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Mitigation Strategies Analysis for apache/airflow

Mitigation Strategy: Implement Secrets Backend for Credentials

  • Description:
    1. Choose a supported secrets backend service like HashiCorp Vault, AWS Secrets Manager, Google Secret Manager, or Azure Key Vault.
    2. Install the corresponding Airflow provider package for your chosen secrets backend (e.g., apache-airflow-providers-hashicorp-vault).
    3. Configure Airflow settings in airflow.cfg or environment variables to enable and configure the secrets backend. This typically involves setting secrets_backend and secrets_backend_kwargs to point to your secrets backend instance.
    4. Migrate existing sensitive credentials (passwords, API keys, tokens) from Airflow Connections and Variables to the chosen secrets backend.
    5. Update DAGs and operators to retrieve credentials dynamically from the secrets backend using Airflow's secrets management features (e.g., secrets.get_connection, secrets.get_variable).
    6. Remove any plaintext credentials from Airflow Connections and Variables.
  • Threats Mitigated:
    • Exposure of credentials in plaintext: (High Severity) - Attackers gaining access to plaintext credentials stored in Airflow metadata database, configuration files, or code repositories.
    • Unauthorized access to sensitive data: (High Severity) - Compromised credentials leading to unauthorized access to external systems and sensitive data managed by Airflow DAGs.
    • Credential leakage through logs or backups: (Medium Severity) - Accidental exposure of credentials in logs, database backups, or configuration exports.
  • Impact:
    • Exposure of credentials in plaintext: High Risk Reduction - Effectively eliminates the risk of plaintext credential storage within Airflow.
    • Unauthorized access to sensitive data: High Risk Reduction - Significantly reduces the risk by centralizing and securing credential management outside of Airflow's core components.
    • Credential leakage through logs or backups: Medium Risk Reduction - Reduces the risk by preventing credentials from being directly present in Airflow's internal data.
  • Currently Implemented:
    • Yes, partially implemented using AWS Secrets Manager for new DAGs and connections related to AWS services. Configuration is set in airflow.cfg and provider package is installed.
  • Missing Implementation:
    • Migration of credentials for existing DAGs and connections that are not AWS related. Need to extend secrets backend usage to all connections and variables containing sensitive information across all DAGs.

Mitigation Strategy: Enforce Role-Based Access Control (RBAC)

  • Description:
    1. Enable RBAC in Airflow configuration by setting [webserver] auth_manager = airflow.providers.fab.auth_manager.SecurityManager.
    2. Define roles based on user responsibilities and the principle of least privilege (e.g., DAG Viewer, DAG Editor, Operator, Admin).
    3. Assign users to appropriate roles based on their job functions.
    4. Grant roles specific permissions within Airflow RBAC, controlling access to DAGs, connections, variables, pools, and other Airflow resources.
    5. Regularly review and update RBAC policies to reflect changes in user roles and project requirements.
    6. Audit user permissions and access logs to ensure RBAC is effectively enforced.
  • Threats Mitigated:
    • Unauthorized access to sensitive DAGs and configurations: (High Severity) - Users accessing and potentially modifying DAGs, connections, or variables they are not authorized to view or manage.
    • Accidental or malicious modification of critical workflows: (High Severity) - Unauthorized users making changes to DAGs that could disrupt critical data pipelines or system operations.
    • Privilege escalation: (Medium Severity) - Users gaining access to higher privileges than intended, potentially leading to broader security breaches.
  • Impact:
    • Unauthorized access to sensitive DAGs and configurations: High Risk Reduction - Significantly restricts unauthorized access by enforcing granular permissions.
    • Accidental or malicious modification of critical workflows: High Risk Reduction - Reduces the risk of unauthorized changes by limiting modification permissions to authorized roles.
    • Privilege escalation: Medium Risk Reduction - Mitigates privilege escalation by clearly defining and enforcing role boundaries.
  • Currently Implemented:
    • Yes, RBAC is enabled in Airflow webserver configuration. Basic roles like Admin, Op, User, Viewer are configured.
  • Missing Implementation:
    • Granular role definitions are missing. Need to create more specific roles tailored to different teams and responsibilities (e.g., "Data Engineering DAG Editor", "Marketing DAG Viewer"). Also, need to implement a process for regular RBAC policy review and user permission audits.

Mitigation Strategy: Implement DAG Code Reviews and Secure Coding Practices

  • Description:
    1. Establish a mandatory code review process for all DAGs before deployment to production.
    2. Train DAG developers on secure coding practices specific to Airflow, including input validation, secure credential handling, and avoiding code injection vulnerabilities.
    3. Use code review checklists that include security considerations for Airflow DAGs.
    4. Utilize static code analysis tools to automatically scan DAG code for potential security vulnerabilities.
    5. Promote the principle of least privilege in DAG design, ensuring DAGs only have the necessary permissions and access to resources.
    6. Regularly update secure coding guidelines and training materials based on new vulnerabilities and best practices.
  • Threats Mitigated:
    • Code injection vulnerabilities in DAGs: (High Severity) - Malicious code injected into DAGs through insecure input handling or vulnerable dependencies, leading to arbitrary code execution on Airflow workers or infrastructure.
    • Insecure handling of credentials within DAG code: (High Severity) - Developers accidentally hardcoding credentials or logging sensitive information within DAG code, leading to exposure.
    • Logic flaws in DAGs leading to data breaches or system compromise: (Medium Severity) - Errors in DAG logic that could result in data leaks, data corruption, or unintended system access.
  • Impact:
    • Code injection vulnerabilities in DAGs: High Risk Reduction - Code reviews and secure coding practices are highly effective in preventing injection vulnerabilities by identifying and fixing them before deployment.
    • Insecure handling of credentials within DAG code: High Risk Reduction - Training and code reviews enforce secure credential management, preventing accidental exposure in DAG code.
    • Logic flaws in DAGs leading to data breaches or system compromise: Medium Risk Reduction - Code reviews help identify and correct logic errors, reducing the risk of unintended consequences.
  • Currently Implemented:
    • Yes, code reviews are mandatory for all DAGs before merging to the main branch. Basic secure coding guidelines are documented.
  • Missing Implementation:
    • Formalized secure coding training for DAG developers is missing. Security-focused code review checklists are not yet implemented. Static code analysis tools are not integrated into the DAG development pipeline.

Mitigation Strategy: Redact Sensitive Data in Airflow Logs

  • Description:
    1. Implement mechanisms to redact sensitive information (passwords, API keys, personal data) from Airflow logs before they are stored or displayed.
    2. Utilize logging filters or custom log handlers within Airflow's logging configuration to identify and mask sensitive data patterns.
    3. Configure operators and DAG code to avoid logging sensitive information directly.
    4. Regularly review log outputs to ensure redaction is effective and identify any missed sensitive data.
    5. Educate DAG developers on avoiding logging sensitive information in the first place and using redaction mechanisms when necessary.
  • Threats Mitigated:
    • Exposure of sensitive data in logs: (High Severity) - Sensitive information like passwords, API keys, or personal data being inadvertently logged and becoming accessible to unauthorized users with access to logs.
    • Compliance violations: (Medium Severity) - Logging sensitive data may violate data privacy regulations (e.g., GDPR, HIPAA).
  • Impact:
    • Exposure of sensitive data in logs: High Risk Reduction - Redaction effectively prevents sensitive data from being exposed in logs, even if logs are compromised.
    • Compliance violations: Medium Risk Reduction - Reduces the risk of compliance violations by preventing logging of sensitive data.
  • Currently Implemented:
    • No, currently no specific redaction mechanisms are implemented in Airflow logging.
  • Missing Implementation:
    • Need to implement logging filters or custom handlers to redact sensitive data. Define patterns for sensitive data to be redacted. Need to review existing logs and DAGs to identify and address potential sensitive data logging.

Mitigation Strategy: Regularly Update Airflow and Dependencies

  • Description:
    1. Establish a process for regularly checking for new Airflow releases and security advisories.
    2. Subscribe to Airflow security mailing lists or monitor official channels for security announcements.
    3. Implement a schedule for applying Airflow updates and security patches in a timely manner.
    4. Regularly update Python dependencies used by Airflow and DAGs to their latest secure versions.
    5. Use dependency scanning tools to identify known vulnerabilities in Airflow and its dependencies.
    6. Test updates in a staging environment before deploying them to production to ensure compatibility and stability.
  • Threats Mitigated:
    • Exploitation of known vulnerabilities in Airflow core and dependencies: (High Severity) - Attackers exploiting publicly known vulnerabilities in outdated versions of Airflow or its dependencies to gain unauthorized access, execute code, or cause denial of service.
    • Zero-day vulnerabilities: (Medium Severity) - While updates primarily address known vulnerabilities, staying up-to-date reduces the window of opportunity for attackers to exploit newly discovered zero-day vulnerabilities.
  • Impact:
    • Exploitation of known vulnerabilities in Airflow core and dependencies: High Risk Reduction - Regularly updating and patching eliminates known vulnerabilities, significantly reducing the attack surface.
    • Zero-day vulnerabilities: Medium Risk Reduction - While not a direct mitigation, timely updates ensure that patches for newly discovered vulnerabilities are applied quickly, minimizing the exposure window.
  • Currently Implemented:
    • Yes, there is a process for checking Airflow releases. Dependency updates are performed periodically but not on a strict schedule.
  • Missing Implementation:
    • Need to implement a more rigorous schedule for Airflow and dependency updates, including automated vulnerability scanning and a defined patching process. No formal subscription to security advisories is in place.