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Threat Model Analysis for woltapp/blurhash

Threat: Excessive Detail Reconstruction

  • Description: An attacker analyzes a Blurhash string, potentially generated with a high number of components, or compares multiple Blurhashes of similar images. They use specialized tools or techniques to reconstruct a more detailed approximation of the original image than intended, revealing sensitive visual information. The attacker might use publicly available or custom-built de-blurring tools.
  • Impact:
    • Privacy Violation: Sensitive details of the original image are revealed, potentially exposing personal information, confidential data, or other private content.
    • Reputational Damage: If the application handles sensitive images, a successful reconstruction could damage the application's reputation and user trust.
    • Regulatory Non-compliance: Depending on the nature of the data and applicable regulations (e.g., GDPR, CCPA), this could lead to legal and financial penalties.
  • Affected Blurhash Component:
    • encode function (specifically, the xComponents and yComponents parameters).
    • The resulting Blurhash string itself.
  • Risk Severity: High (if sensitive images are used)
  • Mitigation Strategies:
    • Minimize Components: Use the absolute minimum number of xComponents and yComponents required for the desired level of blur. Conduct thorough testing to determine this minimum.
    • Consistent Component Use: Apply the same xComponents and yComponents values consistently across all images of a similar type and sensitivity level.
    • Pre-processing Downsampling: Downsample the original image to a very low resolution before encoding it with Blurhash. This limits the initial detail available.
    • Privacy Impact Assessment: Conduct a formal privacy impact assessment to evaluate the risks associated with using Blurhash for the specific image types.

Threat: Encoding-Based Denial of Service (Server-Side)

  • Description: An attacker crafts a malicious image (e.g., extremely large, high-frequency patterns, specific color palettes) and uploads it to the server. This image is designed to cause the Blurhash encode function to consume excessive CPU resources or memory, leading to a denial-of-service condition on the server. The attacker repeatedly uploads such images to amplify the effect.
  • Impact:
    • Service Unavailability: The application becomes unresponsive or unavailable to legitimate users.
    • Resource Exhaustion: Server resources (CPU, memory) are depleted, potentially affecting other applications or services running on the same server.
    • Financial Loss: If the application is critical for business operations, downtime can lead to financial losses.
  • Affected Blurhash Component:
    • encode function.
  • Risk Severity: High
  • Mitigation Strategies:
    • Strict Image Size Limits: Enforce strict limits on the dimensions (width, height) and file size of uploaded images before they reach the encode function.
    • Resource Limits (Encoding): Implement resource limits (CPU time, memory allocation) for the encode function. Terminate the process if these limits are exceeded.
    • Rate Limiting (Uploads): Implement rate limiting on the image upload endpoint to prevent an attacker from submitting a large number of malicious images quickly.
    • Input Validation (Image Format): Validate the image format and basic structure before passing it to the encode function. Reject malformed or suspicious images.
    • Timeout: Implement timeout for encoding process.