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Attack Surface Analysis for toptal/chewy

Attack Surface: Elasticsearch Query Injection

1. Elasticsearch Query Injection

  • Description: Attackers inject malicious Elasticsearch query syntax through user input into application queries built using Chewy, leading to unauthorized data access, modification, or denial of service.
  • Chewy Contribution: Chewy simplifies Elasticsearch query construction, but if developers directly embed unsanitized user input into Chewy's query DSL or raw Elasticsearch queries, Chewy becomes the conduit for this injection vulnerability. Chewy's abstraction might mask the underlying Elasticsearch query complexity, potentially leading to developers overlooking injection risks.
  • Example: An application uses Chewy to filter blog posts based on user-provided tags: PostIndex.filter(terms: { tags: params[:tags] }). If params[:tags] is not sanitized and contains a crafted payload like ["tag1", "} OR _exists_:sensitive_field OR { " ], it could modify the intended query to bypass tag filtering and expose posts with a sensitive_field, which should not be accessible.
  • Impact:
    • Unauthorized access to sensitive data within Elasticsearch indices.
    • Data exfiltration by retrieving unintended information.
    • Data modification or deletion if the Elasticsearch user has write permissions.
    • Denial of Service through resource-intensive or malformed queries that overload Elasticsearch.
    • Bypass of application-level access controls and intended search logic.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Parameterize Queries: Utilize Chewy's query DSL features that support parameterization or prepared statements to separate query structure from user input. Avoid string interpolation of user input directly into queries.
    • Input Sanitization and Validation: Thoroughly sanitize and validate all user input before incorporating it into search queries. Use allowlists for acceptable characters and patterns relevant to search terms.
    • Query DSL Abstraction: Favor using Chewy's higher-level query DSL abstractions instead of raw Elasticsearch queries whenever possible, as they often provide safer ways to construct queries.
    • Regular Code Review: Conduct regular code reviews specifically focused on Chewy query construction to identify and remediate potential injection points.

Attack Surface: Dynamic Mapping Exploitation

2. Dynamic Mapping Exploitation

  • Description: Attackers leverage Chewy's interaction with Elasticsearch's dynamic mapping feature to inject unexpected fields with arbitrary data types into indices by manipulating data sent for indexing. This can lead to data corruption, denial of service, or information disclosure.
  • Chewy Contribution: Chewy, by default, might allow dynamic mapping in Elasticsearch indices if not explicitly configured to use strict, pre-defined mappings. Chewy's ease of indexing can inadvertently facilitate the exploitation of dynamic mapping if developers are not aware of the security implications.
  • Example: An application indexes product data using Chewy. If dynamic mapping is enabled, an attacker could submit a product with a malicious field like "is_admin": true during product creation or update. If the application logic or search results later rely on the index schema without expecting this field, it could lead to unintended behavior or even privilege escalation if the application mistakenly interprets this injected field. Alternatively, injecting a large number of unique fields can cause a mapping explosion, leading to Elasticsearch performance degradation and DoS.
  • Impact:
    • Data corruption and integrity issues within Elasticsearch indices.
    • Denial of Service (mapping explosion, performance degradation of Elasticsearch).
    • Information disclosure through unexpected indexed fields that might be inadvertently exposed in search results or APIs.
    • Potential bypass of application logic or security checks based on assumptions about the index schema.
  • Risk Severity: High
  • Mitigation Strategies:
    • Explicit Mapping Definition: Define explicit and strict mappings for all indices using Chewy's index definition features. Disable dynamic mapping at the Elasticsearch index level to enforce schema control.
    • Input Validation during Indexing: Rigorous validation of data being indexed against the defined schema before sending it to Elasticsearch via Chewy. Reject data that does not conform to the expected structure.
    • Schema Enforcement: Implement server-side schema enforcement in the application layer to ensure data conforms to the expected structure before indexing with Chewy.
    • Regular Mapping Review: Periodically review and update index mappings to ensure they remain aligned with application requirements and security best practices, and to remove any unexpected or malicious fields.

Attack Surface: Data Injection during Synchronization (Unsanitized Data leading to Stored XSS)

3. Data Injection during Synchronization (Unsanitized Data leading to Stored XSS)

  • Description: If Chewy is used to synchronize data from a potentially vulnerable source (like an application database susceptible to stored XSS) to Elasticsearch without proper sanitization during the indexing process, it can propagate stored XSS vulnerabilities into the search index. This results in XSS execution when search results are displayed.
  • Chewy Contribution: Chewy acts as the synchronization mechanism. If the data pipeline through Chewy from the source to Elasticsearch lacks sanitization, Chewy directly facilitates the propagation of unsanitized, potentially malicious content into the search index.
  • Example: An application database has a stored XSS vulnerability in user profile descriptions. Chewy indexes user profiles from this database. When search results display user profiles, the XSS payload from the database, now indexed by Chewy in Elasticsearch, is executed in users' browsers when they view search results containing the affected profiles.
  • Impact:
    • Stored Cross-Site Scripting (XSS) vulnerabilities within the application, triggered through search results.
    • Compromise of user accounts and sessions through XSS attacks.
    • Potential for phishing, malware distribution, and other malicious activities via XSS.
    • Damage to application reputation and user trust.
  • Risk Severity: Critical (due to the severity of XSS vulnerabilities).
  • Mitigation Strategies:
    • Input Sanitization Before Indexing (for XSS): Sanitize data specifically for HTML context before it is indexed into Elasticsearch via Chewy, even if the data is already sanitized for database storage. Apply output encoding appropriate for HTML display in search results.
    • Address Source Vulnerabilities: Prioritize fixing the underlying vulnerabilities in the data source (e.g., database stored XSS vulnerabilities). Sanitization in Chewy is a defense-in-depth measure, not a replacement for fixing source issues.
    • Content Security Policy (CSP): Implement a strong Content Security Policy (CSP) to mitigate the impact of potential XSS vulnerabilities, including those that might originate from search results.
    • Regular Security Audits and XSS Testing: Conduct regular security audits and penetration testing, specifically including XSS testing of search functionality and data indexing pipelines involving Chewy.