attack-surface.md

Attack Surface Analysis for ankane/searchkick

Attack Surface: Elasticsearch Query Injection

• Description: Attackers inject malicious Elasticsearch query syntax into search parameters, manipulating the intended query logic.
• Searchkick Contribution: Searchkick constructs Elasticsearch queries based on application parameters, often derived from user input. Improper handling of these parameters by developers creates direct injection points within Searchkick-powered search features.
• Example: An attacker modifies a search query parameter to include Elasticsearch operators like OR or AND to bypass intended search filters and access data they shouldn't. For instance, injecting OR { "match_all": {} } into a product search to potentially retrieve user data if the application naively incorporates user input into the query.
• Impact: Data exfiltration of sensitive information, unauthorized data modification or deletion within Elasticsearch, and potential denial of service (DoS) of the Elasticsearch cluster.
• Risk Severity: High to Critical
• Mitigation Strategies:
  • Parameterize Search Queries: Utilize Searchkick's features and best practices to parameterize search queries, ensuring user input is treated as data and not executable code within the Elasticsearch query.
  • Strict Input Validation and Sanitization: Implement rigorous validation and sanitization of all user inputs used in search queries. Escape or reject any characters or syntax that could be used for injection.
  • Principle of Least Privilege (Elasticsearch User): Configure the Elasticsearch user credentials used by the application with the minimum necessary permissions to limit the potential damage from a successful injection attack.
  • Implement Query Whitelisting: Define and enforce a whitelist of allowed search parameters, operators, and query structures to restrict the complexity and potential injection vectors in user-provided queries.

Attack Surface: Denial of Service (DoS) via Complex Search Queries

• Description: Attackers exploit Searchkick's ability to create complex search queries to craft resource-intensive requests that overwhelm the Elasticsearch cluster.
• Searchkick Contribution: Searchkick's DSL and features enable developers to build powerful and flexible search functionalities. However, this flexibility can be abused by attackers to create queries that consume excessive resources, leading to DoS.
• Example: An attacker sends repeated search requests with extremely broad wildcard queries (e.g., search "*" ), deeply nested aggregations, or requests for massive result sets without pagination. These queries can exhaust Elasticsearch resources like CPU, memory, and I/O.
• Impact: Elasticsearch cluster overload, significant performance degradation for legitimate users, and potential service unavailability or crashes.
• Risk Severity: High
• Mitigation Strategies:
  • Application-Level Query Complexity Limits and Rate Limiting: Implement rate limiting on search requests and analyze query complexity at the application level to identify and block overly resource-intensive queries before they reach Elasticsearch.
  • Elasticsearch Resource Limits Configuration: Configure Elasticsearch settings to limit resource consumption per query, such as indices.query.bool.max_clause_count and search.max_buckets, to prevent individual queries from monopolizing resources.
  • Robust Monitoring and Alerting: Implement comprehensive monitoring of Elasticsearch cluster performance metrics (CPU, memory, query latency) and set up alerts to detect unusual spikes indicative of a DoS attack.
  • Enforce Pagination and Result Size Limits: Strictly enforce reasonable limits on the number of results returned per page and the maximum total results allowed for any single search request within the application.
  • Implement Query Timeouts: Set appropriate timeouts for Elasticsearch queries both in Searchkick configuration and potentially within Elasticsearch itself to prevent long-running queries from indefinitely consuming resources.

Attack Surface: Information Disclosure via Indexing Sensitive Data

• Description: Sensitive data is inadvertently included in the attributes indexed by Searchkick, making it searchable and potentially accessible to unauthorized users.
• Searchkick Contribution: Searchkick automatically indexes model attributes specified by developers. If developers are not careful in selecting attributes for indexing, sensitive information can be unintentionally exposed through search functionality.
• Example: A developer indexes the email and phone_number attributes of a User model for search purposes without considering access control. An attacker with access to the search interface, even if intended for limited use, could then query and retrieve this PII.
• Impact: Leakage of confidential or sensitive data, privacy violations, potential regulatory compliance breaches, and reputational damage.
• Risk Severity: High to Critical
• Mitigation Strategies:
  • Careful Selection of Indexed Attributes: Thoroughly review and meticulously select only the absolutely necessary model attributes for indexing. Avoid indexing sensitive data unless there is a clear and justified business need and strong access controls are in place.
  • Data Masking or Redaction during Indexing: Implement data masking, tokenization, or redaction techniques to remove or obfuscate sensitive information before it is indexed into Elasticsearch. Index only non-sensitive representations of the data when possible.
  • Strict Access Control on Search Functionality: Implement robust authentication and authorization mechanisms at the application level to strictly control who can perform searches and what data they are authorized to access through search results.
  • Regular Audits of Indexed Data: Periodically audit the Elasticsearch indices to ensure that sensitive data is not inadvertently being indexed and exposed.

Attack Surface: Reindexing Process Vulnerabilities

• Description: Vulnerabilities in the Searchkick reindexing process can be exploited to inject malicious data into the search index or cause denial of service.
• Searchkick Contribution: Searchkick provides mechanisms for reindexing data. If the process to trigger reindexing is not properly secured or if data validation is insufficient during reindexing, it can become an attack vector.
• Example: An attacker discovers an unprotected endpoint or process that triggers Searchkick reindexing. They could repeatedly trigger reindexing, causing excessive load on the database and Elasticsearch (DoS). Alternatively, if the reindexing process involves fetching data from external sources without proper validation, malicious data could be injected into the Elasticsearch index during the reindexing process.
• Impact: Corruption of the search index with malicious or inaccurate data, denial of service due to resource exhaustion during reindexing, and potential disruption of application functionality relying on search.
• Risk Severity: High
• Mitigation Strategies:
  • Secure Reindexing Trigger Mechanism: Ensure that the reindexing process is triggered only through authorized internal processes, administrative interfaces, or secure background jobs with strong authentication and authorization. Avoid exposing reindexing triggers to untrusted sources.
  • Rate Limiting and Scheduling of Reindexing: Implement rate limiting on reindexing operations to prevent abuse and resource exhaustion. Schedule reindexing during off-peak hours to minimize impact on users.
  • Data Validation and Sanitization during Reindexing: If data is transformed or processed during reindexing, implement thorough input validation and sanitization to prevent injection of malicious data into the index.
  • Monitoring of Reindexing Processes: Monitor reindexing processes for unusual activity, errors, or excessive resource consumption. Implement alerting for anomalies.

Attack Surface: Dependency Vulnerabilities (Elasticsearch Client Library)

• Description: Vulnerabilities in the Elasticsearch client library used by Searchkick can be exploited, potentially leading to application compromise.
• Searchkick Contribution: Searchkick directly depends on the elasticsearch Ruby gem, which acts as the client library for interacting with Elasticsearch. Vulnerabilities in this client library can directly impact applications using Searchkick.
• Example: A security vulnerability is discovered in a specific version of the elasticsearch Ruby gem. If the application uses Searchkick with this vulnerable version of the client library, attackers could potentially exploit this vulnerability to compromise the application or its interaction with Elasticsearch.
• Impact: Application compromise, potential data breach if the client library vulnerability allows unauthorized access to Elasticsearch or the application's data, and denial of service if the vulnerability can be exploited to crash the application or Elasticsearch client.
• Risk Severity: High to Critical (depending on the nature of the client library vulnerability).
• Mitigation Strategies:
  • Keep Searchkick and Elasticsearch Client Library Up-to-Date: Regularly update the Searchkick gem and, by extension, the elasticsearch Ruby client library to the latest stable versions. This ensures that known security vulnerabilities are patched promptly.
  • Vulnerability Scanning of Dependencies: Integrate dependency vulnerability scanning into the development and deployment pipeline to automatically detect and alert on known vulnerabilities in the elasticsearch Ruby gem and other dependencies.
  • Security Audits and Patch Management: Conduct periodic security audits to assess the application's dependencies and implement a robust patch management process to quickly address identified vulnerabilities.