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attack-surface.md

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Attack Surface Analysis for woltapp/blurhash

Attack Surface: Malformed Blurhash String Decoding

  • Description: Processing maliciously crafted or invalid blurhash strings can lead to significant Denial of Service (DoS) or unexpected application behavior due to flaws in the decoding logic.
    • Blurhash Contribution: The core functionality of blurhash is decoding strings. Vulnerabilities in the parsing and decoding process directly expose this attack surface.
    • Example: An attacker sends a flood of requests with extremely long or deeply nested blurhash strings. The decoding function attempts to parse these complex strings, consuming excessive CPU resources and memory, leading to a Denial of Service for legitimate users and potentially crashing the application.
    • Impact: High - Denial of Service (DoS) impacting application availability and user experience. Potential for application instability or crashes.
    • Risk Severity: High
    • Mitigation Strategies:
      • Strict Input Validation: Implement rigorous validation of blurhash strings before decoding. Enforce strict length limits, character set restrictions, and adherence to the defined blurhash format. Reject any strings that do not conform to these rules.
      • Robust Error Handling and Resource Limits: Implement comprehensive error handling within the decoding function to gracefully manage invalid input without crashing. Set strict timeouts and resource limits (CPU time, memory usage) for the decoding process to prevent resource exhaustion from malicious inputs.
      • Security Audits and Fuzzing: Conduct regular security audits of the blurhash integration and consider fuzzing the decoding function with a wide range of malformed and edge-case blurhash strings to identify potential parsing vulnerabilities.

Attack Surface: Resource Exhaustion during Decoding (DoS)

  • Description: Crafted blurhash strings can be designed to maximize the computational resources required for decoding, leading to a Denial of Service (DoS) by overloading the server or client processing the decoding.
    • Blurhash Contribution: The decoding algorithm, while designed to be efficient for typical use cases, has a computational complexity that can be exploited with specific input parameters (e.g., manipulating the number of components within allowed limits).
    • Example: An attacker sends numerous requests to decode blurhash strings with a high number of components (numX, numY) pushed towards the upper limits of the specification. While technically valid blurhashes, decoding these strings requires significantly more computation. Repeated requests can exhaust server CPU resources, leading to a DoS and preventing legitimate users from accessing the application.
    • Impact: High - Denial of Service (DoS) impacting application availability and user experience. Server overload and potential service disruption.
    • Risk Severity: High
    • Mitigation Strategies:
      • Resource Limits and Timeouts: Implement strict resource limits (CPU time, memory usage) and timeouts for the blurhash decoding process. Prevent decoding operations from consuming excessive resources.
      • Rate Limiting and Request Queuing: Implement rate limiting on blurhash decoding requests, especially if exposed to public input. Use request queues to manage and prioritize decoding tasks, preventing a flood of requests from overwhelming the system.
      • Complexity Analysis and Optimization: Analyze the computational complexity of the decoding algorithm and optimize it where possible to reduce resource consumption for complex blurhashes. Consider limiting the maximum allowed number of components (numX, numY) to reduce the potential for computationally expensive decoding operations, if application requirements allow.
      • Monitoring and Alerting: Implement monitoring of server resource usage during blurhash decoding. Set up alerts to detect unusual spikes in CPU or memory consumption that might indicate a DoS attack.