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Attack Surface Analysis for cortexproject/cortex

Attack Surface: Metric Injection Attacks

  • Description: Attackers inject malicious or malformed metric data into the Cortex ingestion pipeline.
  • Cortex Contribution: Cortex's distributed nature and ingestion pipeline, especially ingesters, are designed to receive and process large volumes of metrics. This pipeline can be targeted for injection attacks.
  • Example: An attacker sends metrics with extremely long label names or values to an ingester, causing excessive memory consumption and leading to an Out-of-Memory (OOM) error, effectively DoS-ing the ingester.
  • Impact:
    • Denial of Service (DoS) of Cortex ingestion and query services.
    • Resource exhaustion (CPU, memory, disk I/O) on ingesters and potentially other components.
    • Data corruption or instability within Cortex.
  • Risk Severity: High
  • Mitigation Strategies:
    • Input Validation and Sanitization: Implement strict validation on incoming metrics at the distributor and ingester levels. Limit label name and value lengths, and restrict allowed characters.
    • Resource Limits: Configure resource limits (CPU, memory) for ingesters to prevent resource exhaustion from malicious metrics.
    • Rate Limiting: Implement rate limiting on metric ingestion at the distributor or gateway level to prevent overwhelming the system.
    • Anomaly Detection: Implement anomaly detection mechanisms to identify and potentially reject suspicious metric patterns.

Attack Surface: PromQL Injection

  • Description: Attackers inject malicious PromQL queries, exploiting vulnerabilities in query processing or insufficient input sanitization.
  • Cortex Contribution: Cortex relies heavily on PromQL for querying metrics. If user-provided input is directly used in PromQL queries without proper sanitization, it becomes vulnerable to injection.
  • Example: An application allows users to filter metrics based on a user-provided label value. An attacker crafts a malicious label value like label=~"user-.*|.*" which, when incorporated into a PromQL query, could become highly resource-intensive, causing a DoS on queriers. Or, an attacker might attempt to use functions like label_replace in unexpected ways to extract data they shouldn't have access to.
  • Impact:
    • Denial of Service (DoS) of Cortex query services due to resource-intensive queries.
    • Data exfiltration by crafting queries to access sensitive metrics beyond intended access.
    • Information disclosure by gaining insights into internal system metrics or configurations.
  • Risk Severity: High
  • Mitigation Strategies:
    • Input Sanitization and Validation: Thoroughly sanitize and validate all user-provided input before incorporating it into PromQL queries. Use parameterized queries or query builders to avoid direct string concatenation.
    • Principle of Least Privilege: Restrict user access to only the metrics and data they need. Implement granular authorization policies.
    • Query Analysis and Limits: Analyze incoming PromQL queries for complexity and resource usage. Implement query limits (e.g., maximum query duration, series limit, memory limit) to prevent resource exhaustion.
    • PromQL Security Review: Regularly review PromQL usage patterns and potential injection points in applications interacting with Cortex.

Attack Surface: Multi-tenancy Isolation Issues

  • Description: Vulnerabilities in Cortex's multi-tenancy implementation allow one tenant to access data or resources belonging to another tenant.
  • Cortex Contribution: Cortex is designed for multi-tenancy, and relies on tenant IDs for data isolation. Weaknesses in tenant ID handling or enforcement can lead to isolation breaches.
  • Example: A misconfiguration or vulnerability in the distributor or querier components could allow requests from one tenant to be processed under the context of another tenant, leading to cross-tenant data access.
  • Impact:
    • Cross-tenant data leakage and unauthorized access to sensitive metric data.
    • Cross-tenant resource exhaustion, where one tenant can impact the performance or availability of other tenants.
    • Compliance violations and reputational damage due to data breaches.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Strict Tenant ID Enforcement: Ensure tenant IDs are consistently and rigorously enforced across all Cortex components (distributor, ingester, querier, etc.).
    • Authentication and Authorization: Implement robust authentication and authorization mechanisms to verify tenant identity and control access to resources based on tenant ID.
    • Regular Security Audits: Conduct regular security audits and penetration testing specifically focused on multi-tenancy isolation to identify and address potential vulnerabilities.
    • Configuration Review: Regularly review Cortex configuration to ensure proper multi-tenancy settings are in place and no misconfigurations exist that could weaken isolation.

Attack Surface: Authentication and Authorization Bypass

  • Description: Attackers bypass authentication or authorization mechanisms to gain unauthorized access to Cortex components or data.
  • Cortex Contribution: Cortex components expose HTTP APIs for various operations. If authentication and authorization are not correctly implemented or are vulnerable, these APIs become attack vectors.
  • Example: A Cortex deployment fails to properly configure authentication on the querier's API. An attacker can directly access the querier API without credentials and query all metrics, bypassing intended access controls. Or, a vulnerability in the authentication middleware allows bypassing authentication checks.
  • Impact:
    • Unauthorized access to sensitive metric data.
    • Unauthorized modification or deletion of metric data, rules, or configurations.
    • Denial of Service (DoS) by unauthorized users disrupting Cortex operations.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Enable and Enforce Authentication: Always enable and enforce authentication on all Cortex component APIs (distributor, ingester, querier, ruler, alertmanager, etc.). Use strong authentication methods like OAuth 2.0, OpenID Connect, or mutual TLS.
    • Implement Role-Based Access Control (RBAC): Implement RBAC to control access to Cortex resources based on user roles and permissions. Define granular roles with least privilege access.
    • Secure API Gateways: Use secure API gateways in front of Cortex components to handle authentication, authorization, and rate limiting.
    • Regular Security Testing: Conduct regular security testing and vulnerability scanning of Cortex APIs and authentication/authorization mechanisms.

Attack Surface: Dependency Vulnerabilities

  • Description: Vulnerabilities in third-party libraries and dependencies used by Cortex components are exploited by attackers.
  • Cortex Contribution: Cortex, like most software, relies on numerous open-source libraries and dependencies. Vulnerabilities in these dependencies can indirectly affect Cortex security.
  • Example: A critical vulnerability is discovered in a widely used Go library that Cortex depends on. If Cortex is not updated to a patched version, attackers could exploit this vulnerability to potentially gain remote code execution or cause other security breaches.
  • Impact:
    • Remote Code Execution (RCE) on Cortex components.
    • Denial of Service (DoS) due to vulnerable dependencies.
    • Data breaches or information disclosure.
  • Risk Severity: High to Critical (depending on the vulnerability)
  • Mitigation Strategies:
    • Dependency Scanning and Management: Implement automated dependency scanning tools to identify known vulnerabilities in Cortex dependencies.
    • Regular Updates and Patching: Keep Cortex and its dependencies up-to-date with the latest security patches. Establish a process for timely patching of vulnerabilities.
    • Software Composition Analysis (SCA): Use SCA tools to analyze Cortex's codebase and dependencies for security risks and license compliance.
    • Vulnerability Monitoring: Subscribe to security advisories and vulnerability databases to stay informed about new vulnerabilities affecting Cortex dependencies.