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attack-surface.md

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Attack Surface Analysis for woltapp/blurhash

Attack Surface: Denial of Service (DoS) via Encoding Overload

  • Description: Attackers flood the server with requests to encode large images or use excessively high component counts, consuming CPU resources and causing a denial of service. This is specific to the encoding process of BlurHash.
  • How BlurHash Contributes: The BlurHash encoding algorithm's computational complexity increases significantly with image size and the requested component counts (X and Y). This inherent characteristic makes it a target.
  • Example: An attacker sends numerous requests to a BlurHash generation endpoint, each with a very large image (e.g., 10000x10000 pixels) and a high component count (e.g., 9x9). This overwhelms the server's CPU.
  • Impact: Application unavailability, service disruption.
  • Risk Severity: High
  • Mitigation Strategies:
    • Strict Input Validation: Enforce strict limits on the maximum dimensions (width and height) of images accepted for encoding.
    • Component Count Limits: Set reasonable maximum values for the X and Y component counts (e.g., a maximum of 9x9, or lower).
    • Rate Limiting: Implement rate limiting specifically on the BlurHash generation endpoint.
    • Asynchronous Processing: Offload BlurHash generation to a background queue.
    • Resource Monitoring: Monitor CPU usage to detect this specific type of overload.

Attack Surface: Code Execution via Buffer Overflow/Underflow (Decoding - Less Likely, but Critical)

  • Description: Attackers exploit buffer overflows or underflows in the decoder (especially in C/C++ implementations) using maliciously crafted BlurHash strings. This is a direct attack on the decoder's handling of the BlurHash string.
  • How BlurHash Contributes: While the vulnerability is in the decoder's implementation, the attack vector is a maliciously crafted BlurHash string designed to trigger the memory corruption.
  • Example: An attacker crafts a BlurHash string with specific byte sequences that, when processed by a vulnerable C/C++ decoder, overwrite memory, leading to arbitrary code execution.
  • Impact: Arbitrary code execution, complete system compromise.
  • Risk Severity: Critical (Although less likely with well-maintained libraries and memory-safe languages, the impact is severe.)
  • Mitigation Strategies:
    • Memory-Safe Languages: Prioritize using memory-safe languages (Rust, Go, Python, Java) for decoder implementations.
    • Code Review & Audit: For C/C++ decoders, perform rigorous code reviews and security audits, focusing on memory handling and input validation related to the BlurHash string.
    • Fuzz Testing: Use fuzz testing, specifically targeting the BlurHash decoder with a wide range of malformed and edge-case BlurHash strings.
    • Sandboxing: Consider running the decoder in a sandboxed environment to limit the impact of a successful exploit.
    • Up-to-Date Libraries: Use the latest version of well-maintained decoder libraries, and ensure they are patched against known vulnerabilities.