.rocksdb.* in flink-conf.yaml) to fine-tune RocksDB settings. While most settings are performance-related, some have security implications (e.g., limiting memory usage to prevent resource exhaustion).
      • Encryption (If Supported): If your RocksDB version and setup support it, consider enabling encryption at rest for the state data. This would likely involve external configuration and key management, but the decision to use it is driven by the Flink deployment.
    3. Filesystem Configuration (If Using Filesystem State Backend):
      • Directory Permissions: Similar to RocksDB, ensure the state directory has restricted permissions.
      • Flink Configuration: Use Flink's configuration options (state.backend.fs.*) to specify the directory and other relevant settings.
    4. Memory State Backend (If Using):
      • Be aware that the in-memory state backend is not persistent and is vulnerable to data loss on TaskManager failure. If used, ensure that your application can tolerate this.

  • Threats Mitigated:

    • Unauthorized State Access (Severity: High): Prevents unauthorized access to the Flink application's state data.
    • Data Corruption/Loss (Severity: High): Proper configuration helps prevent accidental or malicious corruption or loss of state data.
    • Resource Exhaustion (Severity: Medium): Properly configuring resource limits for the state backend (especially RocksDB) can prevent denial-of-service attacks.

  • Impact:

    • Unauthorized State Access: Risk reduced significantly (e.g., 80-90%, primarily through OS-level file permissions).
    • Data Corruption/Loss: Risk reduced moderately (e.g., 60-70%).
    • Resource Exhaustion: Risk reduced moderately (e.g., 50-70%).

  • Currently Implemented: [Example: We are using RocksDB. The state directory has restricted permissions. We have configured basic RocksDB settings in flink-conf.yaml.]

  • Missing Implementation: [Example: We haven't explored RocksDB's encryption options. We should investigate this.]

Mitigation Strategy: Resource Management Configuration

Mitigation Strategy: Configure Flink's resource management (slots, memory) to limit resource consumption per job and prevent denial-of-service.

  • Description:

    1. Slot Configuration: Configure the number of slots per TaskManager (taskmanager.numberOfTaskSlots in flink-conf.yaml).
    2. Memory Configuration: Configure the amount of memory available to each TaskManager (taskmanager.memory.process.size, taskmanager.memory.flink.size, taskmanager.memory.managed.size, etc., in flink-conf.yaml). Carefully tune these settings to provide sufficient memory for your jobs while preventing excessive memory consumption.
    3. Job-Specific Resource Requests (If Supported): If your Flink version and deployment environment support it (e.g., when running on Kubernetes), you can specify resource requests (CPU, memory) for individual jobs. This allows the resource manager (e.g., Kubernetes) to enforce these limits.
    4. Dynamic Scaling Limits: If using Flink's reactive mode or an autoscaler, configure maximum and minimum limits on the number of TaskManagers to prevent uncontrolled scaling.

  • Threats Mitigated:

    • Denial-of-Service (DoS) (Severity: High): Prevents a single malicious or buggy job from consuming all cluster resources and making the system unavailable.
    • Resource Exhaustion (Severity: Medium): Prevents jobs from exceeding their allocated resources and potentially crashing TaskManagers.

  • Impact:

    • Denial-of-Service (DoS): Risk reduced significantly (e.g., 70-80%).
    • Resource Exhaustion: Risk reduced significantly (e.g., 80-90%).

  • Currently Implemented: [Example: We have configured the number of slots and memory per TaskManager in flink-conf.yaml.]

  • Missing Implementation: [Example: We are not currently using job-specific resource requests. We should investigate this, especially as we move to a Kubernetes deployment.]

Mitigation Strategy: Regular Flink Updates (Version Management)

Mitigation Strategy: Keep your Flink version up-to-date with the latest stable releases. This is a process, but the action is to update Flink itself.

  • Description:

    1. Monitor: Stay informed about new Flink releases and security advisories.
    2. Plan: Schedule regular upgrades to the latest stable Flink version.
    3. Test: Thoroughly test upgrades in a non-production environment before deploying to production.
    4. Upgrade: Follow Flink's upgrade instructions to update your Flink cluster. This usually involves downloading the new version, updating configuration files, and restarting the cluster.
    5. Rollback: Have a plan to roll back to the previous version if necessary.

  • Threats Mitigated:

    • Exploitation of Known Vulnerabilities (Severity: Variable, can range from Low to Critical): Addresses vulnerabilities in Flink itself that could be exploited by attackers.

  • Impact:

    • Exploitation of Known Vulnerabilities: Risk reduction is highly variable and depends on the specific vulnerabilities patched in each release. However, this is crucial for long-term security.

  • Currently Implemented: [Example: We have a policy to upgrade Flink within one month of a new stable release.]

  • Missing Implementation: [Example: Our testing process for Flink upgrades could be more comprehensive.]