mitigations.md

Mitigation Strategies Analysis for grafana/loki

Mitigation Strategy: Rate Limiting Log Ingestion (Loki Distributor)

• Mitigation Strategy: Rate Limiting Log Ingestion (Loki Distributor)
• Description:
  1. Configure Distributor Limits: Within Loki's distributor configuration file (or command-line flags), define rate limits for log ingestion. This is typically done using parameters like ingestion_rate_mb (megabytes per second) and ingestion_burst_size_mb (burst size in megabytes).
  2. Set Limits Based on Capacity: Determine appropriate rate limits based on your Loki cluster's capacity and expected log volume. Consider peak loads and potential bursts.
  3. Monitor Ingestion Rate: Use Grafana dashboards or Loki's metrics endpoints to monitor the actual log ingestion rate and ensure it stays within configured limits.
  4. Adjust Limits as Needed: Periodically review and adjust rate limits based on monitoring data and changes in application logging behavior or Loki cluster capacity.
  5. Consider Tenant-Specific Limits (Multi-tenant Loki): If using Loki in a multi-tenant environment, configure tenant-specific rate limits to isolate tenants and prevent one tenant from impacting others through excessive logging.
• Threats Mitigated:
  • Denial of Service (DoS) - Ingestion Overload (High Severity): Attackers overwhelming Loki with excessive log data, causing performance degradation or system crashes of Loki components (especially distributors and ingesters).
• Impact:
  • DoS - Ingestion Overload: High risk reduction. Effectively prevents ingestion-based DoS attacks by limiting the amount of data Loki will accept at the distributor level.
• Currently Implemented:
  • Basic rate limiting is configured in the Loki distributor using ingestion_rate_mb and ingestion_burst_size_mb parameters in the distributor configuration file.
• Missing Implementation:
  • More granular rate limiting options within Loki distributor (e.g., lines per second, series per second) are not configured.
  • Tenant-specific rate limits are not implemented as the current deployment is single-tenant.
  • Alerting on Loki distributor rate limiting metrics is not fully configured.

Mitigation Strategy: Resource Limits and Quotas for Loki Components (Kubernetes/Deployment Configuration)

• Mitigation Strategy: Resource Limits and Quotas for Loki Components (Kubernetes/Deployment Configuration)
• Description:
  1. Define Resource Requests/Limits: In your Loki deployment manifests (e.g., Kubernetes YAML files, Docker Compose files), define resource requests and limits (CPU and memory) for each Loki component (ingesters, distributors, queriers, compactor).
  2. Set Limits Based on Component Needs: Determine appropriate resource requests and limits based on the expected load and resource requirements of each Loki component. Refer to Loki documentation and performance testing for guidance.
  3. Kubernetes Namespaces/ResourceQuotas (Kubernetes): In Kubernetes environments, consider using namespaces and ResourceQuotas to further isolate Loki components and manage resource consumption within the cluster.
  4. Monitor Resource Usage (Prometheus/Grafana): Utilize Prometheus metrics exposed by Loki components and Grafana dashboards to monitor the actual resource usage and ensure components are operating within defined limits.
  5. Adjust Limits Based on Monitoring: Periodically review and adjust resource requests and limits based on monitoring data and changes in Loki cluster load or application logging volume.
• Threats Mitigated:
  • Denial of Service (DoS) - Resource Exhaustion (High Severity): Attackers or misconfigured applications causing Loki components to consume excessive resources (CPU, memory) within the Loki cluster, leading to performance degradation or system crashes of Loki itself.
  • Resource Starvation (Medium Severity - Multi-tenant Loki): In multi-tenant Loki environments, one tenant's Loki components consuming excessive resources impacting other tenants' Loki performance.
• Impact:
  • DoS - Resource Exhaustion: High risk reduction. Prevents resource exhaustion within the Loki cluster by limiting the resources available to each Loki component through deployment configurations.
  • Resource Starvation: High risk reduction (in multi-tenant setups). Isolates resource usage and ensures fair resource allocation within the Loki cluster using Kubernetes namespaces and quotas.
• Currently Implemented:
  • Resource requests and limits (CPU and memory) are defined for Loki components in the Kubernetes deployment manifests.
• Missing Implementation:
  • Kubernetes ResourceQuotas are not explicitly configured for the Loki namespace, although namespace isolation is in place.
  • Resource limit tuning could be further optimized based on detailed performance testing and monitoring data of Loki components.

Mitigation Strategy: Query Limits and Throttling (Loki Querier)

• Mitigation Strategy: Query Limits and Throttling (Loki Querier)
• Description:
  1. Configure Querier Limits: Within Loki's querier configuration file (or command-line flags), define query limits. Key parameters include:
     • max_query_lookback: Maximum time range a query can span.
     • max_concurrent_queries: Limit on the number of concurrent queries the querier can handle.
     • max_query_length: Maximum length of a LokiQL query string.
     • max_entries_returned: Limit on the maximum number of log entries a query can return.
     • max_条数_per_query: (Note: This might be a typo and should be max_lines_per_query or similar, refer to Loki documentation for correct parameter). Limit on the number of log lines processed per query.
  2. Set Limits Based on Querier Capacity: Determine appropriate query limits based on your Loki querier's capacity and expected query load. Consider complex queries and concurrent user access.
  3. Implement Query Throttling (Using Rate Limiters): Configure query throttling mechanisms in front of the Loki querier (e.g., using an API gateway or load balancer with rate limiting capabilities) to limit the rate of incoming queries, especially from specific users or applications.
  4. Monitor Query Performance (Grafana/Loki Metrics): Monitor query performance metrics exposed by Loki queriers and Grafana dashboards to identify slow queries, resource-intensive queries, and potential query overload situations.
  5. Alerting for Limit Breaches/Slow Queries: Configure alerts to trigger when query limits are exceeded or when query performance degrades significantly, indicating potential DoS attempts or inefficient queries.
• Threats Mitigated:
  • Denial of Service (DoS) - Query Overload (High Severity): Attackers or inefficient queries overwhelming Loki queriers with excessive query load, causing performance degradation or system crashes of Loki querier components.
  • Resource Exhaustion - Query Driven (Medium Severity): Resource exhaustion within the Loki cluster caused by poorly constructed or overly broad LokiQL queries consuming excessive resources in queriers and ingesters.
• Impact:
  • DoS - Query Overload: High risk reduction. Prevents query-based DoS attacks against Loki queriers by limiting query execution parameters and concurrency at the querier level.
  • Resource Exhaustion - Query Driven: Medium risk reduction. Limits the impact of inefficient or malicious LokiQL queries on Loki cluster resources by enforcing query limits.
• Currently Implemented:
  • max_query_lookback and max_concurrent_queries are configured in the Loki querier configuration.
• Missing Implementation:
  • max_query_length, max_entries_returned, and max_条数_per_query (or equivalent lines/entries limit) are not configured in Loki querier.
  • Query throttling mechanisms in front of Loki queriers (e.g., API gateway rate limiting) are not implemented.
  • Detailed alerting for Loki querier query limit breaches and slow query performance is not fully set up.

Mitigation Strategy: Role-Based Access Control (RBAC) for Loki (Authentication and Authorization Configuration)

• Mitigation Strategy: Role-Based Access Control (RBAC) for Loki (Authentication and Authorization Configuration)
• Description:
  1. Enable Authentication (Loki Gateway/Frontend): Configure authentication for accessing Loki's query and API endpoints. This can be done at the Loki gateway level (if using one) or directly on Loki frontend/querier components. Options include:
     • Basic Authentication: Username/password based authentication (less secure, use for testing only).
     • API Keys: Token-based authentication for applications and automated access.
     • OAuth 2.0/OpenID Connect: Integration with identity providers for centralized authentication and authorization.
     • mTLS (Mutual TLS): Certificate-based authentication for secure client-server communication.
  2. Implement Authorization (Loki Authorizer/Gateway): Configure authorization rules to control access to specific log streams, labels, or tenants based on user roles or identities. This might involve:
     • Loki's built-in authorization (if available and feature-rich enough): Check Loki documentation for built-in RBAC capabilities.
     • External Authorizer/Policy Engine: Integrate Loki with an external authorization service or policy engine (e.g., Open Policy Agent - OPA) to enforce fine-grained access control policies.
     • API Gateway Authorization: Implement authorization rules at the API gateway level in front of Loki.
  3. Define Roles and Permissions: Define roles with specific permissions for accessing and querying Loki. Examples: read-only-logs, developer-logs, security-logs, admin-logs.
  4. Assign Roles to Users/Applications: Assign defined roles to users and applications based on their need to access Loki data.
  5. Enforce Least Privilege: Grant users and applications only the minimum necessary permissions to access Loki logs, adhering to the principle of least privilege.
  6. Regularly Review and Audit Access: Periodically review and audit Loki access control configurations, user roles, and permissions to ensure they remain aligned with security requirements and least privilege principles.
• Threats Mitigated:
  • Unauthorized Access to Logs (High Severity): Unauthorized users or applications gaining access to sensitive log data stored in Loki.
  • Data Exfiltration (Medium Severity): Unauthorized users with excessive access potentially exfiltrating sensitive log data from Loki.
  • Information Disclosure (Medium Severity): Accidental or intentional disclosure of sensitive information due to overly permissive access controls to Loki.
• Impact:
  • Unauthorized Access to Logs: High risk reduction. RBAC effectively restricts access to Loki logs based on defined roles and permissions, preventing unauthorized access.
  • Data Exfiltration: Medium risk reduction. Limits the scope of potential data exfiltration by restricting access to authorized users and applications only.
  • Information Disclosure: Medium risk reduction. Reduces the risk of accidental or intentional information disclosure by enforcing least privilege access to Loki data.
• Currently Implemented:
  • Basic authentication is enabled for Grafana access to Loki using Grafana's built-in authentication (which is external to Loki itself).
• Missing Implementation:
  • RBAC is not implemented within Loki itself. Access control is currently primarily managed at the Grafana level, which is less granular and Loki-aware.
  • No integration with a centralized authentication provider (OAuth 2.0, OpenID Connect, LDAP) for Loki API access.
  • Fine-grained authorization based on log streams, labels, or tenants within Loki is not implemented.
  • No external authorizer or policy engine is integrated with Loki for advanced RBAC.

Mitigation Strategy: Encryption at Rest for Loki Storage Backend (Storage Configuration)

• Mitigation Strategy: Encryption at Rest for Loki Storage Backend (Storage Configuration)
• Description:
  1. Choose Storage Encryption Method: Select an encryption method supported by your chosen storage backend for Loki (e.g., object storage like AWS S3, Google Cloud Storage, or local filesystem).
     • Storage Provider Encryption (Recommended for Object Storage): Utilize server-side encryption features provided by the object storage provider (e.g., AWS S3 Server-Side Encryption - SSE, Google Cloud Storage Encryption). This is often the easiest and most robust option for object storage.
     • Filesystem Level Encryption (for Local Storage): If using local filesystem storage for Loki (less common in production), use operating system level encryption tools like LUKS (Linux Unified Key Setup) or dm-crypt to encrypt the underlying filesystem.
  2. Configure Storage Encryption: Configure the chosen encryption method within your storage backend settings. For object storage, this is typically enabled through the storage provider's console or API. For filesystem encryption, configure it at the OS level.
  3. Key Management (Storage Provider/Key Management Service): Implement secure key management practices for encryption keys.
     • Storage Provider Managed Keys (easiest for object storage): Let the storage provider manage encryption keys (e.g., AWS S3 SSE-S3).
     • Customer Managed Keys (more control, more complexity): Use customer-managed keys stored in a Key Management Service (KMS) for greater control over key lifecycle and access (e.g., AWS KMS, Google Cloud KMS, HashiCorp Vault).
  4. Verify Encryption Status: Verify that encryption at rest is properly configured and active for Loki's storage backend. Check storage provider console or use command-line tools to confirm encryption status.
  5. Regular Key Rotation (KMS): If using customer-managed keys, implement regular key rotation policies in your KMS to enhance security.
• Threats Mitigated:
  • Data Exfiltration - Storage Compromise (High Severity): If Loki's storage backend is compromised (e.g., unauthorized access to object storage buckets or physical access to storage media), encryption at rest protects sensitive log data from unauthorized access.
  • Data Breach - Physical Security (Medium Severity): In case of physical theft of storage media containing Loki data, encryption prevents access to log data without the decryption key.
• Impact:
  • Data Exfiltration - Storage Compromise: High risk reduction. Encryption renders log data unreadable without the decryption key, significantly mitigating the impact of storage compromise.
  • Data Breach - Physical Security: Medium risk reduction. Protects data in case of physical media theft by making it inaccessible without decryption keys.
• Currently Implemented:
  • Storage provider encryption (AWS S3 Server-Side Encryption using S3-managed keys - SSE-S3) is enabled for Loki's object storage backend on AWS S3.
• Missing Implementation:
  • Customer-managed keys (e.g., using AWS KMS) are not implemented for Loki's storage encryption, which would provide more control over key management.
  • Key rotation policies for storage encryption keys are not explicitly configured beyond AWS S3's default key rotation (if any for SSE-S3).

Mitigation Strategy: Regular Updates and Patching of Loki Components (Operational Security)

• Mitigation Strategy: Regular Updates and Patching of Loki Components (Operational Security)
• Description:
  1. Monitor Loki Releases and Security Advisories: Regularly monitor Grafana Labs' release notes, security advisories, and community channels for announcements of new Loki versions and security vulnerabilities.
  2. Establish Patching Schedule: Define a regular schedule for updating Loki components (ingesters, distributors, queriers, compactor, Promtail if managed centrally) to the latest stable versions, prioritizing security patches.
  3. Test Updates in Staging Environment: Before deploying updates to production Loki clusters, thoroughly test them in a staging or non-production environment to identify and resolve any compatibility issues or unexpected behavior.
  4. Automate Update Process (Infrastructure as Code/Automation Tools): Automate the Loki update process using infrastructure-as-code tools (e.g., Terraform, Ansible, Kubernetes Operators) and automation pipelines to ensure consistent and timely patching.
  5. Track Component Versions (Inventory Management): Maintain an inventory of Loki component versions deployed in each environment to easily track patch status and identify outdated components that need updating.
• Threats Mitigated:
  • Exploitation of Known Vulnerabilities in Loki (High Severity): Attackers exploiting publicly known security vulnerabilities in outdated Loki components to gain unauthorized access to Loki, cause DoS attacks against Loki, or potentially exfiltrate log data.
• Impact:
  • Exploitation of Known Vulnerabilities in Loki: High risk reduction. Regularly patching Loki components eliminates known attack vectors and significantly reduces the risk of exploitation of known vulnerabilities within the Loki system itself.
• Currently Implemented:
  • A manual process exists for updating Loki components, but it is not consistently applied on a regular schedule.
• Missing Implementation:
  • No automated patch management process for Loki components.
  • No formal subscription to Grafana Labs security advisories or release channels.
  • Consistent testing of Loki updates in a staging environment before production deployment is not always performed.
  • Systematic tracking of Loki component versions across environments is not implemented.