threat-modeling.md

Threat Model Analysis for elastic/elasticsearch

Threat: Unauthorized Data Access via Direct API Exploitation

• Description: An attacker directly queries the Elasticsearch REST API, bypassing the application layer, using exposed endpoints and exploiting weak or missing authentication/authorization. The attacker might use tools like curl or custom scripts to send crafted requests. They could attempt to enumerate indices, retrieve documents, or even modify data if write access is not properly restricted.
• Impact:
  • Data breach: Sensitive information (PII, financial data, etc.) is exposed.
  • Data modification: Unauthorized changes to data, leading to data corruption or integrity issues.
  • Reputational damage: Loss of customer trust and potential legal consequences.
• Affected Elasticsearch Component:
  • REST API (all endpoints, including _search, _bulk, _index, etc.)
  • Security module (if X-Pack/Security is not enabled or misconfigured)
• Risk Severity: Critical
• Mitigation Strategies:
  • Enable and Configure Elasticsearch Security (X-Pack/Security): This is the primary mitigation. Implement strong authentication (usernames/passwords, API keys, mutual TLS).
  • Implement Role-Based Access Control (RBAC): Define granular roles and permissions, limiting access to specific indices, fields, and actions based on the principle of least privilege.
  • Network Segmentation: Isolate the Elasticsearch cluster within a private network (VPC). Do not expose it directly to the public internet. Use a firewall or security groups to restrict network access.
  • API Key Management: If using API keys, manage them securely. Rotate keys regularly and restrict their permissions.
  • Disable Unused API Endpoints: If certain API endpoints are not needed, disable them to reduce the attack surface.

Threat: Data Exfiltration via Search Query Manipulation

• Description: An attacker, with legitimate but limited access to the application's search functionality, crafts malicious search queries to extract data beyond their authorized scope. They might exploit features like aggregations, scripting, or highlighting to retrieve sensitive information hidden within documents or to infer information from query results. They might use wildcard searches, regular expressions, or other techniques to bypass intended restrictions.
• Impact:
  • Data leakage: Sensitive information is exposed, even without direct access to the underlying indices.
  • Privacy violation: Exposure of PII or other confidential data.
• Affected Elasticsearch Component:
  • Search API (_search endpoint)
  • Query DSL (all query types, including match, term, bool, range, etc.)
  • Aggregations API
  • Scripting engine (if enabled)
• Risk Severity: High
• Mitigation Strategies:
  • Strict Input Validation and Sanitization: Thoroughly validate and sanitize all user-provided search input before it reaches Elasticsearch. Use a whitelist approach to allow only known-good characters and patterns. Reject any input that contains suspicious characters or query operators.
  • Query Rewriting/Filtering: Implement a layer between the user and Elasticsearch that rewrites or filters queries to enforce security policies. This could involve removing potentially dangerous query clauses, limiting the scope of searches, or adding mandatory filters.
  • Limit Query Complexity: Restrict the complexity of user queries. Limit the number of clauses, nested queries, aggregations, and the use of wildcards.
  • Disable or Restrict Scripting: If scripting is not essential, disable it entirely. If it's needed, use the Painless scripting language and configure strict security settings to limit its capabilities.
  • Field-Level and Document-Level Security: Use Elasticsearch's security features to restrict access to specific fields and documents, even if a user can execute a search query.
  • Parameterized Queries: Use parameterized queries or the Elasticsearch Query DSL to construct queries, rather than concatenating user input directly into query strings.

Threat: Denial of Service (DoS) via Resource Exhaustion

• Description: An attacker sends a large number of complex, resource-intensive queries (e.g., deep aggregations, expensive scripts, large wildcard searches) to overwhelm the Elasticsearch cluster. This can lead to slow response times, timeouts, or even cluster crashes. The attacker might use automated tools to generate a high volume of requests.
• Impact:
  • Service unavailability: The application becomes unresponsive or unusable.
  • Data loss (in extreme cases): If the cluster crashes, data might be lost if it hasn't been properly replicated or backed up.
  • Financial loss: Downtime can lead to lost revenue and business disruption.
• Affected Elasticsearch Component:
  • Search API (_search endpoint)
  • Aggregations API
  • Scripting engine
  • Cluster resources (CPU, memory, disk I/O, network bandwidth)
• Risk Severity: High
• Mitigation Strategies:
  • Rate Limiting: Implement rate limiting on search requests, both at the application level and within Elasticsearch (using Ingest Pipelines or a proxy).
  • Query Timeouts: Set reasonable timeouts for search queries to prevent long-running queries from consuming resources indefinitely.
  • Circuit Breakers: Use Elasticsearch's circuit breakers to prevent individual queries from consuming excessive resources (memory, CPU).
  • Resource Limits: Configure appropriate resource limits (CPU, memory, heap size) for the Elasticsearch cluster and individual nodes.
  • Cluster Sizing and Scaling: Ensure the cluster is adequately sized to handle the expected load and potential spikes. Implement auto-scaling to automatically adjust resources based on demand.
  • Dedicated Master Nodes: Use dedicated master nodes to improve cluster stability and prevent them from being overloaded.
  • Monitor Cluster Health: Continuously monitor cluster health and performance metrics (CPU usage, memory usage, query latency, indexing rate). Set up alerts for resource exhaustion or performance degradation.

Threat: Script Injection Leading to Remote Code Execution (RCE)

• Description: An attacker injects malicious code into a script field within a document or a script used in a query or aggregation. If dynamic scripting is enabled and not properly secured, this code could be executed by Elasticsearch, potentially leading to RCE on the Elasticsearch nodes.
• Impact:
  • Complete system compromise: The attacker gains full control over the Elasticsearch nodes.
  • Data theft: Access to all data stored in the cluster.
  • Data destruction: The attacker could delete or corrupt data.
  • Lateral movement: The attacker could use the compromised nodes to attack other systems on the network.
• Affected Elasticsearch Component:
  • Scripting engine (Painless, Groovy, etc.)
  • script field in queries and aggregations
  • scripted_metric aggregation
  • script_score function
  • Ingest pipelines with script processors
• Risk Severity: Critical
• Mitigation Strategies:
  • Disable Dynamic Scripting (Preferred): If dynamic scripting is not absolutely necessary, disable it entirely. This is the most effective mitigation.
  • Use Painless (and Configure Securely): If scripting is required, use the Painless scripting language, which is designed to be more secure. Configure strict security settings for Painless, limiting its capabilities (e.g., disabling access to system classes, restricting network access).
  • Strict Input Validation (Essential): Thoroughly validate and sanitize all user-provided input that might be used in scripts. Use a whitelist approach to allow only known-good characters and patterns.
  • Use Stored Scripts: Instead of allowing users to provide scripts directly, use pre-defined, stored scripts that have been thoroughly reviewed and tested.
  • Regular Security Audits: Conduct regular security audits to review script usage and identify potential vulnerabilities.

Threat: Unauthorized Snapshot Access and Data Theft

• Description: An attacker gains unauthorized access to Elasticsearch snapshots, either by exploiting vulnerabilities in the snapshot repository (e.g., an insecurely configured S3 bucket) or by compromising credentials with snapshot access. The attacker can then download the snapshots and extract the data.
• Impact:
  • Data breach: Exposure of all data stored in the Elasticsearch cluster at the time the snapshot was taken.
  • Reputational damage: Loss of customer trust and potential legal consequences.
• Affected Elasticsearch Component:
  • Snapshot and Restore API
  • Snapshot repositories (e.g., S3, shared file system, HDFS)
• Risk Severity: High
• Mitigation Strategies:
  • Secure Snapshot Repositories: Store snapshots in secure, access-controlled repositories. Use strong authentication and authorization mechanisms. For cloud storage (e.g., S3), use IAM roles and policies to restrict access.
  • Encrypt Snapshots: Encrypt the snapshots themselves, both at rest and in transit.
  • Restrict Snapshot/Restore Permissions: Limit the users and roles that have permissions to create and restore snapshots. Use the principle of least privilege.
  • Monitor Snapshot Activity: Monitor snapshot creation and restore operations for suspicious activity.
  • Regularly Delete Old Snapshots: Delete old snapshots that are no longer needed to reduce the risk of data exposure.