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Mitigation Strategies Analysis for timberio/vector

Mitigation Strategy: Implement TLS Encryption for All Vector Communication

Mitigation Strategy: Implement TLS Encryption for All Vector Communication

Description:

  • Step 1: Configure Vector Sinks for TLS: For each Vector sink that supports TLS (e.g., http, elasticsearch), enable TLS in the sink configuration. Specify the paths to TLS certificate, private key, and CA certificate if required for mutual TLS or custom CA verification. Example: in http sink, set tls.enabled: true, tls.key_path, tls.cert_path, and tls.ca_cert_path as needed.
  • Step 2: Configure Vector Sources for TLS (if applicable): If Vector is acting as a server (e.g., using http_listener source), configure TLS for the source to accept only encrypted connections. Set tls.enabled: true and provide necessary certificate paths in the source configuration.
  • Step 3: Enforce TLS for Internal Vector Communication (if applicable): If using Vector aggregator components, ensure TLS is enabled for communication between agents and aggregators, and between aggregators and sinks, by configuring TLS settings in listeners and connectors.

List of Threats Mitigated:

  • Data in Transit Interception (Confidentiality Breach): Severity: High. Unencrypted data transmitted by Vector can be intercepted and read by attackers on the network.
  • Man-in-the-Middle (MITM) Attacks: Severity: High. Without TLS, attackers can intercept and potentially modify data in transit between Vector components or between Vector and external systems.

Impact:

  • Data in Transit Interception (Confidentiality Breach): High reduction. TLS encryption makes intercepted data unreadable, significantly reducing the risk of confidentiality breaches.
  • Man-in-the-Middle (MITM) Attacks: High reduction. TLS with proper certificate verification prevents attackers from impersonating legitimate endpoints and manipulating data flow.

Currently Implemented: Partially - TLS is configured for Vector agents sending logs to our central Elasticsearch sink using the http sink with tls.enabled: true.

Missing Implementation:

  • TLS is not enforced for potential future internal communication between Vector agents and aggregators.
  • TLS is not configured for Vector sources if we were to use Vector as a data receiver (e.g., http_listener).
  • TLS might be missing for other sinks if they are added in the future and support TLS.

Mitigation Strategy: Utilize Data Masking and Redaction within Vector Pipelines

Mitigation Strategy: Utilize Data Masking and Redaction within Vector Pipelines

Description:

  • Step 1: Identify Sensitive Data Fields: Determine which fields in your logs, metrics, or traces contain sensitive information that needs to be protected.
  • Step 2: Implement Vector Transforms: In your Vector pipeline configuration, insert appropriate transforms like mask, regex_replace, or replace to target and modify the identified sensitive fields.
  • Step 3: Define Transformation Rules: Configure the transforms with specific rules (e.g., regular expressions, field names) to accurately mask or redact sensitive data. For example, use mask transform with a regex to replace email addresses with asterisks.
  • Step 4: Apply Transforms in Pipelines: Ensure these transforms are applied in the Vector pipeline before data is sent to sinks, ideally as early as possible in the processing stage.
  • Step 5: Test and Verify Transformations: Thoroughly test your Vector pipeline configuration with sample data to confirm that masking and redaction rules are working as expected and are not inadvertently affecting non-sensitive data.

List of Threats Mitigated:

  • Data Leakage through Logs/Metrics/Traces: Severity: High. Sensitive data present in logs, metrics, or traces can be exposed if sinks are compromised or accessed by unauthorized users.
  • Compliance Violations (e.g., GDPR, HIPAA, PCI DSS): Severity: High. Storing or transmitting sensitive data in logs without proper masking or redaction can lead to regulatory non-compliance.

Impact:

  • Data Leakage through Logs/Metrics/Traces: High reduction. Masking and redaction significantly reduce the risk of exposing sensitive data in sinks, even if sinks are compromised.
  • Compliance Violations: High reduction. By removing or obscuring sensitive data, the risk of violating data protection regulations is substantially lowered.

Currently Implemented: Partially - Basic masking is used for API keys in application logs using the mask transform in our Vector agent pipelines.

Missing Implementation:

  • Consistent redaction of PII (like email addresses, usernames, IP addresses) is not implemented across all log sources.
  • Masking or redaction is not applied to metrics or traces data.
  • More sophisticated redaction techniques (like tokenization or pseudonymization, if needed) are not explored.

Mitigation Strategy: Securely Manage Vector Configuration and Secrets within Vector

Mitigation Strategy: Securely Manage Vector Configuration and Secrets within Vector

Description:

  • Step 1: Utilize Vector's Secret Management Features (if available): Explore if Vector offers built-in secret management capabilities or integrations with external secret stores. (Note: Vector's built-in secret management might be limited, so focus on external integration if possible).
  • Step 2: Configure Vector to Read Secrets from Environment Variables or Files: If direct secret management integration is limited, configure Vector to read sensitive credentials (API keys, passwords) from environment variables or securely mounted files instead of hardcoding them in configuration files.
  • Step 3: Avoid Hardcoding Secrets in Configuration: Strictly avoid embedding secrets directly within Vector configuration files that are stored in version control or are easily accessible.
  • Step 4: Securely Store Configuration Files (excluding secrets): Store Vector configuration files (that do not contain secrets) in a secure location with appropriate access controls. Use version control for configuration files to track changes and enable rollback.

List of Threats Mitigated:

  • Exposure of Secrets in Configuration Files: Severity: High. Hardcoded secrets in Vector configuration files can be easily exposed if configuration files are compromised or accidentally leaked.
  • Unauthorized Access to Sinks and Sources: Severity: High. Compromised secrets can be used to gain unauthorized access to downstream sinks or upstream data sources that Vector interacts with.

Impact:

  • Exposure of Secrets in Configuration Files: High reduction. By avoiding hardcoding, the risk of directly exposing secrets through configuration files is eliminated.
  • Unauthorized Access to Sinks and Sources: High reduction. Securely managing secrets reduces the likelihood of secrets being compromised and misused for unauthorized access.

Currently Implemented: Partially - We are using Kubernetes Secrets to inject API keys as environment variables into Vector agent containers, which Vector reads.

Missing Implementation:

  • Vector configuration files themselves (excluding secrets) are not consistently stored in a version-controlled and access-controlled manner.
  • We are not fully leveraging potential Vector integrations with dedicated secret management solutions if available and beneficial.

Mitigation Strategy: Implement Rate Limiting and Traffic Shaping within Vector Pipelines

Mitigation Strategy: Implement Rate Limiting and Traffic Shaping within Vector Pipelines

Description:

  • Step 1: Identify Sinks with Rate Limits: Determine if your downstream sinks (e.g., logging platforms, monitoring systems) have rate limits or can be overwhelmed by excessive data volume.
  • Step 2: Configure Vector Rate Limiting Transforms: Use Vector's rate_limit transform in your pipelines to control the rate at which data is forwarded to sinks. Configure parameters like limit (events per second) and period.
  • Step 3: Implement Traffic Shaping with Vector Routing (if needed): If you have different types of data with varying priorities, use Vector's routing capabilities and potentially the filter transform to prioritize important data streams and shape traffic to sinks accordingly.
  • Step 4: Monitor Vector Performance and Sink Load: Monitor Vector's performance metrics and the load on your sinks to ensure rate limiting and traffic shaping configurations are effective and not causing data loss or performance bottlenecks.
  • Step 5: Adjust Rate Limits Dynamically (if possible): Explore if Vector allows for dynamic adjustment of rate limits based on sink health or other metrics for more adaptive traffic management.

List of Threats Mitigated:

  • Sink Overload and Denial of Service: Severity: Medium. Sudden spikes in data volume or malicious activity can overwhelm downstream sinks, leading to service disruptions or data loss.
  • Resource Exhaustion on Sinks: Severity: Medium. Uncontrolled data flow can exhaust resources (CPU, memory, network) on sinks, impacting their performance and stability.

Impact:

  • Sink Overload and Denial of Service: Medium reduction. Rate limiting and traffic shaping protect sinks from being overwhelmed by excessive data, improving their availability and resilience.
  • Resource Exhaustion on Sinks: Medium reduction. By controlling data flow, resource consumption on sinks is managed, preventing resource exhaustion and performance degradation.

Currently Implemented: No - Rate limiting is currently not implemented in our Vector pipelines.

Missing Implementation:

  • Rate limiting is not configured for any of our Vector sinks.
  • Traffic shaping based on data priority is not implemented.
  • Dynamic rate limit adjustments are not explored.

Mitigation Strategy: Validate and Sanitize Input Data within Vector Pipelines

Mitigation Strategy: Validate and Sanitize Input Data within Vector Pipelines

Description:

  • Step 1: Define Input Data Schemas: Clearly define the expected schema and data types for each data source that Vector ingests.
  • Step 2: Utilize Vector Validation Transforms: Implement Vector transforms like json_parser, logfmt_parser, or custom transforms with scripting languages (e.g., Lua) to parse and validate input data against the defined schemas.
  • Step 3: Implement Sanitization Transforms: Use Vector transforms like regex_replace, replace, or custom transforms to sanitize input data by removing or modifying potentially harmful or malformed data. This can include escaping special characters, removing invalid characters, or normalizing data formats.
  • Step 4: Handle Invalid Data: Configure Vector pipelines to handle invalid or malformed data appropriately. This could involve dropping invalid events, routing them to a separate "dead-letter queue" sink for investigation, or applying default values.
  • Step 5: Test Input Validation and Sanitization: Thoroughly test your Vector pipeline configuration with various types of input data, including valid, invalid, and potentially malicious data, to ensure validation and sanitization rules are effective.

List of Threats Mitigated:

  • Injection Attacks via Logs/Metrics/Traces: Severity: Medium. Malicious data injected into logs, metrics, or traces could potentially exploit vulnerabilities in downstream systems that process this data.
  • Data Corruption and Processing Errors: Severity: Medium. Malformed or invalid input data can cause errors in Vector pipelines or downstream systems, leading to data corruption or processing failures.

Impact:

  • Injection Attacks via Logs/Metrics/Traces: Medium reduction. Input validation and sanitization reduce the risk of malicious data being passed through Vector pipelines and potentially exploiting downstream systems.
  • Data Corruption and Processing Errors: Medium reduction. By ensuring data conforms to expected schemas and sanitizing potentially problematic data, the risk of data corruption and processing errors is lowered.

Currently Implemented: No - Input validation and sanitization are not explicitly implemented in our Vector pipelines.

Missing Implementation:

  • No explicit validation of input data schemas is performed in Vector pipelines.
  • Data sanitization beyond basic masking is not implemented.
  • Handling of invalid or malformed data is not explicitly configured; Vector might be dropping or misprocessing invalid data silently.