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Mitigation Strategies Analysis for elastic/logstash

Mitigation Strategy: Input Validation and Sanitization (Logstash-Focused)

  • Description:
    1. Define Expected Input Format within Logstash: For each input pipeline in Logstash, clearly define the expected log format (e.g., JSON, CSV, plain text) and data types for each field within the Logstash configuration.
    2. Implement Validation Filters in Logstash: In Logstash pipeline configuration files, add if conditions and mutate filters within the pipeline to perform validation.
      • Use grok or json filters in Logstash to parse the input and check format conformance.
      • Use mutate filter with convert in Logstash to enforce data types (e.g., convert => { "timestamp" => "date" }).
      • Use if conditions in Logstash to drop events that fail validation.
    3. Implement Sanitization Filters in Logstash: Use mutate filter with gsub in Logstash to sanitize input fields.
      • Escape special characters using gsub within Logstash filters.
      • Remove or replace malicious patterns using gsub within Logstash filters.
    4. Test and Monitor Logstash Validation: Test validation and sanitization filters within the Logstash pipeline with various inputs. Monitor Logstash logs for dropped events and filter errors.
  • List of Threats Mitigated:
    • Log Injection Attacks (High Severity): Prevents malicious log entries from being processed by Logstash, mitigating downstream exploits.
    • Cross-Site Scripting (XSS) in Logs (Medium Severity): Reduces the risk of storing and displaying malicious scripts embedded in logs processed by Logstash.
    • SQL Injection via Logs (Medium Severity): Mitigates the risk of logs containing SQL injection payloads being processed by Logstash and potentially reaching vulnerable systems.
    • Data Corruption (Low Severity): Prevents malformed input data from causing errors within Logstash processing.
  • Impact:
    • Log Injection Attacks: High Risk Reduction
    • XSS in Logs: Medium Risk Reduction
    • SQL Injection via Logs: Medium Risk Reduction
    • Data Corruption: Low Risk Reduction
  • Currently Implemented: Partially implemented in application-logs.conf pipeline for Beats inputs. Basic sanitization using gsub for Elasticsearch indexing is present.
  • Missing Implementation: Validation and sanitization are missing for TCP and system logs ingested directly by Logstash. More comprehensive sanitization rules are needed across all pipelines.

Mitigation Strategy: Rate Limiting and Resource Management (Logstash-Focused)

  • Description:
    1. Implement Rate Limiting using Logstash Filters:
      • Logstash Filter Level (using throttle filter - community plugin): Install and configure the throttle filter plugin within Logstash to limit events based on source IP, application, or other fields available in the log event. Define thresholds and actions (e.g., drop, tag) within the filter configuration.
    2. Configure Logstash Resource Limits:
      • JVM Heap Size (Logstash Configuration): Adjust the JVM heap size for Logstash in jvm.options or logstash.yml based on expected load.
      • Pipeline Worker Configuration (Logstash Configuration): Adjust the number of pipeline workers in logstash.yml to optimize resource utilization and prevent overload.
    3. Monitor Logstash Resource Usage: Monitor Logstash resource usage (CPU, memory, JVM heap) and input queue sizes using Logstash monitoring APIs or plugins. Set up alerts for resource exhaustion or queue buildup based on Logstash metrics.
  • List of Threats Mitigated:
    • Denial of Service (DoS) Attacks (High Severity): Prevents overwhelming Logstash itself with excessive log data, maintaining Logstash service availability.
    • Resource Exhaustion (Medium Severity): Protects Logstash from resource exhaustion due to legitimate log spikes, ensuring Logstash stability.
    • Performance Degradation (Medium Severity): Prevents performance degradation of Logstash due to uncontrolled log ingestion rates, ensuring efficient log processing.
  • Impact:
    • DoS Attacks: High Risk Reduction
    • Resource Exhaustion: Medium Risk Reduction
    • Performance Degradation: Medium Risk Reduction
  • Currently Implemented: JVM heap size and pipeline workers are configured in logstash.yml. Basic resource monitoring via Prometheus and Grafana (external to Logstash metrics).
  • Missing Implementation: Rate limiting using Logstash filters (like throttle) is not implemented. Alerting based on Logstash internal metrics is not fully configured.

Mitigation Strategy: Secure Input Plugins Configuration (Logstash-Focused)

  • Description:
    1. Review Input Plugin Configuration in Logstash: Regularly review the configuration of all input plugins defined in Logstash pipelines.
    2. Secure Credentials Management for Logstash Inputs: For input plugins requiring credentials (e.g., jdbc, kafka, redis), avoid hardcoding credentials directly in Logstash configuration files.
      • Use environment variables passed to the Logstash process to provide credentials.
      • Utilize secure secrets management solutions (e.g., HashiCorp Vault, AWS Secrets Manager) and configure Logstash to fetch credentials from these stores via plugins or environment variables.
    3. Disable Unnecessary Input Plugins in Logstash: Disable or remove any input plugins from Logstash configuration that are not actively used.
    4. Regular Plugin Updates for Logstash: Keep input plugins installed in Logstash updated to the latest versions.
  • List of Threats Mitigated:
    • Credential Exposure (High Severity): Reduces the risk of exposing sensitive credentials used by Logstash input plugins.
    • Plugin Vulnerabilities Exploitation (Medium Severity): Mitigates the risk of attackers exploiting vulnerabilities in outdated input plugins within Logstash.
  • Impact:
    • Credential Exposure: High Risk Reduction
    • Plugin Vulnerabilities Exploitation: Medium Risk Reduction
  • Currently Implemented: Environment variables are used for database credentials in jdbc input. Plugin updates are performed periodically.
  • Missing Implementation: Secure secrets management solution integration for all credentials used by Logstash. Formalized review and update process for input plugin configurations in Logstash. Regular removal of unused plugins from Logstash.

Mitigation Strategy: Principle of Least Privilege for Filters (Logstash-Focused)

  • Description:
    1. Review Filter Pipeline Logic in Logstash: Analyze each filter within Logstash pipelines and understand its purpose and data access requirements.
    2. Minimize Filter Operations in Logstash: Design filters in Logstash to perform only necessary operations on log data. Avoid granting filters broad data modification capabilities.
    3. Restrict Access to Sensitive Fields in Logstash Filters: If filters in Logstash need to access sensitive fields, limit access to only those specific fields within the filter configuration.
    4. Avoid Overly Permissive Filters in Logstash: Avoid using filters in Logstash that are overly permissive or grant excessive capabilities within the Logstash pipeline.
    5. Regular Filter Audits in Logstash: Periodically audit filter configurations in Logstash pipelines to ensure adherence to least privilege.
  • List of Threats Mitigated:
    • Data Breaches due to Filter Misconfiguration (Medium Severity): Reduces unintentional data breaches caused by filters within Logstash mismanaging sensitive data.
    • Unintended Data Modification (Low Severity): Prevents filters in Logstash from unintentionally modifying or corrupting log data.
  • Impact:
    • Data Breaches due to Filter Misconfiguration: Medium Risk Reduction
    • Unintended Data Modification: Low Risk Reduction
  • Currently Implemented: Filters are generally task-specific. Code review for pipeline changes includes filter logic.
  • Missing Implementation: Formal process for reviewing and enforcing least privilege for Logstash filters. Automated checks for overly permissive filters in Logstash configurations.

Mitigation Strategy: Secure Plugin Usage and Auditing (Logstash-Focused)

  • Description:
    1. Plugin Vetting Process for Logstash: Establish a process for vetting and approving new plugins before installation in Logstash.
      • Verify plugin source and maintainer reputation before adding to Logstash.
      • Review plugin documentation and code for security concerns before Logstash deployment.
    2. Use Official and Trusted Plugins in Logstash: Prioritize using officially maintained plugins from the Elastic ecosystem for Logstash.
    3. Regular Plugin Updates for Logstash: Implement a process for regularly updating plugins installed in Logstash.
    4. Plugin Inventory and Auditing for Logstash: Maintain an inventory of all plugins installed in each Logstash instance. Regularly audit plugin usage within Logstash.
    5. Security Monitoring for Logstash Plugins: Monitor for security advisories related to plugins used in Logstash.
  • List of Threats Mitigated:
    • Plugin Vulnerabilities Exploitation (High Severity): Prevents attackers from exploiting vulnerabilities in plugins within Logstash.
    • Malicious Plugins (Medium Severity): Reduces the risk of using malicious plugins in Logstash.
    • Supply Chain Attacks (Medium Severity): Mitigates risks related to plugin supply chain for Logstash.
  • Impact:
    • Plugin Vulnerabilities Exploitation: High Risk Reduction
    • Malicious Plugins: Medium Risk Reduction
    • Supply Chain Attacks: Medium Risk Reduction
  • Currently Implemented: Plugins are generally chosen from official Elastic sources. Plugin updates are performed periodically.
  • Missing Implementation: Formal plugin vetting and approval process for Logstash. Plugin inventory and auditing for Logstash instances. Automated security monitoring for Logstash plugins.

Mitigation Strategy: Data Masking and Redaction (Logstash-Focused)

  • Description:
    1. Define Masking/Redaction Rules for Logstash: Define clear rules for masking or redacting sensitive data within Logstash pipelines.
    2. Implement Masking Filters in Logstash: In Logstash pipeline configuration files, add mutate filters with gsub or use dedicated masking plugins (e.g., mask filter - community plugin) within Logstash to implement masking rules.
      • Use regular expressions in gsub within Logstash filters.
      • Configure masking plugins within Logstash pipelines.
    3. Test and Validate Masking in Logstash: Thoroughly test masking filters within Logstash pipelines.
    4. Regularly Review Masking Rules in Logstash: Periodically review and update masking rules applied in Logstash.
  • List of Threats Mitigated:
    • Data Breaches via Logs (High Severity): Prevents sensitive data from being exposed in logs processed and stored by Logstash.
    • Compliance Violations (Medium Severity): Helps comply with data privacy regulations by preventing Logstash from processing and storing sensitive personal information in logs.
    • Internal Data Misuse (Medium Severity): Reduces the risk of internal users misusing sensitive data from logs processed by Logstash.
  • Impact:
    • Data Breaches via Logs: High Risk Reduction
    • Compliance Violations: Medium Risk Reduction
    • Internal Data Misuse: Medium Risk Reduction
  • Currently Implemented: Basic masking for API keys and passwords using mutate and gsub in application-logs.conf.
  • Missing Implementation: Comprehensive sensitive data identification for Logstash pipelines. Formalized masking rules for Logstash. Use of dedicated masking plugins in Logstash. Regular review of masking rules in Logstash.

Mitigation Strategy: Avoid Code Injection Vulnerabilities in Filters (Logstash-Focused)

  • Description:
    1. Minimize Scripting Usage in Logstash Filters: Minimize the use of scripting filters (e.g., ruby, script) within Logstash pipelines.
    2. Secure Script Development for Logstash Filters: If scripting filters are used in Logstash, follow secure coding practices.
      • Sanitize user-controlled input within Logstash scripts.
      • Avoid dynamic code execution in Logstash scripts.
    3. Code Review for Logstash Scripts: Conduct thorough code reviews of all scripting filters in Logstash pipelines.
    4. Restrict Scripting Permissions in Logstash (if possible): If possible, restrict permissions granted to scripting filters within Logstash.
    5. Regular Security Audits of Logstash Configurations: Include scripting filters in regular security audits of Logstash configurations.
  • List of Threats Mitigated:
    • Code Injection Attacks via Filters (High Severity): Prevents code injection attacks targeting Logstash filters.
    • Privilege Escalation via Filters (Medium Severity): Reduces the risk of privilege escalation through code injection in Logstash filters.
  • Impact:
    • Code Injection Attacks via Filters: High Risk Reduction
    • Privilege Escalation via Filters: Medium Risk Reduction
  • Currently Implemented: Scripting filters are generally avoided in Logstash. Code review for all Logstash configuration changes.
  • Missing Implementation: Formal policy to minimize scripting in Logstash filters. Specific security guidelines for scripting filters in Logstash. Automated static analysis for code injection in Logstash configurations.