threat-modeling.md

Threat Model Analysis for ra1028/differencekit

Threat: Incorrect Diff Calculation

• Description: An attacker could provide crafted input data or exploit edge cases that cause differencekit's diffing algorithm to produce an incorrect difference. This could lead to the application displaying wrong critical information, performing unintended critical actions based on faulty UI updates, or corrupting critical data if the diff is used to update a sensitive data model. For example, in a financial application, an incorrect diff could lead to displaying an incorrect account balance, potentially leading to incorrect financial transactions initiated by the user based on the flawed UI.
• Impact: Data corruption (potentially critical), information disclosure (potentially critical if sensitive data is misrepresented), unauthorized actions (if UI controls critical functions), application logic errors leading to severe consequences, UI inconsistencies that mislead users into critical errors.
• Affected DifferenceKit Component: Differentiable protocol implementation, difference(from:to:) functions, core diffing algorithms within the library.
• Risk Severity: High (can lead to significant data integrity issues and potentially critical application errors).
• Mitigation Strategies:
  • Implement extremely thorough and rigorous unit and integration tests specifically focused on edge cases, boundary conditions, and potentially malicious input patterns designed to test the limits and correctness of the diffing algorithm.
  • Employ property-based testing extensively to automatically generate a vast range of diverse and potentially problematic input data to rigorously verify the correctness of diff calculations under stress.
  • Conduct intensive manual UI/UX testing, particularly focusing on critical workflows and data displays, to ensure absolute visual consistency and data accuracy after diff updates, simulating various error scenarios and edge cases.
  • Implement robust server-side validation and authorization checks for all critical actions, ensuring that no critical operations are solely reliant on potentially flawed UI updates driven by differencekit. Treat the UI as untrusted input for critical operations.

Threat: Incorrect Patch Application

• Description: Even if differencekit calculates a correct diff, a vulnerability in the application's code responsible for applying the ChangeSet (patch) to the UI or data model could lead to critical errors. An attacker might not directly target differencekit, but exploit weaknesses in the application's patch application logic. For instance, if the application incorrectly interprets or applies the ChangeSet, especially in complex scenarios or edge cases, it could result in critical data corruption, application crashes in sensitive modules, or UI glitches that lead to user errors with severe consequences. In a medical device application, a flawed patch application could lead to incorrect display of vital patient data, potentially causing misdiagnosis or incorrect treatment.
• Impact: Data corruption (potentially critical), UI inconsistencies leading to critical user errors, application crashes in critical modules, unexpected and potentially dangerous application behavior.
• Affected DifferenceKit Component: Application code using ChangeSet to update UI or data models, specifically the logic that interprets and applies the changes described in the ChangeSet.
• Risk Severity: High (can lead to critical data integrity issues, application instability in critical areas, and user errors with severe consequences).
• Mitigation Strategies:
  • Implement extremely rigorous testing of the application's patch application logic, with a strong focus on handling complex ChangeSet scenarios, edge cases, and error conditions. Use fuzzing techniques to generate unexpected ChangeSet inputs and test application robustness.
  • Implement multiple layers of data validation and integrity checks after applying diffs, especially for critical data. Use checksums, data invariants, and redundancy to detect and prevent propagation of errors caused by incorrect patching.
  • Design and implement robust rollback or undo mechanisms specifically for critical data updates, allowing the application to revert to a known good state if patch application fails or leads to errors in sensitive data.
  • Employ defensive programming techniques extensively when handling ChangeSet and applying updates, including comprehensive error handling, input validation of ChangeSet data, and assertions to detect unexpected states during patch application. Consider using formal verification methods for critical patch application logic if feasible.