attack-surface.md

Attack Surface Analysis for ra1028/differencekit

Attack Surface: Denial of Service (DoS) via Algorithmic Complexity

• Description: An attacker can exploit the computational complexity of differencekit's diffing algorithms by providing crafted input data. This input triggers worst-case performance scenarios, leading to excessive CPU and memory consumption, effectively causing a denial of service on the application.
• How DifferenceKit Contributes: differencekit's core functionality is to calculate differences between collections. The efficiency of these calculations depends on the input data. Specifically designed, large, or complex datasets can force differencekit into computationally expensive operations, exceeding reasonable resource limits.
• Example: An application displays a real-time dashboard using data updated via a websocket. differencekit is used to efficiently update the UI list based on incoming data. An attacker, by manipulating the websocket data stream, sends extremely large and intricately structured datasets. When differencekit processes these datasets to calculate UI updates, it consumes all available CPU resources on the user's device, making the dashboard unresponsive and effectively denying the user access to critical real-time information.
• Impact: Application becomes unusable, critical functionalities are disrupted, user experience is severely degraded, potential for business disruption if the application is essential for operations.
• Risk Severity: High
• Mitigation Strategies:
  • Strict Input Validation and Sanitization: Implement rigorous validation and sanitization of all data used as input for differencekit. Define and enforce strict limits on the size and complexity of collections. Reject or truncate data exceeding these limits.
  • Aggressive Performance Testing and Profiling: Conduct extensive performance testing under heavy load and with adversarial datasets designed to maximize diffing complexity. Profile application performance specifically focusing on differencekit operations to identify bottlenecks and resource consumption patterns.
  • Resource Quotas and Timeouts: Implement strict resource quotas (CPU, memory) and timeouts for differencekit operations. If diffing operations exceed predefined limits, terminate them and implement fallback mechanisms to prevent resource exhaustion.
  • Rate Limiting and Throttling: If data is received from external sources (e.g., network), implement rate limiting and throttling to control the frequency and volume of data processed by differencekit, preventing attackers from overwhelming the application with malicious data.
  • Consider Asynchronous Diffing and Background Processing: Offload diffing operations to background threads or processes to prevent blocking the main UI thread and maintain application responsiveness even under heavy load. This can mitigate the immediate DoS impact on the user interface.