sec-design-deep-analysis.md

Deep Security Analysis of Cortex - Horizontally Scalable Prometheus as a Service

1. Objective, Scope, and Methodology

1.1. Objective:

The primary objective of this deep security analysis is to thoroughly evaluate the security posture of Cortex, a horizontally scalable Prometheus-as-a-Service platform, based on its architecture and component design as outlined in the provided Security Design Review document and inferred from the codebase (https://github.com/cortexproject/cortex). This analysis aims to identify potential security vulnerabilities, threats, and weaknesses within the Cortex system and propose specific, actionable mitigation strategies tailored to its unique architecture and operational context.

1.2. Scope:

This analysis encompasses the following key components of Cortex, as described in the Security Design Review:

• Ingestion Path: Distributor, Ingester
• Query Path: Query Frontend, Querier
• Storage Layer: Store Gateway, Compactor, Object Storage
• Operational Components: Ruler, Alerter, Gossip Ring

The analysis will focus on the security implications of these components, their interactions, and the overall data flow within Cortex. It will consider aspects of authentication, authorization, tenant isolation, data confidentiality, integrity, availability, and compliance. The analysis will be limited to the information available in the provided design document and publicly accessible information about Cortex, including its GitHub repository. Deep code review is outside the scope, but architectural inferences will be drawn from the project's design principles and component descriptions.

1.3. Methodology:

The methodology for this deep security analysis will involve the following steps:

1. Document Review and Architecture Inference: In-depth review of the provided Security Design Review document to understand the architecture, components, data flow, and initial security considerations. Inferring the underlying architecture and component interactions based on the descriptions and the project's cloud-native, distributed nature. Leveraging knowledge of common patterns in distributed systems and time-series databases.
2. Component-Level Security Analysis: Breaking down each key component of Cortex and analyzing its specific security responsibilities, potential threats, and vulnerabilities. This will involve considering the STRIDE threat model (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) implicitly, as suggested by the design document's threat categorization.
3. Data Flow Security Analysis: Analyzing the ingestion and query data flows to identify potential security risks at each stage of data processing and transmission.
4. Threat Modeling and Mitigation Strategy Development: Based on the component and data flow analysis, identifying specific threats relevant to Cortex and developing tailored, actionable mitigation strategies for each identified threat. These strategies will be specific to Cortex's architecture and operational context, avoiding generic security recommendations.
5. Actionable Recommendations: Formulating concrete, actionable recommendations for the development team to enhance the security of Cortex. These recommendations will be practical, implementable, and prioritized based on their potential impact and feasibility.

2. Security Implications of Key Components

2.1. Distributor:

• Security Focus: Tenant Authentication & Authorization, Input Validation, DoS Prevention, Secure Gossip Communication.
• Security Implications:
  • Tenant Authentication Bypass/Spoofing: If tenant authentication is weak or bypassed, malicious actors could inject metrics under the guise of legitimate tenants, leading to data pollution, unauthorized resource consumption, and potential cross-tenant interference.
  • Input Validation Failures: Lack of robust input validation on incoming metrics (metric names, labels, values) could allow injection attacks (e.g., manipulating labels to bypass tenant isolation, injecting malformed data to crash downstream components).
  • Denial of Service (DoS): The Distributor is the entry point for all write requests. Without proper rate limiting and admission control, it is vulnerable to DoS attacks that could overwhelm the system and prevent legitimate metric ingestion.
  • Gossip Ring Vulnerabilities: If the Gossip Ring communication is not secured, attackers could inject malicious gossip messages, potentially disrupting service discovery, load balancing, and overall cluster stability.
• Specific Threats: Tenant ID spoofing, metric injection attacks, DoS attacks via write floods, unauthorized access to gossip information, manipulation of gossip ring membership.

2.2. Ingester:

• Security Focus: Memory Management, Data Integrity, Access Control for Recent Data Queries, Secure Gossip Communication.
• Security Implications:
  • Memory Exhaustion Attacks: If not properly managed, memory usage in Ingesters could be exploited to cause DoS. Malicious or poorly behaving tenants could send excessive metrics, leading to memory exhaustion and Ingester crashes.
  • Data Corruption: Data corruption in memory or during flushing to storage could lead to inaccurate metrics and unreliable monitoring. This could be caused by software bugs or malicious manipulation if access controls are weak.
  • Unauthorized Access to Recent Data: If access control for querying recent data from Ingesters is insufficient, unauthorized tenants or internal components could gain access to sensitive metric data.
  • Gossip Ring Vulnerabilities: Similar to the Distributor, vulnerabilities in the Gossip Ring communication could impact Ingester availability and data consistency.
• Specific Threats: Memory exhaustion DoS, data corruption in memory or during flushing, unauthorized access to in-memory data, vulnerabilities in chunk encoding/decoding leading to data corruption or exploits, resource exhaustion due to inefficient chunk handling.

2.3. Query Frontend:

• Security Focus: Cache Security, Input Validation (PromQL), DoS Prevention, Access Control to Cached Data.
• Security Implications:
  • Cache Poisoning: If the query cache is not properly secured, attackers could poison the cache with malicious or incorrect query results, leading to misleading data being served to users.
  • Information Leakage via Cached Data: If tenant isolation is not strictly enforced in the cache, cached query results from one tenant could potentially be accessed by another tenant, leading to information disclosure.
  • PromQL Injection Attacks: Vulnerabilities in PromQL parsing and execution within the Query Frontend could be exploited for injection attacks, potentially allowing attackers to bypass access controls or extract sensitive data.
  • DoS via Excessive Queries: Without query rate limiting and concurrency control, the Query Frontend could be overwhelmed by excessive query requests, leading to DoS and impacting query performance for all tenants.
• Specific Threats: Cache poisoning, cross-tenant information leakage via cache, PromQL injection attacks, DoS attacks via query floods, unauthorized access to cached query results.

2.4. Querier:

• Security Focus: PromQL Engine Security, Data Access Control, Resource Management, Secure Communication with Ingesters/Store Gateways, Secure Gossip Communication.
• Security Implications:
  • PromQL Engine Vulnerabilities: Bugs or vulnerabilities in the PromQL engine could be exploited for code execution, information disclosure, or DoS attacks.
  • Unauthorized Data Access: If tenant data isolation and access control within the Querier are not robust, cross-tenant data access could occur, leading to data breaches and privacy violations.
  • Resource Exhaustion due to Complex Queries: Malicious or poorly written PromQL queries could consume excessive resources (CPU, memory, I/O) in the Querier, leading to performance degradation or DoS for other tenants.
  • Insecure Communication Channels: If communication between Queriers and Ingesters/Store Gateways is not encrypted and authenticated, man-in-the-middle attacks could occur, potentially leading to data interception or manipulation.
  • Gossip Ring Vulnerabilities: Similar to other components, Gossip Ring vulnerabilities can impact Querier availability and data consistency.
• Specific Threats: PromQL engine vulnerabilities, cross-tenant data access, resource exhaustion due to complex queries, insecure communication with data sources, manipulation of query results, DoS via crafted PromQL queries.

2.5. Store Gateway:

• Security Focus: Object Storage Access Control, Tenant Data Isolation in Storage, Data Encryption at Rest (Object Storage), Cache Security, Secure Gossip Communication.
• Security Implications:
  • Object Storage Credential Compromise: If Store Gateway's object storage credentials are compromised, attackers could gain unauthorized access to all stored metric data, leading to massive data breaches.
  • Cross-Tenant Data Access in Object Storage: Misconfigurations in object storage access control policies could allow Store Gateways to access data belonging to other tenants, violating tenant isolation.
  • Data Breaches due to Lack of Encryption: If data at rest in object storage is not encrypted, data breaches could occur if the storage system is compromised or misconfigured.
  • Cache-Based Information Leakage: Similar to Query Frontend, cache vulnerabilities in Store Gateway could lead to information leakage between tenants.
  • Gossip Ring Vulnerabilities: Gossip Ring issues can affect Store Gateway availability and its ability to serve data.
• Specific Threats: Object storage credential compromise, cross-tenant data access in object storage, data breaches due to lack of encryption at rest, cache-based information leakage, unauthorized access to gossip information, manipulation of stored data.

2.6. Compactor:

• Security Focus: Data Integrity during Compaction, Secure Object Storage Access, Data Retention Policy Enforcement Accuracy, Secure Gossip Communication.
• Security Implications:
  • Data Corruption during Compaction: Bugs or vulnerabilities in the compaction process could lead to data corruption, resulting in inaccurate historical metrics.
  • Unauthorized Access to Object Storage: Similar to Store Gateway, compromised Compactor credentials could lead to unauthorized access to object storage.
  • Incorrect Data Deletion due to Retention Policy Errors: Errors in retention policy enforcement could lead to premature deletion of valuable metric data or failure to delete data as required for compliance.
  • Gossip Ring Vulnerabilities: Gossip Ring issues can impact Compactor coordination and data consistency.
• Specific Threats: Data corruption during compaction, unauthorized access to object storage, incorrect data deletion due to retention policy errors, resource exhaustion during compaction impacting performance, insecure gossip communication leading to inconsistent compaction.

2.7. Ruler:

• Security Focus: Secure Access to Queriers, Input Validation of Rules (PromQL), Secure Rule Configuration Storage, Secure Gossip Communication.
• Security Implications:
  • Unauthorized Access to Metric Data via Queriers: If Ruler's access to Queriers is not properly controlled, attackers could potentially use the Ruler to query and extract sensitive metric data without proper authorization.
  • PromQL Injection in Rules: Vulnerabilities in PromQL rule parsing and execution could allow injection attacks, potentially leading to unauthorized data access or DoS.
  • Rule Configuration Tampering: If rule configurations are not securely stored and managed, attackers could tamper with rules, leading to incorrect alerts, suppressed alerts, or malicious recording rules.
  • DoS via Complex Rules: Maliciously crafted or overly complex rules could consume excessive resources in the Ruler and Queriers, leading to DoS.
  • Gossip Ring Vulnerabilities: Gossip Ring issues can affect Ruler's ability to discover Queriers and distribute rules.
• Specific Threats: Unauthorized access to metric data via Queriers, PromQL injection in rules, rule configuration tampering, DoS via complex rules, insecure gossip communication leading to rule distribution issues.

2.8. Alerter:

• Security Focus: Secure Communication with External Systems, Secure Alert Notification Configuration Storage, Alert Data Confidentiality.
• Security Implications:
  • Alert Spoofing: If communication with external alert managers is not properly authenticated, attackers could spoof alerts, causing unnecessary alarms and potentially masking real issues.
  • Unauthorized Access to Alert Notification Configurations: If alert notification configurations are not securely stored and managed, attackers could modify configurations to redirect alerts, suppress alerts, or gain access to sensitive alert information.
  • Insecure Communication with External Alert Managers: If communication with external alert managers or notification channels is not encrypted, alert data (which may contain sensitive information) could be intercepted in transit.
  • Information Leakage via Alert Notifications: Alert notifications themselves could inadvertently leak sensitive information if not carefully designed and configured.
• Specific Threats: Alert spoofing, unauthorized access to alert notification configurations, insecure communication with external alert managers, information leakage via alert notifications, DoS via alert flooding.

2.9. Gossip Ring:

• Security Focus: Membership Authentication and Authorization, Confidentiality and Integrity of Gossip Messages, Resistance to Sybil Attacks, DoS Prevention on Gossip Protocol.
• Security Implications:
  • Gossip Ring Poisoning: If membership authentication is weak or absent, attackers could join the Gossip Ring as malicious nodes, injecting false information and disrupting cluster operations.
  • Unauthorized Membership: Unauthenticated nodes joining the ring can lead to resource exhaustion and potential manipulation of cluster state.
  • Information Leakage via Gossip Messages: If gossip messages contain sensitive information and are not encrypted, this information could be exposed to unauthorized parties within the network.
  • Sybil Attacks: Lack of Sybil attack resistance could allow attackers to create multiple fake nodes and overwhelm the Gossip Ring, disrupting its functionality.
  • DoS Attacks on Gossip Protocol: Exploiting vulnerabilities in the gossip protocol itself could lead to DoS attacks, disrupting cluster communication and stability.
  • Eavesdropping on Gossip Communication: If gossip communication is not encrypted, attackers on the network could eavesdrop and potentially gain insights into cluster topology and internal state.
• Specific Threats: Gossip ring poisoning, unauthorized membership, information leakage via gossip messages, Sybil attacks disrupting cluster operation, DoS attacks on gossip protocol, eavesdropping on gossip communication, manipulation of cluster state via gossip.

3. Architecture, Components, and Data Flow Inference

Based on the design document and general knowledge of distributed systems, we can infer the following architectural and data flow characteristics:

• Microservices Architecture: Cortex is designed as a set of independent microservices, enabling scalability and fault tolerance. Each component (Distributor, Ingester, etc.) likely runs as a separate process or container.
• gRPC for Internal Communication: The document mentions gRPC as the communication protocol for internal components. This suggests that components communicate with each other using gRPC for efficiency and potentially for built-in security features like authentication and encryption.
• HTTP/2 for External API Access: HTTP/2 is used for external API access, likely for remote_write and PromQL queries. This allows for efficient multiplexing and potentially TLS encryption for external communication.
• Object Storage Abstraction: The Store Gateway provides an abstraction layer over object storage (S3, GCS, Azure Blob Storage). This implies that Cortex is designed to be cloud-agnostic in terms of storage and can leverage different object storage backends.
• Gossip Ring for Service Discovery and Clustering: The Gossip Ring (using memberlist library) is central to Cortex's distributed nature. It enables dynamic service discovery, failure detection, and consistent hashing, crucial for load balancing and fault tolerance.
• Tenant Isolation through Namespaces/Prefixes: Tenant isolation is likely implemented by using tenant IDs to namespace or prefix data in various components, including object storage. This ensures logical separation of tenant data.
• Caching at Multiple Layers: Caching is used at Query Frontend and Store Gateway to improve query performance and reduce load on backend systems and object storage.

Data Flow Inference:

• Ingestion Path: Prometheus agents send remote_write requests over HTTP/2 (ideally TLS) to the Distributor. The Distributor authenticates the tenant, validates the data, and uses consistent hashing (likely based on series hash and Gossip Ring information) to route data to specific Ingesters via gRPC. Ingesters store data in memory and periodically flush chunks to object storage via Store Gateways using object storage APIs.
• Query Path: Users send PromQL queries over HTTP/2 (ideally TLS) to the Query Frontend. The Query Frontend caches queries and results. It then forwards queries to Queriers via gRPC. Queriers determine which Ingesters (for recent data) and Store Gateways (for historical data) to query. Queriers communicate with Ingesters and Store Gateways via gRPC. Store Gateways retrieve chunks from object storage using object storage APIs and return them to Queriers via gRPC. Queriers merge and process data and return results to the Query Frontend via gRPC, which then returns them to the user over HTTP/2.

4. Tailored Security Considerations for Cortex

Based on the analysis, the following security considerations are specifically tailored to Cortex:

• Multi-Tenancy Security is Paramount: Cortex is designed for multi-tenancy. Robust tenant isolation, authentication, and authorization are critical to prevent cross-tenant data access, resource interference, and security breaches.
• Gossip Ring Security is Foundational: The Gossip Ring is the backbone of Cortex's distributed architecture. Securing the Gossip Ring is essential for cluster stability, service discovery, and preventing malicious manipulation of the cluster.
• Object Storage Security is Critical for Data Confidentiality and Integrity: Cortex relies heavily on object storage for long-term data persistence. Securing object storage access, enabling encryption at rest, and ensuring proper access control are crucial for protecting metric data.
• PromQL Security is Essential for Query Integrity: PromQL is the query language for Cortex. Securing the PromQL engine against injection attacks and resource exhaustion is vital for maintaining query integrity and system availability.
• Input Validation at Ingestion is the First Line of Defense: The Distributor, as the entry point for metric ingestion, must perform thorough input validation to prevent injection attacks, data corruption, and DoS attacks.
• Secure Communication is Required at All Layers: Encryption and authentication should be enforced for all communication channels, both external (e.g., remote_write, PromQL queries) and internal (inter-component communication via gRPC, object storage access).
• Resource Management and Rate Limiting are Crucial for DoS Prevention: Rate limiting and resource quotas must be implemented at various levels (Distributor, Query Frontend, Querier, Ingester) to prevent DoS attacks and ensure fair resource sharing among tenants.
• Caching Security is Important to Prevent Information Leakage and Poisoning: Caches in Query Frontend and Store Gateway must be secured to prevent information leakage between tenants and cache poisoning attacks.
• Security Monitoring and Logging are Necessary for Threat Detection and Incident Response: Comprehensive security monitoring and centralized logging are essential for detecting suspicious activity, identifying security incidents, and enabling effective incident response.
• Regular Security Audits and Penetration Testing are Vital for Continuous Improvement: Regular security assessments, penetration testing, and vulnerability scanning are necessary to identify and address security weaknesses proactively.

5. Actionable and Tailored Mitigation Strategies

Based on the identified threats and security considerations, here are actionable and tailored mitigation strategies for Cortex:

5.1. Tenant Authentication and Authorization:

• Action: Implement Mutual TLS (mTLS) for remote_write: Enforce mTLS for Prometheus agents sending remote_write requests to the Distributor. This provides strong client authentication and encryption in transit. Configure Distributor to verify client certificates and map them to tenants.
• Action: Support API Keys and OAuth 2.0 for Tenant Authentication: Offer API keys and OAuth 2.0 as alternative authentication methods for remote_write and potentially for PromQL queries. Implement robust API key management and OAuth 2.0 integration.
• Action: Implement Role-Based Access Control (RBAC) for Authorization: Define roles and permissions for tenants and users. Enforce RBAC at the Distributor, Querier, Ruler, and Alerter to control access to data and operations based on tenant and user roles.
• Action: Audit Tenant Authentication and Authorization Events: Log all tenant authentication attempts and authorization decisions for security monitoring and auditing.

5.2. Gossip Ring Security:

• Action: Enable Gossip Encryption and Authentication: Configure the Gossip Ring to use encryption (e.g., using TLS or a similar mechanism) and authentication for gossip messages. This protects the confidentiality and integrity of gossip communication and prevents unauthorized nodes from joining.
• Action: Implement Gossip Ring Membership Validation: Implement mechanisms to validate new nodes joining the Gossip Ring, preventing unauthorized nodes from participating and potentially disrupting the cluster.
• Action: Monitor Gossip Ring Health and Membership: Continuously monitor the health and membership of the Gossip Ring to detect anomalies and potential attacks. Alert on suspicious changes in membership or gossip traffic patterns.
• Action: Regularly Rotate Gossip Ring Keys (if applicable): If the gossip encryption mechanism uses keys, implement regular key rotation to minimize the impact of potential key compromise.

5.3. Object Storage Security:

• Action: Enable Encryption at Rest for Object Storage: Mandate and enforce encryption at rest for the object storage backend used by Cortex. Utilize cloud provider KMS (Key Management Service) for secure key management.
• Action: Implement Least Privilege Access Control for Store Gateway and Compactor: Grant Store Gateways and Compactors only the necessary IAM roles and permissions to access object storage. Follow the principle of least privilege to minimize the impact of credential compromise.
• Action: Enable Object Storage Access Logging: Enable access logging for object storage buckets used by Cortex. Analyze these logs for suspicious access patterns and potential security incidents.
• Action: Regularly Audit Object Storage Access Policies: Periodically review and audit object storage access policies to ensure they are correctly configured and follow the principle of least privilege.

5.4. PromQL Security:

• Action: Implement PromQL Query Parsing and Validation: Thoroughly parse and validate PromQL queries in Query Frontend and Queriers to prevent PromQL injection vulnerabilities. Use a robust PromQL parser and validator library.
• Action: Set Query Resource Limits: Implement resource limits (e.g., CPU time, memory usage, data points processed) for PromQL queries to prevent resource exhaustion and DoS attacks caused by complex or malicious queries.
• Action: Sanitize PromQL Query Inputs: Sanitize user inputs used in PromQL queries to prevent injection attacks.
• Action: Regularly Review and Update PromQL Parser and Engine: Stay up-to-date with security patches and updates for the PromQL parser and engine to address known vulnerabilities.

5.5. Input Validation at Ingestion:

• Action: Implement Schema Validation for remote_write Requests: Define a strict schema for remote_write requests and implement schema validation at the Distributor. Validate metric names, label names, label values, and metric values against the schema.
• Action: Sanitize Metric Names and Labels: Sanitize metric names and labels to prevent injection attacks and ensure data consistency. Enforce restrictions on allowed characters and formats.
• Action: Validate Metric Value Types and Ranges: Validate that metric values are of the expected type (e.g., float64) and within acceptable ranges. Reject invalid data points.
• Action: Implement Rate Limiting and Admission Control at the Distributor: Configure rate limiting and admission control at the Distributor to protect downstream components from overload and DoS attacks.

5.6. Secure Communication:

• Action: Enforce TLS Encryption for All External Communication: Mandate TLS encryption for all external communication channels, including remote_write requests, PromQL queries, and communication with external alert managers.
• Action: Enforce gRPC Authentication and Encryption for Internal Communication: Configure gRPC communication between Cortex components to use authentication (e.g., mutual TLS or gRPC authentication) and encryption.
• Action: Use HTTPS for Web Interfaces (if any): If Cortex exposes any web interfaces (e.g., for configuration or monitoring), ensure they are served over HTTPS with TLS encryption.

5.7. Resource Management and DoS Prevention:

• Action: Implement Rate Limiting at Distributor and Query Frontend: Configure rate limiting at the Distributor for write requests and at the Query Frontend for query requests. Fine-tune rate limits based on capacity and expected traffic patterns.
• Action: Set Connection Limits for Each Component: Configure connection limits for each Cortex component to prevent connection exhaustion attacks.
• Action: Implement Resource Quotas and Limits per Tenant: Enforce resource quotas and limits per tenant to prevent resource exhaustion and ensure fair resource sharing. Limit CPU, memory, and storage usage per tenant.
• Action: Monitor Resource Usage of Each Component: Continuously monitor the resource usage (CPU, memory, network, I/O) of each Cortex component. Alert on anomalies and potential resource exhaustion.

5.8. Caching Security:

• Action: Implement Tenant-Aware Caching: Ensure that caches in Query Frontend and Store Gateway are tenant-aware and enforce strict tenant isolation. Prevent cross-tenant access to cached data.
• Action: Validate Cache Entries: Validate cache entries to prevent cache poisoning attacks. Implement mechanisms to verify the integrity and authenticity of cached data.
• Action: Limit Cache Size and TTL: Configure appropriate cache sizes and Time-To-Live (TTL) values to prevent cache exhaustion and minimize the risk of serving stale or outdated data.
• Action: Secure Cache Storage (if persistent): If caches are persisted to disk, ensure that the cache storage is properly secured and encrypted at rest.

5.9. Security Monitoring and Logging:

• Action: Implement Centralized Logging: Aggregate logs from all Cortex components into a centralized logging system (e.g., Elasticsearch, Loki, Splunk). Include security-relevant events in logs (authentication attempts, authorization decisions, errors, warnings).
• Action: Implement Security Monitoring and Alerting: Set up security monitoring and alerting rules to detect suspicious activity, security events, and performance anomalies. Integrate with a SIEM system if available.
• Action: Monitor Key Security Metrics: Monitor key security metrics, such as authentication failures, authorization denials, rate limiting events, error rates, and resource usage anomalies.
• Action: Regularly Review Security Logs and Alerts: Periodically review security logs and alerts to identify potential security incidents and improve security posture.

5.10. Regular Security Audits and Penetration Testing:

• Action: Conduct Regular Security Audits: Perform regular security audits of the Cortex codebase, configuration, and deployment to identify potential security weaknesses.
• Action: Perform Penetration Testing: Conduct periodic penetration testing to simulate real-world attacks and assess the effectiveness of security controls. Engage external security experts for penetration testing.
• Action: Implement Vulnerability Scanning: Implement automated vulnerability scanning for container images, dependencies, and infrastructure components used by Cortex. Regularly scan for and remediate known vulnerabilities.
• Action: Establish a Security Incident Response Plan: Develop and maintain a security incident response plan for Cortex. Define procedures for handling security incidents, including detection, containment, eradication, recovery, and post-incident analysis.

By implementing these tailored mitigation strategies, the development team can significantly enhance the security posture of Cortex and provide a more secure and reliable Prometheus-as-a-Service platform for its users. It is crucial to prioritize these recommendations based on risk assessment and implement them iteratively, continuously improving the security of Cortex over time.