sec-design-deep-analysis.md

Okay, let's perform a deep security analysis of the Cortex project based on the provided design review.

1. Objective, Scope, and Methodology

Objective:

The primary objective is to conduct a thorough security analysis of the Cortex architecture, its key components, and their interactions. This analysis aims to identify potential security vulnerabilities, assess their impact, and propose specific, actionable mitigation strategies. The focus is on ensuring the confidentiality, integrity, and availability of metrics data and the Cortex service itself, considering its multi-tenant nature and reliance on external services. We will pay particular attention to:

• Data breaches: Unauthorized access to metrics data.
• Data loss: Loss of historical metrics.
• Service disruption: Outages or performance degradation.
• Compromise of Cortex components: Attackers gaining control of parts of the system.
• Multi-tenancy violations: One tenant accessing another tenant's data.

Scope:

The scope of this analysis includes:

• All Cortex components as described in the C4 Container diagram (Distributor, Ingester, Querier, Query Frontend, Ruler, Compactor, Store Gateway).
• Interactions between Cortex components.
• Interactions with external systems (Prometheus, Grafana, Alertmanager, Object Storage, Key-Value Store).
• The Kubernetes deployment model.
• The build process using Go modules and GitHub Actions.
• Authentication, authorization, and encryption mechanisms.
• Input validation and rate limiting.
• Dependency management.

The scope excludes a detailed security review of the external systems (Object Storage, Key-Value Store, Prometheus, Grafana, Alertmanager) themselves, except where their configuration directly impacts Cortex's security. We assume these external systems have their own security measures in place, but we will highlight integration points where vulnerabilities could arise.

Methodology:

1. Architecture Review: Analyze the provided C4 diagrams and descriptions to understand the system's architecture, data flow, and component interactions. Infer potential attack surfaces based on this understanding.
2. Codebase Inference: Although we don't have direct access to the codebase, we will use the GitHub repository information (https://github.com/cortexproject/cortex) and available documentation to infer implementation details relevant to security. This includes examining configuration options, API definitions, and common code patterns.
3. Threat Modeling: Identify potential threats based on the business risks, security posture, and architecture. We will use a combination of STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) and attack trees to systematically identify threats.
4. Vulnerability Analysis: Assess the likelihood and impact of each identified threat, considering existing security controls.
5. Mitigation Recommendations: Propose specific, actionable, and tailored mitigation strategies to address the identified vulnerabilities. These recommendations will be prioritized based on their impact and feasibility.

2. Security Implications of Key Components

We'll analyze each component from the C4 Container diagram, focusing on security implications and potential vulnerabilities.

• Distributor:

  • Security Implications: The entry point for all write requests. A critical component for availability and data integrity. Handles authentication and routing.
  • Potential Vulnerabilities:
    • DoS/DDoS: Vulnerable to denial-of-service attacks if rate limiting is not properly configured or if the hashing algorithm is predictable, allowing an attacker to overload specific ingesters.
    • Input Validation Bypass: If input validation is flawed, malicious payloads could be injected, potentially leading to code execution or data corruption in the ingesters.
    • Authentication Bypass: Weaknesses in authentication mechanisms (basic auth, JWT, TLS client certs) could allow unauthorized clients to send data.
    • Spoofing: An attacker could impersonate a legitimate Prometheus instance.
    • Information Disclosure: Error messages or logging could reveal sensitive information about the internal architecture or configuration.
  • Mitigation Strategies:
    • Robust Rate Limiting: Implement strict, per-tenant and global rate limiting, potentially using adaptive rate limiting to handle bursts. Consider using a dedicated rate-limiting service.
    • Comprehensive Input Validation: Validate all incoming data (headers, payloads) against a strict schema. Use a well-vetted library for parsing Prometheus data. Sanitize all inputs.
    • Strengthen Authentication: Prefer TLS client certificates with mutual authentication (mTLS) over basic auth or JWT. If using JWT, ensure proper signature validation and short token lifetimes. Integrate with a robust identity provider.
    • IP Whitelisting/Network Policies: Restrict access to the Distributor to known Prometheus instances using Kubernetes network policies or firewall rules.
    • Secure Logging: Configure logging to avoid exposing sensitive information. Implement log redaction if necessary. Monitor logs for suspicious activity.
    • Hash Algorithm Review: Ensure the consistent hashing algorithm used for distributing samples is cryptographically secure and resistant to manipulation.

• Ingester:

  • Security Implications: Responsible for writing data to storage. A compromise could lead to data loss, corruption, or unauthorized data modification.
  • Potential Vulnerabilities:
    • Resource Exhaustion: Insufficient resources (CPU, memory, disk I/O) could lead to crashes or data loss.
    • Data Corruption: Bugs in the chunk creation logic could lead to corrupted data being written to storage.
    • Unauthorized Data Modification: If an attacker gains access to an Ingester, they could modify or delete data.
    • Dependency Issues: Vulnerabilities in libraries used for interacting with object storage or the key-value store.
  • Mitigation Strategies:
    • Resource Limits: Set appropriate resource limits (CPU, memory) in Kubernetes to prevent resource exhaustion. Implement horizontal pod autoscaling (HPA).
    • Data Integrity Checks: Implement checksums or other data integrity checks to detect and prevent data corruption.
    • Least Privilege: Run the Ingester with the minimum necessary privileges. Use Kubernetes service accounts with limited access to the object storage and key-value store.
    • Dependency Scanning: Regularly scan dependencies for known vulnerabilities using tools like snyk or dependabot.
    • Immutable Infrastructure: Treat Ingester pods as immutable. Any configuration changes should result in a new deployment.

• Querier:

  • Security Implications: Handles read requests. A compromise could lead to data breaches or denial of service.
  • Potential Vulnerabilities:
    • DoS: Complex or resource-intensive queries could overwhelm the Querier, leading to denial of service.
    • Data Exfiltration: An attacker could craft queries to extract sensitive data if authorization is not properly enforced.
    • Injection Attacks: Vulnerabilities in PromQL parsing could allow attackers to inject malicious code.
    • Information Disclosure: Error messages or logging could reveal sensitive information.
  • Mitigation Strategies:
    • Query Limits: Implement limits on query complexity, execution time, and the amount of data returned. Reject overly complex or expensive queries.
    • Strict Authorization: Enforce fine-grained authorization based on tenant and user roles. Ensure that users can only access data they are authorized to see.
    • Secure PromQL Parsing: Use a well-vetted and secure PromQL parser. Consider using a parser that is specifically designed to prevent injection attacks.
    • Secure Logging: Similar to the Distributor, avoid exposing sensitive information in logs.
    • Input Sanitization: Sanitize all query inputs before processing.

• Query Frontend:

  • Security Implications: Provides caching and query splitting. A compromise could impact performance and potentially expose cached data.
  • Potential Vulnerabilities:
    • Cache Poisoning: An attacker could inject malicious data into the cache, leading to incorrect results being served to other users.
    • DoS: Overwhelming the cache with requests could lead to denial of service.
    • Information Disclosure: If the cache is not properly secured, an attacker could access cached data.
  • Mitigation Strategies:
    • Cache Validation: Validate cached data before serving it to users. Use checksums or other integrity checks.
    • Rate Limiting: Implement rate limiting to prevent cache abuse.
    • Secure Cache Storage: Use a secure cache storage mechanism (e.g., in-memory cache with encryption, or a dedicated caching service).
    • Cache Key Isolation: Ensure cache keys are properly scoped to tenants to prevent cross-tenant data leakage.

• Ruler:

  • Security Implications: Evaluates recording and alerting rules. A compromise could lead to incorrect alerts or the execution of malicious code.
  • Potential Vulnerabilities:
    • Code Injection: Vulnerabilities in PromQL evaluation could allow attackers to inject malicious code through recording or alerting rules.
    • DoS: Complex or resource-intensive rules could overwhelm the Ruler.
    • Unauthorized Rule Modification: An attacker could modify rules to suppress alerts or trigger false alerts.
  • Mitigation Strategies:
    • Secure PromQL Evaluation: Similar to the Querier, use a secure PromQL evaluator. Consider sandboxing the evaluation environment.
    • Rule Limits: Implement limits on rule complexity and execution time.
    • RBAC for Rules: Implement role-based access control for managing recording and alerting rules. Only authorized users should be able to create, modify, or delete rules.
    • Audit Logging of Rule Changes: Track all changes to rules, including who made the change and when.

• Compactor:

  • Security Implications: Optimizes data storage. A compromise could lead to data loss or corruption.
  • Potential Vulnerabilities:
    • Data Corruption: Bugs in the compaction logic could lead to data loss or corruption.
    • Resource Exhaustion: Compaction can be resource-intensive, potentially leading to denial of service.
    • Unauthorized Data Access: If an attacker gains access to a Compactor, they could potentially read or modify data.
  • Mitigation Strategies:
    • Data Integrity Checks: Implement thorough data integrity checks before and after compaction.
    • Resource Limits: Set appropriate resource limits for the Compactor.
    • Least Privilege: Run the Compactor with the minimum necessary privileges.

• Store Gateway:

  • Security Implications: Direct interface to object storage for querying. A compromise could lead to data breaches.
  • Potential Vulnerabilities:
    • Data Exfiltration: An attacker could bypass the Querier and directly access data in object storage if authorization is not properly enforced.
    • DoS: Excessive requests to the Store Gateway could overwhelm it or the object storage.
  • Mitigation Strategies:
    • Strict Authorization: Enforce strict authorization, ensuring that only authorized users and components can access the Store Gateway. Leverage IAM roles and policies in the cloud provider.
    • Rate Limiting: Implement rate limiting to prevent abuse.
    • Network Segmentation: Isolate the Store Gateway from other components using network policies.

3. Architecture, Components, and Data Flow (Inferences)

Based on the documentation and GitHub repository, we can infer the following:

• Communication: Cortex components primarily communicate using gRPC, which typically uses TLS for encryption. HTTP is also used, particularly for external interfaces (Prometheus, Grafana).
• Configuration: Cortex is highly configurable, with numerous flags and options. This complexity increases the risk of misconfiguration.
• API: Cortex exposes a well-defined API for reading and writing metrics. This API is a key attack surface.
• Multi-tenancy: Cortex uses tenant IDs to isolate data between different users or teams. This is a critical security feature.
• Object Storage Interaction: Cortex relies heavily on object storage (S3, GCS, etc.) for storing chunks. The security of this interaction is crucial.
• Key-Value Store Interaction: Cortex uses a key-value store (Consul, Etcd) to store the index and metadata. The security of this interaction is also crucial.

4. Specific Security Considerations (Tailored to Cortex)

• Multi-tenancy Isolation: This is the most critical security consideration for Cortex. Any vulnerability that allows one tenant to access another tenant's data is a major security breach. This requires rigorous enforcement of tenant IDs in all components, particularly the Distributor, Querier, and Store Gateway.
• PromQL Security: PromQL is a powerful query language, and vulnerabilities in its parsing or evaluation could have severe consequences. This is particularly relevant for the Querier and Ruler.
• Object Storage Security: The security of the object storage is paramount. Cortex relies on the object storage provider's security controls (encryption, access control, etc.). Misconfiguration of these controls could lead to data breaches.
• Key-Value Store Security: The key-value store contains the index, which is essential for querying data. Compromise of the key-value store could lead to data loss or denial of service.
• Dependency Management: Cortex has numerous dependencies. Regularly scanning for and updating vulnerable dependencies is crucial.
• Configuration Management: Cortex's complex configuration makes it prone to misconfiguration. Using a secure and auditable configuration management system is essential.
• Kubernetes Security: The Kubernetes deployment introduces its own set of security considerations, such as network policies, pod security policies, and RBAC.

5. Actionable Mitigation Strategies (Tailored to Cortex)

In addition to the component-specific mitigations listed above, here are some overarching, actionable strategies:

• Mandatory mTLS: Enforce mutual TLS (mTLS) for all internal communication between Cortex components. This provides strong authentication and encryption. Use a service mesh like Istio or Linkerd to simplify mTLS management.
• Network Policies: Implement strict Kubernetes network policies to isolate Cortex components from each other and from the outside world. Only allow necessary communication paths.
• Pod Security Policies (or equivalent): Use Pod Security Policies (or their successor, Pod Security Admission) to restrict the capabilities of Cortex pods. For example, prevent pods from running as root, mounting host volumes, or accessing sensitive network resources.
• RBAC: Implement fine-grained role-based access control (RBAC) within Kubernetes and within Cortex itself. Grant users and components only the minimum necessary privileges.
• Secret Management: Use a robust secrets management solution (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault) to store and manage sensitive information, such as credentials for accessing object storage and the key-value store. Never store secrets directly in configuration files or environment variables.
• Configuration Validation: Implement automated configuration validation to detect and prevent common misconfigurations. Use a tool like kubeval or a custom validation script.
• Regular Auditing: Regularly audit Cortex configurations, Kubernetes resources, and access logs. Use a SIEM (Security Information and Event Management) system to collect and analyze logs.
• Vulnerability Scanning: Regularly scan Cortex Docker images and dependencies for known vulnerabilities. Use a container image scanning tool (e.g., Trivy, Clair) and a dependency scanning tool (e.g., Snyk, Dependabot).
• Penetration Testing: Conduct regular penetration testing to identify vulnerabilities that may be missed by automated tools.
• PromQL Sandboxing: Explore options for sandboxing PromQL evaluation, particularly in the Ruler. This could involve using a restricted execution environment or a custom PromQL parser with built-in security controls.
• Tenant Isolation Verification: Implement specific tests to verify tenant isolation. These tests should attempt to access data from other tenants and confirm that access is denied.
• Object Storage and Key-Value Store Best Practices: Follow the security best practices for the chosen object storage and key-value store providers. This includes enabling encryption at rest, using strong access control policies, and regularly auditing configurations.
• Supply Chain Security:
  • Signed Commits: Enforce signed commits in the GitHub repository.
  • Software Bill of Materials (SBOM): Generate and maintain an SBOM for Cortex to track all dependencies and their versions.
  • Container Image Signing: Sign Docker images to ensure their integrity and authenticity. Use tools like Notary or Cosign.
• WAF (Web Application Firewall): Deploy a WAF in front of the Query Frontend to protect against common web attacks, such as cross-site scripting (XSS) and SQL injection. Although PromQL is not SQL, a WAF can still provide valuable protection against other types of attacks.
• Alerting on Security Events: Configure alerting for security-relevant events, such as failed authentication attempts, unauthorized access attempts, and changes to critical configurations.

This deep analysis provides a comprehensive overview of the security considerations for the Cortex project. By implementing these mitigation strategies, the development team can significantly improve the security posture of Cortex and protect it against a wide range of threats. Remember that security is an ongoing process, and regular reviews and updates are essential to maintain a strong security posture.