sec-design-deep-analysis.md

Deep Security Analysis of Apache Druid Deployment

1. Objective, Scope, and Methodology

Objective:

This deep security analysis aims to identify and evaluate potential security vulnerabilities and risks associated with deploying Apache Druid, based on the provided Security Design Review. The objective is to provide actionable, Druid-specific security recommendations and mitigation strategies to enhance the security posture of the application. This analysis will thoroughly examine the key components of Druid, their interactions, and the overall architecture to pinpoint potential weaknesses and suggest targeted improvements.

Scope:

The scope of this analysis encompasses the following:

• Druid Components: Broker, Router, Coordinator, Overlord, Historical, MiddleManager, Zookeeper, Metadata Store, and Deep Storage as described in the C4 Container diagram.
• Deployment Environment: Cloud-based Kubernetes deployment as outlined in the Deployment diagram.
• Build Process: CI/CD pipeline and artifact management as described in the Build diagram.
• Security Controls: Existing, accepted, and recommended security controls listed in the Security Design Review.
• Security Requirements: Authentication, Authorization, Input Validation, and Cryptography requirements outlined in the Security Design Review.
• Business Risks: Data breaches, service disruption, performance degradation, data integrity issues, and supply chain vulnerabilities as defined in the Business Risks section.

This analysis will not cover:

• Detailed code-level vulnerability analysis of Druid source code.
• Generic security best practices not directly applicable to Druid.
• Security aspects outside of the described architecture and deployment scenario.

Methodology:

This deep security analysis will employ the following methodology:

1. Architecture Decomposition: Analyze the C4 Context, Container, Deployment, and Build diagrams to understand the architecture, components, data flow, and interactions within the Druid system.
2. Threat Modeling: For each key component and data flow, identify potential threats based on common attack vectors, OWASP Top 10, and database security best practices. Consider threats relevant to the specific functionalities of each Druid component.
3. Security Control Mapping: Map existing and recommended security controls from the Security Design Review to the identified threats and Druid components. Evaluate the effectiveness of these controls and identify gaps.
4. Vulnerability Analysis (Conceptual): Based on the understanding of Druid's architecture and common database vulnerabilities, infer potential vulnerabilities within Druid components, focusing on areas like authentication, authorization, input validation, data handling, and inter-component communication.
5. Risk Assessment: Evaluate the potential impact and likelihood of identified threats, considering the business risks outlined in the Security Design Review.
6. Mitigation Strategy Development: Develop specific, actionable, and Druid-tailored mitigation strategies for each identified threat and vulnerability. Prioritize mitigations based on risk severity and feasibility.
7. Recommendation Generation: Formulate clear and concise security recommendations for the development team, focusing on practical steps to enhance the security of the Druid deployment.

2. Security Implications of Key Components

Based on the C4 Container diagram and component descriptions, the following security implications are identified for each key component of Apache Druid:

2.1 Broker:

• Functionality: Entry point for queries from Analytics Users and Applications. Routes queries, merges results, and caches queries.
• Security Implications:
  • Authentication and Authorization Bypass: If authentication and authorization mechanisms are weak or misconfigured, unauthorized users could gain access to sensitive data by executing queries.
  • SQL/Native Injection Attacks: Vulnerable to injection attacks if query inputs are not properly validated and sanitized. Malicious queries could potentially bypass security controls, access unauthorized data, or even compromise the Broker or backend systems.
  • Denial of Service (DoS): Susceptible to DoS attacks through excessive or malformed queries, potentially impacting real-time analytics availability.
  • Query Result Tampering: Although less likely in Druid's architecture, vulnerabilities in query processing could theoretically lead to tampered query results, affecting data integrity.
  • Information Disclosure: Error messages or verbose logging could inadvertently expose sensitive information about the Druid cluster or data.
  • Cache Poisoning: If query caching is not implemented securely, malicious actors might be able to poison the cache with incorrect data, leading to inaccurate analytics.

2.2 Router (Optional):

• Functionality: Load balancer and query router in front of Brokers.
• Security Implications:
  • Bypass Router for Direct Broker Access: Misconfiguration could allow bypassing the Router and directly accessing Brokers, potentially circumventing security controls implemented at the Router level (e.g., rate limiting, WAF).
  • Router Vulnerabilities: Vulnerabilities in the Router component itself (if it's a separate application or reverse proxy) could be exploited to compromise the query path.
  • DoS at Router Level: Router can be a target for DoS attacks, impacting the availability of the entire Druid query service.
  • TLS Termination Issues: Improper TLS termination at the Router could expose traffic in plaintext between the Router and Brokers.

2.3 Coordinator:

• Functionality: Manages data segment lifecycle, data balancing, and cluster health.
• Security Implications:
  • Unauthorized Administrative Access: If access to the Coordinator API is not properly secured, unauthorized users could manipulate data segment management, potentially leading to data loss, corruption, or service disruption.
  • Data Manipulation via Segment Management: Vulnerabilities in segment management logic could be exploited to inject malicious data or delete legitimate data segments.
  • Cluster State Manipulation: Compromising the Coordinator could allow attackers to manipulate the cluster state, leading to instability or denial of service.
  • Metadata Store Compromise via Coordinator: If the Coordinator is compromised, it could be used as a pivot point to attack the Metadata Store, gaining access to sensitive cluster configuration and metadata.

2.4 Overlord:

• Functionality: Manages data ingestion tasks and MiddleManagers.
• Security Implications:
  • Unauthorized Ingestion Task Management: Lack of proper authorization could allow unauthorized users to submit, modify, or delete ingestion tasks, potentially leading to data injection, data deletion, or service disruption.
  • Malicious Data Injection: Vulnerabilities in data ingestion pipelines or input validation at the Overlord level could allow injection of malicious data into Druid, compromising data integrity and potentially leading to further exploits.
  • MiddleManager Compromise via Overlord: A compromised Overlord could be used to attack or control MiddleManagers, potentially gaining access to data in transit or temporary storage.
  • Resource Exhaustion via Ingestion Tasks: Maliciously crafted ingestion tasks could be used to exhaust cluster resources (CPU, memory, storage), leading to DoS.

2.5 Historical:

• Functionality: Stores and serves queryable data segments.
• Security Implications:
  • Unauthorized Data Access: If access controls to data segments are weak or bypassed, unauthorized users could directly access sensitive data stored in Historical processes.
  • Data Exfiltration: Vulnerabilities in query processing or data serving could potentially be exploited to exfiltrate large volumes of data.
  • Data Segment Corruption: Although less likely, vulnerabilities could theoretically lead to corruption of data segments, impacting data integrity.
  • Local File System Access: If Historical processes are compromised, attackers could gain access to the underlying file system where data segments are stored, potentially leading to data breaches or system compromise.

2.6 MiddleManager:

• Functionality: Real-time data ingestion and indexing, creates data segments.
• Security Implications:
  • Input Validation Vulnerabilities during Ingestion: MiddleManagers are critical points for input validation. Weak input validation could lead to injection attacks, data corruption, or DoS.
  • Data in Transit Exposure: Data being ingested by MiddleManagers might be vulnerable if encryption in transit is not properly implemented for all ingestion channels.
  • Temporary Storage Security: MiddleManagers use temporary storage during ingestion. If not secured properly, sensitive data in temporary storage could be exposed.
  • Resource Exhaustion during Ingestion: Malicious data streams or ingestion tasks could be designed to exhaust MiddleManager resources, leading to DoS.

2.7 Zookeeper:

• Functionality: Cluster coordination and configuration management.
• Security Implications:
  • Zookeeper Compromise = Cluster Compromise: Zookeeper is a critical component. Compromising Zookeeper can lead to complete cluster compromise, including data access, service disruption, and control of all Druid components.
  • Unauthorized Access to Cluster Configuration: Access to Zookeeper allows modification of cluster configuration, which could be exploited to weaken security controls or disrupt the cluster.
  • Man-in-the-Middle Attacks on Zookeeper Communication: If communication within the Zookeeper ensemble and between Druid components and Zookeeper is not encrypted and authenticated, it could be vulnerable to man-in-the-middle attacks.

2.8 Metadata Store (e.g., MySQL, PostgreSQL):

• Functionality: Stores metadata about data segments, cluster state, and tasks.
• Security Implications:
  • Metadata Store Compromise = Sensitive Information Exposure: The Metadata Store contains sensitive information about the Druid cluster, data segments, and potentially user credentials or access control policies. Compromise could lead to widespread data breaches and system compromise.
  • SQL Injection in Metadata Store Access: If Druid components interact with the Metadata Store using dynamically constructed SQL queries without proper parameterization, they could be vulnerable to SQL injection attacks.
  • Database Vulnerabilities: Underlying database vulnerabilities in MySQL or PostgreSQL could be exploited to compromise the Metadata Store.
  • Weak Database Access Controls: Insufficient access controls to the Metadata Store database could allow unauthorized access and manipulation of metadata.

2.9 Deep Storage (e.g., S3, HDFS):

• Functionality: Durable and reliable storage of data segments.
• Security Implications:
  • Unauthorized Access to Deep Storage = Data Breach: Deep Storage holds the persistent data segments. Unauthorized access directly leads to a data breach.
  • Data Exfiltration from Deep Storage: Misconfigured access controls or vulnerabilities in Deep Storage services could allow attackers to exfiltrate large volumes of data.
  • Data Deletion or Corruption in Deep Storage: Although less likely, vulnerabilities or misconfigurations could potentially lead to accidental or malicious deletion or corruption of data segments in Deep Storage, impacting data availability and integrity.
  • Insecure Access Credentials Management: If access credentials for Deep Storage (e.g., AWS IAM keys) are not managed securely, they could be compromised, granting unauthorized access.

3. Architecture, Components, and Data Flow Inference

Based on the provided diagrams and descriptions, the inferred architecture, components, and data flow are as follows:

Architecture: Distributed, microservices-like architecture designed for scalability and fault tolerance. Key components are separated by function (querying, ingestion, coordination, storage).

Components:

• External Facing: Router (optional), Broker (query entry points).
• Query Processing: Broker, Historical, MiddleManager.
• Ingestion: Overlord, MiddleManager.
• Coordination & Management: Coordinator, Overlord, Zookeeper.
• Metadata & State: Zookeeper, Metadata Store.
• Data Storage: Historical (local), Deep Storage (persistent).

Data Flow:

1. Data Ingestion:
   • Data Source -> Overlord (task submission) -> MiddleManager (ingestion, indexing) -> Historical (segment serving) -> Deep Storage (segment persistence).
2. Query Processing:
   • Analytics User/Application -> Router (optional) -> Broker (query reception, routing) -> Historical & MiddleManager (data retrieval) -> Broker (result merging) -> Router (optional) -> Analytics User/Application.
3. Metadata & Control Flow:
   • Coordinator & Overlord & Broker -> Zookeeper (cluster state, coordination).
   • Coordinator & Overlord & Broker -> Metadata Store (metadata persistence).

Key Security Data Flows to Consider:

• External Query Requests: Analytics User/Application -> Router/Broker. Requires strong authentication, authorization, input validation, and encryption in transit (TLS).
• Data Ingestion Streams: Data Source -> Overlord/MiddleManager. Requires authentication, authorization (if applicable), input validation, and encryption in transit (TLS/SSL depending on ingestion method).
• Inter-Component Communication: Broker <-> Historical, Broker <-> Coordinator, Overlord <-> MiddleManager, Components <-> Zookeeper, Components <-> Metadata Store. Requires authentication and authorization between components, and encryption in transit for sensitive communication.
• Access to Deep Storage and Metadata Store: Druid components (especially Historical, MiddleManager, Coordinator, Overlord) accessing Deep Storage and Metadata Store. Requires strong authentication and authorization mechanisms for accessing these backend systems, and secure credential management.

4. Specific Security Recommendations for Druid Deployment

Based on the analysis, here are specific security recommendations tailored to the Druid deployment:

4.1 Authentication and Authorization:

• Recommendation 1: Implement Druid Authentication and Authorization Plugins: Enable and configure Druid's built-in authentication and authorization mechanisms. Utilize plugins like "basic security" or integrate with enterprise identity providers (LDAP, Active Directory, OAuth 2.0) as per security requirements.
  • Actionable Mitigation: Choose an appropriate Druid security extension, configure authentication providers (e.g., LDAP), define user roles and permissions, and enforce authentication for all Broker and administrative APIs (Coordinator, Overlord).
• Recommendation 2: Enforce Role-Based Access Control (RBAC): Implement granular RBAC policies to control access to Druid data and operations based on user roles and privileges. Define roles that align with the principle of least privilege.
  • Actionable Mitigation: Define roles (e.g., data-analyst, data-scientist, admin), map users to roles, and configure Druid authorization rules to restrict access to specific datasources, segments, or operations based on roles.
• Recommendation 3: Implement Multi-Factor Authentication (MFA) for Administrative Access: Enforce MFA for all administrative access to Druid components (Coordinator, Overlord, Zookeeper, Metadata Store, Deep Storage) as recommended in the Security Design Review.
  • Actionable Mitigation: Integrate MFA for administrative users accessing Kubernetes cluster, Druid administrative APIs, and backend systems. Utilize cloud provider MFA services or integrate with enterprise MFA solutions.

4.2 Input Validation and Injection Prevention:

• Recommendation 4: Implement Robust Input Validation for Queries: Thoroughly validate and sanitize all user inputs in SQL and Native queries at the Broker level to prevent injection attacks. Utilize parameterized queries or prepared statements where possible.
  • Actionable Mitigation: Implement input validation logic in Broker query processing, focusing on sanitizing special characters and keywords. Explore Druid's query parameterization capabilities. Conduct regular security testing for injection vulnerabilities.
• Recommendation 5: Input Validation for Data Ingestion: Implement strict input validation at the Overlord and MiddleManager levels for all data ingestion pipelines. Validate data types, formats, and ranges to prevent malicious data injection and data corruption.
  • Actionable Mitigation: Define data schemas and validation rules for ingestion pipelines. Implement validation logic in Druid ingestion specs. Monitor and log validation failures.

4.3 Cryptography and Data Protection:

• Recommendation 6: Enable Encryption at Rest for Deep Storage and Metadata Store: Ensure encryption at rest is enabled for both Deep Storage (e.g., S3 server-side encryption, HDFS encryption) and the Metadata Store (e.g., RDS encryption).
  • Actionable Mitigation: Configure server-side encryption for S3 buckets used for Deep Storage. Enable encryption at rest for the RDS PostgreSQL instance used as the Metadata Store. Verify encryption is active and keys are managed securely.
• Recommendation 7: Enforce Encryption in Transit (TLS/SSL) for All Communication Channels: Enable TLS/SSL encryption for all communication channels, including:
  • Client to Broker/Router (HTTPS).
  • Broker/Router to Historical/MiddleManager.
  • Druid components to Zookeeper (if supported by Zookeeper configuration).
  • Druid components to Metadata Store (JDBC/database connection encryption).
  • Data ingestion channels (HTTPS, Kafka with TLS, etc.).
  • Actionable Mitigation: Configure TLS for Broker and Router HTTP endpoints. Configure secure JDBC connections to the Metadata Store. Enable TLS for Kafka or other ingestion sources. Investigate and enable TLS for Zookeeper communication if feasible and beneficial.
• Recommendation 8: Securely Manage Cryptographic Keys and Secrets: Implement a robust secret management solution (e.g., HashiCorp Vault, Kubernetes Secrets, cloud provider secret management services) to securely store and manage cryptographic keys, database credentials, API keys, and other sensitive information used by Druid components. Avoid hardcoding secrets in configuration files or code.
  • Actionable Mitigation: Implement a secret management system. Migrate all sensitive credentials and keys to the secret management system. Configure Druid components to retrieve secrets from the secret management system. Rotate secrets regularly.

4.4 Network Security and Isolation:

• Recommendation 9: Implement Network Segmentation and Firewall Rules: Utilize Kubernetes network policies and cloud provider security groups/firewalls to segment the Druid cluster network and restrict network access to only necessary ports and services. Isolate Druid components into separate network segments based on their function.
  • Actionable Mitigation: Define network policies in Kubernetes to restrict pod-to-pod communication. Configure security groups/firewalls to control inbound and outbound traffic to Kubernetes nodes and pods. Limit external access to Broker/Router to necessary ports (HTTPS).
• Recommendation 10: Secure Zookeeper Access: Restrict access to Zookeeper nodes to only authorized Druid components. Implement Zookeeper authentication (if feasible and beneficial) to further control access.
  • Actionable Mitigation: Use Kubernetes network policies to restrict network access to Zookeeper pods. Investigate Zookeeper ACLs or other authentication mechanisms to control access from Druid components.

4.5 Security Monitoring and Logging:

• Recommendation 11: Implement Comprehensive Security Logging and Monitoring: Establish a robust security monitoring and logging system to collect logs from all Druid components (Broker, Router, Coordinator, Overlord, Historical, MiddleManager, Zookeeper, Metadata Store). Monitor for security events, anomalies, and suspicious activities.
  • Actionable Mitigation: Configure Druid components to generate detailed security logs (authentication attempts, authorization decisions, query logs, ingestion logs, administrative actions). Centralize log collection and analysis using a SIEM or logging platform. Set up alerts for critical security events.
• Recommendation 12: Regularly Perform Vulnerability Scanning and Penetration Testing: Conduct regular vulnerability scanning and penetration testing of the Druid deployment to identify and remediate potential security weaknesses. Include both infrastructure and application-level testing.
  • Actionable Mitigation: Integrate vulnerability scanning into the CI/CD pipeline and schedule regular penetration testing (at least annually). Use both automated scanning tools and manual penetration testing by security experts. Remediate identified vulnerabilities promptly.

4.6 Build and Deployment Security:

• Recommendation 13: Automate Security Checks in CI/CD Pipeline: Integrate security checks into the CI/CD pipeline, including SAST, DAST, dependency scanning, and container image scanning.
  • Actionable Mitigation: Integrate SAST tools to scan Druid configuration files and deployment manifests. Integrate DAST tools to test running Druid instances. Implement dependency scanning to identify vulnerabilities in Druid dependencies. Scan Docker images for vulnerabilities before deployment.
• Recommendation 14: Implement Dependency Scanning and Management: Utilize dependency scanning tools to identify and manage vulnerabilities in third-party libraries used by Druid. Regularly update dependencies to address known vulnerabilities.
  • Actionable Mitigation: Use dependency scanning tools (e.g., OWASP Dependency-Check, Snyk) to scan Druid dependencies. Implement a process for tracking and updating vulnerable dependencies.
• Recommendation 15: Secure Build Environment and Artifact Repository: Harden the build environment and secure access to the artifact repository. Implement access controls, audit logging, and vulnerability scanning for both.
  • Actionable Mitigation: Harden build servers and containers. Implement RBAC for access to the artifact repository. Scan artifacts for vulnerabilities before deployment. Secure artifact signing to ensure integrity.

4.7 Incident Response:

• Recommendation 16: Establish a Druid-Specific Security Incident Response Plan: Develop a security incident response plan specifically tailored to Druid deployments. Define procedures for detecting, responding to, and recovering from security incidents affecting Druid.
  • Actionable Mitigation: Create an incident response plan document. Define roles and responsibilities. Outline procedures for incident detection, containment, eradication, recovery, and post-incident analysis. Conduct regular incident response drills.

5. Actionable and Tailored Mitigation Strategies

For each recommendation above, actionable and tailored mitigation strategies are already embedded within the "Actionable Mitigation" points. To summarize and further emphasize, here are some key actionable strategies:

• Configuration as Code & Automation: Manage Druid configurations, security settings, and deployment manifests as code in version control. Automate security configurations and deployments using CI/CD pipelines to ensure consistency and reduce manual errors.
• Least Privilege Principle: Apply the principle of least privilege across all aspects of Druid security. Grant users and components only the necessary permissions to perform their functions.
• Regular Security Audits and Reviews: Conduct periodic security audits and reviews of Druid configurations, access controls, security logs, and incident response procedures to identify and address security gaps.
• Security Training for Operations and Development Teams: Provide security training to operations and development teams on Druid security best practices, common vulnerabilities, and secure configuration techniques.
• Stay Updated with Druid Security Advisories: Regularly monitor Apache Druid security mailing lists and advisories for any reported vulnerabilities and security patches. Apply security patches promptly.
• Leverage Cloud Provider Security Features: Utilize security features provided by the cloud provider (Kubernetes security features, network security groups, IAM roles, secret management services, monitoring services) to enhance the security of the Druid deployment.

By implementing these specific recommendations and actionable mitigation strategies, the development team can significantly enhance the security posture of their Apache Druid deployment, mitigating the identified threats and reducing the business risks associated with data breaches, service disruption, and data integrity issues. Continuous monitoring, regular security assessments, and proactive security management are crucial for maintaining a strong security posture over time.