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Threat Model Analysis for mislav/will_paginate

  • Threat: Excessive Data Exposure via per_page Manipulation

    • Description: An attacker modifies the per_page parameter in the URL (e.g., example.com/products?per_page=1000000) to request an extremely large number of records per page. They might do this by manually editing the URL, using automated tools, or crafting malicious links. will_paginate directly processes this parameter.
    • Impact:
      • Exposure of a larger-than-intended dataset.
      • Potential revelation of the total number of records in a table.
      • Increased database load, potentially leading to performance degradation or even a denial-of-service (DoS) if combined with other factors.
      • If combined with other vulnerabilities (e.g., insufficient authorization checks), it could lead to unauthorized data access.
    • will_paginate Component Affected:
      • The paginate method (and its underlying implementation) in the model. This method directly processes the per_page parameter and uses it in the database query.
      • View helpers that generate pagination links (e.g., will_paginate helper) – these helpers construct the URLs containing the per_page parameter, making the vulnerability exploitable.
    • Risk Severity: High
    • Mitigation Strategies:
      • Strict per_page Validation: Implement server-side validation to enforce a hard maximum allowed value for per_page. Use a whitelist approach (e.g., params[:per_page].to_i.clamp(1, 100)). Do not rely solely on client-side validation or the default per_page setting. This is the most critical mitigation.
      • Configuration-Based Limit: Define a global maximum per_page value in a configuration file (e.g., config/initializers/will_paginate.rb) and enforce it consistently across the application.
      • Database Query Optimization: Ensure that database queries are optimized to handle large result sets efficiently, even if a large (but still within the allowed limit) per_page value is requested.
  • Threat: Denial of Service via Large page Parameter

    • Description: An attacker provides an extremely large page number in the URL (e.g., example.com/products?page=999999999). They might do this manually, through automated tools, or by crafting malicious links. will_paginate directly uses this parameter to calculate the database offset.
    • Impact:
      • High database load, leading to slow response times or complete unavailability (DoS). This is the primary impact.
      • Potential exhaustion of database connections or other resources.
      • Degraded performance for all users of the application.
    • will_paginate Component Affected:
      • The paginate method in the model. This method directly processes the page parameter and calculates the database offset, which is the core of the vulnerability.
      • View helpers that generate pagination links (e.g., will_paginate helper) – these helpers construct the URLs containing the page parameter, making the attack possible.
    • Risk Severity: High
    • Mitigation Strategies:
      • page Parameter Validation: Implement server-side validation to limit the page parameter to a reasonable range. While a strict maximum is data-dependent, set a practical upper bound based on expected data volume and monitor for abuse. This is crucial.
      • Rate Limiting: Implement rate limiting on pagination requests, especially for endpoints known to be resource-intensive. This can prevent attackers from repeatedly requesting very large page numbers.
      • Database Query Optimization: Optimize database queries to handle large offsets efficiently. Use appropriate indexing. Consider using EXPLAIN (or your database's equivalent) to analyze query performance.
      • Keyset Pagination (Alternative): For very large datasets, consider using keyset pagination (cursor-based pagination) instead of offset-based pagination. This avoids the need to calculate large offsets, fundamentally mitigating this threat. This would require a different pagination solution or a custom implementation, as will_paginate doesn't natively support it.