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Attack Tree Analysis for mislav/will_paginate

Objective: Compromise application using will_paginate via High-Risk Attack Paths. (Focus on DoS for High-Risk Paths)

Attack Tree Visualization

Attack Goal: **Compromise Application using will_paginate** (Critical Node)

    └─── **1. Exploit Pagination Logic Vulnerabilities** (Critical Node)
        ├─── **1.1. Parameter Manipulation** (Critical Node)
        │   ├─── **1.1.1. Large Page Number Attack (DoS)** (Critical Node) - **HIGH-RISK PATH**
        │   │   └─── 1.1.1.1. Send requests with extremely high page numbers
        │   │       └─── Insight: Application might attempt to calculate offsets based on excessively large numbers, leading to performance degradation or resource exhaustion.
        │   └─── **1.1.5. Parameter Overflow/Integer Overflow (DoS/Error)** (Critical Node) - **HIGH-RISK PATH**
        │       └─── 1.1.5.1. Send requests with extremely large integer page numbers exceeding system limits
        │           └─── Insight:  Could lead to integer overflow issues in backend calculations, potentially causing errors, crashes, or DoS.
        └─── **1.2. Inefficient Query Generation (DoS)** (Critical Node) - **HIGH-RISK PATH**
            ├─── **1.2.1. Exploit Complex Queries with Large Datasets** (Critical Node) - **HIGH-RISK PATH**
            │   └─── 1.2.1.1. Trigger pagination on endpoints with very large datasets and potentially complex filtering/sorting
            │       └─── Insight: `will_paginate` generates SQL queries with `LIMIT` and `OFFSET`.  For very large datasets and deep pagination (high page numbers), `OFFSET` can become inefficient in some database systems, leading to slow query execution and DoS.
            └─── **1.2.3. Pagination on Resource-Intensive Operations** (Critical Node) - **HIGH-RISK PATH**
                └─── 1.2.3.1. Trigger pagination on endpoints that perform resource-intensive operations beyond simple database queries (e.g., complex calculations, external API calls per item).
                    └─── Insight:  If each paginated item retrieval involves significant processing, even with efficient pagination queries, attackers can overload the server by requesting many pages, triggering these resource-intensive operations repeatedly.

Attack Tree Path: Large Page Number Attack (DoS)

  • Attack Vector: Sending requests with extremely high page numbers (e.g., page=999999999).
  • Mechanism: Application attempts to calculate large OFFSET values in database queries, leading to performance degradation.
  • Impact: Denial of Service (Availability loss) due to slow queries and resource exhaustion.
  • Likelihood: High - trivial to execute.
  • Effort: Low - minimal effort required.
  • Skill Level: Low - script kiddie level.
  • Detection Difficulty: Low - easily detectable through web logs and performance monitoring.
  • Mitigation:
    • Implement input validation to limit maximum page number.
    • Consider efficient pagination techniques like cursor-based pagination for large datasets.
    • Implement rate limiting.

Attack Tree Path: Parameter Overflow/Integer Overflow (DoS/Error)

  • Attack Vector: Sending requests with extremely large integer page numbers that might exceed system limits.
  • Mechanism: Integer overflow in backend calculations related to page numbers and limits, potentially causing errors or crashes.
  • Impact: Denial of Service (Availability loss), potential errors and unexpected behavior.
  • Likelihood: Medium - depends on system architecture and handling of large integers.
  • Effort: Low - easy to send large integer values.
  • Skill Level: Low - basic understanding of integer limits.
  • Detection Difficulty: Low - errors and crashes might be logged, performance monitoring can reveal issues.
  • Mitigation:
    • Input validation to limit maximum page number to a safe range.
    • Use appropriate data types in backend calculations to prevent integer overflow.

Attack Tree Path: Inefficient Query Generation (DoS)

  • Attack Vector: Exploiting the OFFSET-based pagination generated by will_paginate when used with large datasets and complex queries.
  • Mechanism: OFFSET becomes inefficient for deep pagination, leading to slow database queries and increased load.
  • Impact: Denial of Service (Availability loss) due to slow response times and database overload.
  • Likelihood: Medium - depends on application's data volume and query complexity.
  • Effort: Low to Medium - requires identifying vulnerable endpoints.
  • Skill Level: Low to Medium - basic understanding of database performance.
  • Detection Difficulty: Medium - might be initially mistaken for normal slow queries, requires performance monitoring.
  • Mitigation:
    • For large datasets, consider cursor-based pagination or other efficient methods.
    • Optimize database queries and indexes.
    • Implement caching for paginated results.
    • Limit page size (per_page).

Attack Tree Path: Exploit Complex Queries with Large Datasets

  • Attack Vector: Specifically targeting pagination on endpoints that retrieve very large datasets and involve complex filtering or sorting.
  • Mechanism: Combination of large datasets, complex queries, and OFFSET-based pagination exacerbates performance issues.
  • Impact: Denial of Service (Availability loss) - severe performance degradation and potential database crash.
  • Likelihood: Medium - requires identifying specific vulnerable endpoints.
  • Effort: Medium - requires some endpoint analysis.
  • Skill Level: Medium - understanding of database query performance and application endpoints.
  • Detection Difficulty: Medium - requires monitoring of slow queries and database performance.
  • Mitigation:
    • Focus on efficient pagination strategies for these specific endpoints.
    • Optimize complex queries and database indexes.
    • Consider data partitioning or sharding for very large datasets.

Attack Tree Path: Pagination on Resource-Intensive Operations

  • Attack Vector: Triggering pagination on endpoints that perform resource-intensive operations for each paginated item.
  • Mechanism: Repeatedly executing resource-intensive operations for each page request, leading to server overload.
  • Impact: Denial of Service (Availability loss) - server overload and slow response times.
  • Likelihood: Medium - depends on application design and resource intensity of operations.
  • Effort: Low - easy to send multiple page requests once identified.
  • Skill Level: Low - basic understanding of web requests.
  • Detection Difficulty: Medium - might be harder to distinguish from legitimate heavy load initially, requires monitoring resource usage per endpoint.
  • Mitigation:
    • Optimize resource-intensive operations (caching, background processing).
    • Limit or disable pagination for such endpoints.
    • Implement stricter rate limiting.
    • Consider asynchronous processing for resource-intensive tasks.