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Threat Model Analysis for timescale/timescaledb

Threat: Chunk Corruption due to Storage Issues

  • Description: Underlying storage failures (disk errors, filesystem corruption) cause corruption of individual chunks within TimescaleDB hypertables. This leads to data corruption specifically within the time-partitioned chunks managed by TimescaleDB. An attacker might target storage subsystems to induce chunk corruption and data loss in time-series data.
    • Impact: Data loss or inconsistencies for specific time ranges stored in affected TimescaleDB chunks. Application errors when querying corrupted time-series data. Potential data integrity violations for critical time-series datasets managed by TimescaleDB.
    • TimescaleDB Component Affected: Chunk Storage, Hypertables
    • Risk Severity: High
    • Mitigation Strategies:
      • Implement RAID for storage redundancy specifically for TimescaleDB data volumes.
      • Regularly perform disk checks and filesystem integrity scans on storage used by TimescaleDB.
      • Utilize PostgreSQL's checksums for data integrity, ensuring they are enabled for TimescaleDB managed tablespaces.
      • Implement robust backup and recovery procedures specifically for TimescaleDB, focusing on chunk-level backups if possible.
      • Monitor storage health and performance metrics relevant to TimescaleDB operations (e.g., chunk I/O latency).

Threat: Unauthorized Access to Hypertables and Chunks

  • Description: Insufficiently configured PostgreSQL role-based access control (RBAC) allows unauthorized users or roles to directly access TimescaleDB hypertables and their underlying chunks. This bypasses potential application-level access controls and exposes raw time-series data. An attacker exploiting RBAC misconfigurations can directly query and exfiltrate sensitive time-series data from TimescaleDB.
    • Impact: Confidentiality breach. Exposure of sensitive time-series data stored in TimescaleDB hypertables to unauthorized parties. Potential for misuse of time-series data, impacting privacy or business intelligence depending on the data's sensitivity.
    • TimescaleDB Component Affected: PostgreSQL RBAC, Hypertables, Chunk Storage
    • Risk Severity: High
    • Mitigation Strategies:
      • Implement granular RBAC in PostgreSQL, specifically defining roles with least privilege access to TimescaleDB hypertables and chunks.
      • Define roles that restrict access to specific hypertables or even specific columns within hypertables based on application needs.
      • Regularly review and audit role permissions related to TimescaleDB objects (hypertables, chunks, continuous aggregates).
      • Enforce row-level security policies within PostgreSQL to further restrict access to data within TimescaleDB hypertables based on user roles or application context.

Threat: Denial of Service (DoS) through Resource Exhaustion via Hypertables

  • Description: Malicious or poorly optimized queries specifically targeting large TimescaleDB hypertables can consume excessive database resources (CPU, memory, I/O). This can lead to performance degradation or denial of service, particularly when querying across large time ranges or un-indexed columns in hypertables. An attacker can craft complex queries against hypertables to overload the TimescaleDB instance.
    • Impact: Service unavailability or severe performance degradation for applications relying on TimescaleDB. Application downtime due to database overload. Potential cascading failures if other services depend on TimescaleDB availability.
    • TimescaleDB Component Affected: Query Processing, PostgreSQL Core, Hypertables
    • Risk Severity: High
    • Mitigation Strategies:
      • Implement query optimization techniques specifically for TimescaleDB hypertables, including proper indexing on time and other frequently queried columns.
      • Set resource limits for database users and roles, including query timeouts and connection limits, to prevent resource exhaustion from runaway queries against hypertables.
      • Use connection pooling and rate limiting to control query load on TimescaleDB, especially for applications querying large hypertables.
      • Monitor database performance and resource utilization, focusing on metrics relevant to hypertables query performance (query execution time, I/O wait).
      • Implement query analysis and blocking mechanisms to identify and terminate long-running or resource-intensive queries targeting hypertables.

Threat: Vulnerabilities in TimescaleDB Extension Code

  • Description: Bugs or security vulnerabilities within the TimescaleDB extension code itself can be exploited by attackers. These vulnerabilities could lead to database crashes, data corruption, unauthorized access, or denial of service specifically within the TimescaleDB functionality. An attacker might exploit known vulnerabilities or discover zero-day exploits in the TimescaleDB extension.
    • Impact: Database crashes, instability, denial of service specifically affecting TimescaleDB features. Potential data corruption or unauthorized access to time-series data due to extension vulnerabilities.
    • TimescaleDB Component Affected: TimescaleDB Extension Code, Specific Modules/Functions within the extension
    • Risk Severity: High
    • Mitigation Strategies:
      • Keep TimescaleDB updated to the latest stable versions, ensuring timely application of security patches released by TimescaleDB developers.
      • Subscribe to security advisories and release notes from TimescaleDB to stay informed about known vulnerabilities and recommended updates.
      • Follow best practices for PostgreSQL extension security, including regular security scanning and vulnerability assessments of the TimescaleDB extension if possible.
      • Implement robust testing and staging environments to evaluate TimescaleDB updates and patches before deploying them to production.

Threat: Privilege Escalation within TimescaleDB Features

  • Description: Vulnerabilities or misconfigurations within specific TimescaleDB features, such as continuous aggregates or data retention policies, could be exploited to escalate privileges. This allows an attacker to perform actions beyond their intended authorization level within the TimescaleDB context. An attacker might leverage feature-specific flaws to gain administrative control over TimescaleDB functionalities.
    • Impact: Unauthorized access to sensitive TimescaleDB features or configurations. Ability to bypass access controls and perform privileged operations within TimescaleDB, potentially leading to data manipulation or service disruption.
    • TimescaleDB Component Affected: TimescaleDB Features (Continuous Aggregates, Retention Policies, etc.), Feature-Specific Access Control
    • Risk Severity: High
    • Mitigation Strategies:
      • Stay updated with TimescaleDB security patches and feature updates, as these often address potential privilege escalation vulnerabilities.
      • Follow security best practices for configuring and using TimescaleDB features, carefully reviewing documentation and security guidelines for each feature.
      • Regularly audit the security configurations of TimescaleDB features, ensuring that access controls and permissions are correctly applied and enforced.
      • Implement input validation and sanitization for feature configurations to prevent injection attacks that could lead to privilege escalation.
      • Apply the principle of least privilege when granting permissions for managing TimescaleDB features, limiting access to only necessary users and roles.