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Mitigation Strategies Analysis for open-telemetry/opentelemetry-collector

Mitigation Strategy: Input Validation and Rate Limiting (Receiver Level - Collector Config)

  • Description:

    1. Receiver Configuration: Within your config.yaml (or equivalent), locate the configuration for each active receiver (e.g., otlp, jaeger, zipkin).
    2. Data Size Limits:
      • otlp Receiver: Use the max_allowed_size setting (if available) within the protocols section (e.g., grpc or http) to limit the size of individual spans and log entries in bytes.
      • Other Receivers: Consult the documentation for each receiver to find equivalent settings for limiting message sizes.
    3. Attribute Count Limits: Within the receiver configuration, if supported, set limits on the number of attributes per span, metric, or log entry. Look for settings like max_attributes or similar.
    4. Data Type Validation: Most receivers perform basic data type validation against the OpenTelemetry specification by default. Confirm this in the receiver's documentation.
    5. Rate Limiting (Collector-Side): Check if your receivers have built-in rate limiting options (e.g., max_requests_per_second, rate_limiting configurations). If available, configure limits per client IP address or other identifying information within the receiver's configuration.
    6. Custom Receiver (If Necessary): If a built-in receiver lacks crucial validation or rate limiting, and external solutions are not feasible, consider developing a custom receiver that implements these features. This is an advanced option.
    7. Testing: Use a load-testing tool to send high volumes of data and oversized/malformed payloads to the collector. Verify that the configured limits are enforced and the collector remains stable.

  • Threats Mitigated:

    • Denial of Service (DoS) - High Severity: Prevents attackers from overwhelming the collector with excessive data.
    • Resource Exhaustion - High Severity: Limits resource consumption.
    • Data Injection (Partial) - Medium Severity: Provides some protection against large malicious payloads.

  • Impact:

    • DoS: Significantly reduces risk (if rate limiting and size limits are properly configured).
    • Resource Exhaustion: Significantly reduces risk.
    • Data Injection: Provides partial mitigation.

  • Currently Implemented (Example):

    • max_allowed_size is set for the otlp/grpc receiver.
    • No attribute count limits are configured.
    • No collector-side rate limiting is configured.

  • Missing Implementation (Example):

    • Attribute count limits are missing for all receivers.
    • Collector-side rate limiting is missing (where supported by the receiver).

Mitigation Strategy: Authentication and Authorization (Receiver Level - Collector Config & Extensions)

  • Description:

    1. Receiver Configuration: Within your config.yaml, examine each receiver's configuration for authentication options.
    2. mTLS (Recommended):
      • If the receiver supports mTLS (e.g., otlp with grpc or http), configure it:
        • Specify the path to the collector's server certificate and key (cert_file, key_file).
        • Specify the path to the CA certificate used to verify client certificates (ca_file).
        • Enable client authentication (e.g., client_auth: require).
    3. API Keys/Tokens (Less Secure):
      • If the receiver supports API key/token authentication, configure it:
        • Define a mechanism for validating API keys (e.g., a static list in the configuration, or a custom authenticator extension).
        • Specify the expected header containing the API key (e.g., X-API-Key).
    4. Custom Authenticator Extension: If built-in authentication mechanisms are insufficient, develop a custom authenticator extension. This extension would:
      • Implement the configauth.Authenticator interface.
      • Authenticate incoming requests based on your chosen method (mTLS, API keys, custom logic).
      • Be referenced in the receiver's configuration.
    5. Custom Authorizer Extension (Optional): For fine-grained authorization, develop a custom extension that:
      • Implements an appropriate interface (likely a custom one you define).
      • Receives authentication information (from the authenticator) and the incoming data.
      • Makes authorization decisions (allow/deny) based on your policies.
      • Integrates with the collector's pipeline (potentially as a processor).
    6. Testing: Send requests with valid, invalid, and expired credentials. Verify that only authenticated and authorized requests are processed.

  • Threats Mitigated:

    • Data Injection - High Severity: Prevents unauthorized data submission.
    • Data Tampering - High Severity: Ensures data integrity.
    • Unauthorized Access - High Severity: Prevents unauthorized connections.

  • Impact:

    • Data Injection: Significantly reduces risk.
    • Data Tampering: Significantly reduces risk.
    • Unauthorized Access: Significantly reduces risk.

  • Currently Implemented (Example):

    • No authentication is configured on any receivers.

  • Missing Implementation (Example):

    • Critical Gap: No authentication mechanism is implemented.
    • No custom authenticator or authorizer extensions are developed.

Mitigation Strategy: Data Masking/Redaction (Processor Level - Collector Config & Extensions)

  • Description:

    1. Identify Sensitive Data: Analyze your telemetry data to identify sensitive fields.
    2. Processor Configuration: Within your config.yaml, configure processors in the pipeline:
      • attributes Processor: Use this to modify or remove attributes:
        • insert, update, upsert: Use these actions with regular expressions or other logic to replace sensitive data with masked values (e.g., ***).
        • delete: Remove entire attributes containing sensitive data.
      • resource Processor: Similar to attributes, but for resource attributes.
      • filter Processor: Use this to drop entire spans, metrics, or logs based on the presence of sensitive data (less precise).
    3. Custom Processor (Recommended for Complex Logic): Develop a custom processor that:
      • Implements the appropriate processor interface (e.g., component.TracesProcessor, component.MetricsProcessor, component.LogsProcessor).
      • Contains your specific redaction logic (regular expressions, hashing, encryption, custom rules).
      • Is referenced in the pipelines section of your config.yaml.
    4. Testing: Send test data containing sensitive information. Verify that the processors correctly mask or remove the sensitive data before it is exported.

  • Threats Mitigated:

    • Data Leakage - High Severity: Prevents sensitive data exposure.
    • Compliance Violations - High Severity: Helps meet compliance requirements.

  • Impact:

    • Data Leakage: Significantly reduces risk.
    • Compliance Violations: Significantly reduces risk.

  • Currently Implemented (Example):

    • No data masking/redaction is implemented.

  • Missing Implementation (Example):

    • Critical Gap: No data masking/redaction is in place.
    • No custom processors for redaction are developed.

Mitigation Strategy: Secure Communication (Exporter Level - Collector Config)

  • Description:

    1. Exporter Configuration: Within your config.yaml, locate the configuration for each active exporter.
    2. TLS:
      • For each exporter, configure TLS encryption:
        • Specify the path to the CA certificate (or the server certificate) used to verify the backend's identity (ca_file or equivalent).
        • Enable TLS (e.g., tls: { enabled: true } or equivalent). The exact configuration depends on the exporter.
    3. mTLS (If Supported):
      • If the backend and exporter support mTLS, configure it:
        • Specify the path to the collector's client certificate and key (cert_file, key_file).
        • Enable client authentication on the backend (if you control it).
    4. Testing: Use network analysis tools (e.g., tcpdump, Wireshark) to verify that communication between the collector and backends is encrypted.

  • Threats Mitigated:

    • Man-in-the-Middle (MitM) Attacks - High Severity: Prevents data interception.
    • Data Tampering (in transit) - High Severity: Ensures data integrity.

  • Impact:

    • MitM Attacks: Significantly reduces risk.
    • Data Tampering (in transit): Significantly reduces risk.

  • Currently Implemented (Example):

    • TLS is enabled for the otlp exporter.
    • No mTLS is configured.

  • Missing Implementation (Example):

    • mTLS is not used, even where supported.

Mitigation Strategy: Configuration Hardening (Collector Config)

  • Description:

    1. Least Privilege: In your config.yaml, disable any receivers, processors, exporters, and extensions that are not absolutely required.
    2. Configuration Review: Regularly review the entire config.yaml for any unnecessary or insecure settings.
    3. Disable Debugging in Production: Ensure that debugging features (e.g., service::telemetry::logs::level: debug) are disabled in production. Set the log level to info or warn.
    4. Secure Configuration Storage: Do not store sensitive information (API keys, credentials) directly in config.yaml. Use environment variables or a secrets management system, and reference them in the configuration.
    5. Testing: After making configuration changes, thoroughly test the collector in a non-production environment.

  • Threats Mitigated:

    • Configuration Errors - Medium Severity: Reduces the risk of misconfigurations.
    • Information Disclosure - Medium Severity: Prevents sensitive data leaks.

  • Impact:

    • Configuration Errors: Reduces risk.
    • Information Disclosure: Reduces risk.

  • Currently Implemented (Example):

    • Some unused receivers are disabled.
    • Debugging is enabled in production.
    • API keys are stored directly in config.yaml.

  • Missing Implementation (Example):

    • A comprehensive configuration review is needed.
    • Debugging needs to be disabled in production.
    • Critical: Sensitive information needs to be removed from config.yaml.

Mitigation Strategy: Extension Security (Collector Extensions)

  • Description:

    1. Inventory: List all custom or third-party extensions used by your collector.
    2. Source Verification:
      • Prefer extensions from the official OpenTelemetry project or reputable vendors.
      • For community extensions, carefully review the source code for potential security issues (input validation, error handling, secure coding practices).
    3. Least Privilege (Within Extension Code): If you are developing custom extensions, ensure they only access the necessary resources and data. Avoid granting broad permissions.
    4. Regular Updates: Keep extensions up-to-date. Monitor for security advisories.
    5. Testing (For Custom Extensions): Thoroughly test custom extensions, focusing on security aspects:
      • Input validation: Test with invalid and malicious inputs.
      • Error handling: Ensure errors are handled gracefully and do not expose sensitive information.
      • Resource usage: Monitor resource consumption to prevent DoS vulnerabilities.

  • Threats Mitigated:

    • Vulnerabilities in Extensions - Medium to High Severity: Reduces the risk of exploiting vulnerabilities in extensions.
    • Malicious Extensions - High Severity: Helps prevent the use of malicious code.

  • Impact:

    • Vulnerabilities in Extensions: Reduces risk.
    • Malicious Extensions: Reduces risk.

  • Currently Implemented (Example):

    • Only official OpenTelemetry extensions are used.
    • No formal code review process for extensions is in place.

  • Missing Implementation (Example):

    • A documented process for managing and vetting extensions is needed.
    • Code review of custom extensions (if any) is essential.

Mitigation Strategy: Observability of the Collector (Collector Config)

  • Description:

    1. Metrics: In your config.yaml, ensure the collector is configured to expose its internal metrics. The most common way is to use the prometheus exporter: 
      exporters:
  prometheus:
    endpoint: "0.0.0.0:8889" # Or a specific interface/port
    # ... other prometheus exporter settings ...
service:
  pipelines:
    metrics:
      receivers: [ ... ]
      processors: [ ... ]
      exporters: [prometheus, ... ] # Include prometheus in your metrics pipeline
    2. Logging: Configure the collector's logging within the service::telemetry::logs section of config.yaml: 
      service:
  telemetry:
    logs:
      level: info  # Or warn, error - avoid debug in production
      # ... other logging settings ...
    3. Testing: Verify that metrics are being exposed correctly (e.g., by querying the Prometheus endpoint) and that logs are being generated at the configured level.

  • Threats Mitigated:

    • Undetected Issues - Medium Severity: Allows for early detection of problems.
    • Delayed Response to Incidents - Medium Severity: Facilitates faster response.

  • Impact:

    • Undetected Issues: Significantly reduces risk.
    • Delayed Response to Incidents: Significantly reduces risk.

  • Currently Implemented (Example):

    • Metrics are exposed via the prometheus exporter.
    • Logging is enabled, but the log level is set to debug in production.

  • Missing Implementation (Example):

    • The log level should be changed to info or warn in production.