attack-surface.md

Attack Surface Analysis for toptal/chewy

Attack Surface: 1. Elasticsearch Query Injection

• Description: Attackers craft malicious input to manipulate Elasticsearch queries, bypassing intended security controls and potentially accessing or modifying data they shouldn't.
• How Chewy Contributes: Chewy's query DSL, while generally helpful, can be misused to construct queries directly from user input without proper sanitization or escaping. The ease of building complex queries increases the risk if not handled carefully. This is the primary attack vector directly related to Chewy.
• Example:

  # Vulnerable code:
  query_string = params[:search] # User-provided input
  MyIndex.query(query_string: { query: query_string })
  
  # Attacker input:  "name:admin') OR 1=1 --"

  This could bypass intended filters and return all documents.
• Impact:
  • Unauthorized data access (reading, modifying, deleting).
  • Denial of service (by crafting expensive queries).
  • Potential for further exploitation (if combined with other vulnerabilities).
• Risk Severity: Critical
• Mitigation Strategies:
  • Use Chewy's DSL correctly: Employ methods like query, filter, term, match, etc., which handle escaping. Never directly embed user input into raw query strings.
  • Input Validation: Implement strict validation before constructing the query. Validate data types, lengths, and allowed characters.
  • Sanitization: If you must use user input in a way that isn't automatically handled by the DSL, sanitize it thoroughly (though validation is preferred).
  • Parameterized Queries: Although Chewy doesn't have direct parameterized query support like SQL databases, using the DSL methods effectively achieves the same security benefits.
  • Principle of Least Privilege: Ensure the Elasticsearch user Chewy connects with has only the necessary permissions (read-only if appropriate).

Attack Surface: 2. Index Setting Misconfiguration (Overly Permissive Mappings) - Specifically due to Chewy's DSL

• Description: Incorrectly configured index mappings, specifically facilitated by Chewy's DSL, can lead to unexpected data storage, potential information disclosure, or denial-of-service. The core issue is Elasticsearch configuration, but Chewy's DSL is the direct means of creating this misconfiguration.
• How Chewy Contributes: Chewy's index definition DSL makes it easy to define mappings, but also easy to make mistakes. Developers might not fully understand the implications of each setting, and the DSL's convenience can lead to overlooking security best practices.
• Example:

  # Vulnerable index definition:
  define_type MyModel do
    field :user_input, type: 'text' # Too broad, should be 'keyword' if not analyzed
  end

  An attacker could inject a very long string into user_input, potentially causing performance issues. Or, if dynamic mapping is enabled globally (also configurable via Chewy), unexpected fields could be created.
• Impact:
  • Index bloat (excessive storage consumption).
  • Performance degradation.
  • Potential denial of service.
  • Indirect information disclosure (through dynamic field creation).
• Risk Severity: High
• Mitigation Strategies:
  • Explicit Mappings: Define all index mappings explicitly within the Chewy DSL. Avoid relying on dynamic mapping unless absolutely necessary and carefully controlled through Chewy's configuration.
  • Precise Field Types: Use the most specific field type possible (e.g., keyword, integer, date) within the Chewy DSL. Avoid text unless full-text search is required on that field.
  • Review and Audit: Regularly review and audit index settings defined within Chewy.
  • Limit Field Lengths: Use Chewy's fields options to set maximum lengths for text fields where appropriate.

Attack Surface: 3. Data Exposure in Index - Due to Chewy's Indexing Mechanism

• Description: Storing sensitive data directly in the Elasticsearch index, specifically because Chewy is used to define what gets indexed.
• How Chewy Contributes: Chewy's ease of indexing, and its central role in defining which model attributes are indexed, makes it the direct point of control for preventing this vulnerability.
• Example:

  # Vulnerable model:
  class User < ApplicationRecord
    # ...
    def self.chewy_index
      # ...
      field :password_hash # DO NOT INDEX THIS!  Chewy is directly responsible for this.
      # ...
    end
  end

• Impact: Direct access to sensitive data if the Elasticsearch cluster is compromised.
• Risk Severity: Critical
• Mitigation Strategies:
  • Never Index Sensitive Data: Exclude sensitive fields from the index definition within Chewy's chewy_index block using Chewy's fields option or by simply not listing them. This is the primary mitigation, directly controlled by how Chewy is used.
  • Data Masking/Tokenization: If you need to search on a sensitive field, consider tokenizing or masking it before indexing (this would be implemented in conjunction with Chewy's indexing logic).
  • Field-Level Encryption: If absolutely necessary, use Elasticsearch's field-level encryption (configured through Chewy's mapping definitions).

Attack Surface: 4. Insecure Update Strategies - Misuse of Chewy's API

• Description: Using Chewy's update strategies (e.g., atomic, sidekiq) incorrectly, specifically through improper calls to Chewy's API, can lead to race conditions or data inconsistencies.
• How Chewy Contributes: The vulnerability arises from how the developer chooses to use Chewy's provided update mechanisms. The choice of strategy and how it's invoked are directly within Chewy's API.
• Example:
  • Using MyIndex.import(my_objects, strategy: :sidekiq) without proper locking or concurrency handling in the application code that triggers the import. The choice of :sidekiq and the lack of surrounding concurrency control are the direct contributors.
• Impact:
  • Data loss or corruption.
  • Data inconsistency.
• Risk Severity: High
• Mitigation Strategies:
  • Choose the Right Strategy: Carefully select the update strategy within Chewy's API calls based on the application's needs. atomic is generally safest for simple cases.
  • Concurrency Control: If using asynchronous updates (like :sidekiq through Chewy), implement proper locking or other concurrency control mechanisms in the application code that interacts with Chewy.
  • Error Handling: Implement robust error handling and retry mechanisms for asynchronous updates initiated through Chewy.