Skip to content

Latest commit

 

History

History
114 lines (101 loc) · 11.9 KB

mitigations.md

File metadata and controls

114 lines (101 loc) · 11.9 KB

Mitigation Strategies Analysis for woltapp/blurhash

Mitigation Strategy: Limit Image Size for Blurhash Generation

  • Mitigation Strategy: Limit Image Size for Blurhash Generation
  • Description:
    1. Define Maximum Dimensions: Determine acceptable maximum width and height for images used to generate blurhashes. Base this on application needs and server resource capacity. For example, set a limit of 2048x2048 pixels.
    2. Implement Server-Side Validation: On the server-side, before processing any uploaded image for blurhash generation, check its dimensions using an image processing library.
    3. Reject Oversized Images: If an image exceeds the defined maximum dimensions, reject the upload and return an error message to the user, explaining the size limit.
    4. Client-Side Pre-validation (Optional but Recommended): Implement client-side JavaScript validation to check image dimensions before uploading. This provides immediate feedback and reduces unnecessary server requests.
  • Threats Mitigated:
    • Resource Exhaustion (High Severity): Attackers could upload extremely large images to consume excessive server CPU and memory during blurhash encoding.
    • Denial of Service (DoS) (High Severity): Repeatedly sending large images can overload the server, causing a DoS condition.
  • Impact:
    • Resource Exhaustion: Significantly reduces the risk by preventing processing of excessively large images for blurhash generation.
    • Denial of Service (DoS): Moderately reduces the risk by limiting the impact of large image uploads on blurhash processing.
  • Currently Implemented:
    • Server-Side Validation: Implemented in the image upload endpoint (/api/upload) using an image processing library to check dimensions before blurhash generation.
  • Missing Implementation:
    • Client-Side Pre-validation: Not yet implemented on the frontend. Should be added to the image upload form using JavaScript to improve user experience and reduce server load.

Mitigation Strategy: Control Blurhash Component Count

  • Mitigation Strategy: Control Blurhash Component Count
  • Description:
    1. Establish Recommended Range: Define a recommended range for X and Y component counts for blurhash generation. For example, recommend 4-6 components for both X and Y to balance blur quality and performance.
    2. Enforce Maximum Component Count: Implement server-side validation to enforce a maximum allowed component count for blurhash generation requests. Reject requests exceeding this limit. For example, set a maximum of 8 components for both X and Y.
    3. Default to Safe Values: Set default component counts to values within the recommended range if the user or application doesn't explicitly specify them.
    4. Document Recommendations: Clearly document the recommended component count range and the rationale for developers and users generating blurhashes.
  • Threats Mitigated:
    • Resource Exhaustion (Medium Severity): High component counts increase processing time for both encoding and decoding, potentially leading to resource exhaustion, especially on the server-side during generation.
    • Client-Side Performance Issues (Medium Severity): Decoding blurhashes with very high component counts can impact client-side performance, especially on low-powered devices.
  • Impact:
    • Resource Exhaustion: Moderately reduces the risk by limiting the computational complexity of blurhash generation and decoding.
    • Client-Side Performance Issues: Moderately reduces the risk of client-side performance problems by preventing excessively complex blurhashes.
  • Currently Implemented:
    • Default Values: Default component counts are set to 4x4 in the blurhash generation service.
  • Missing Implementation:
    • Maximum Component Count Enforcement: Server-side validation to enforce a maximum component count is not yet implemented in the blurhash generation API endpoint.
    • Documentation: Recommendations for component counts are not yet formally documented for developers.

Mitigation Strategy: Cache Generated Blurhashes

  • Mitigation Strategy: Cache Generated Blurhashes
  • Description:
    1. Choose Caching Mechanism: Select a caching mechanism (e.g., Redis, Memcached, or database cache).
    2. Implement Cache Key Generation: Define a strategy to generate unique cache keys for blurhashes. This could be based on a hash of the image data, image URL, or a unique image identifier.
    3. Cache Lookup Before Generation: Before generating a blurhash, check if a blurhash for the corresponding image already exists in the cache using the generated cache key.
    4. Serve from Cache if Available: If a cached blurhash is found, serve it directly from the cache, bypassing the blurhash generation process.
    5. Cache Newly Generated Blurhashes: If a blurhash is not found in the cache, generate it, store it in the cache using the cache key, and then serve it.
    6. Cache Invalidation Strategy (Optional): If images can be updated, implement a cache invalidation strategy to ensure users see blurhashes based on the latest image versions.
  • Threats Mitigated:
    • Resource Exhaustion (Medium Severity): Repeatedly generating blurhashes for the same images wastes server resources.
    • Performance Degradation (Medium Severity): Unnecessary blurhash generation slows down response times, especially for frequently accessed images.
  • Impact:
    • Resource Exhaustion: Moderately reduces the risk by significantly reducing the number of blurhash generation operations.
    • Performance Degradation: Moderately reduces performance degradation by serving cached blurhashes quickly.
  • Currently Implemented:
    • Basic In-Memory Cache: A simple in-memory cache is used in the blurhash service, but it's not persistent.
  • Missing Implementation:
    • Persistent Cache: Implementation of a persistent cache (e.g., using Redis) is missing for better scalability and persistence across service restarts.
    • Robust Cache Key Generation: A more robust cache key generation strategy based on image content hash should be implemented instead of relying solely on image URLs.

Mitigation Strategy: Limit Component Count for Client-Side Decoding

  • Mitigation Strategy: Limit Component Count for Client-Side Decoding
  • Description:
    1. Establish Client-Side Decoding Limit: Determine a reasonable maximum component count for blurhashes that will be decoded on the client-side, considering performance on lower-powered devices.
    2. Enforce Limit During Generation: When generating blurhashes intended for client-side decoding, ensure the component counts do not exceed the established client-side limit. This might involve different generation profiles for different use cases.
    3. Document Client-Side Recommendations: Document the recommended component count limits for optimal client-side performance for developers.
  • Threats Mitigated:
    • Client-Side Performance Issues (Medium Severity): Decoding blurhashes with very high component counts can freeze or slow down the user interface in web browsers, especially on mobile devices.
    • Client-Side DoS (Low Severity): In extreme cases, excessively complex blurhashes could theoretically be used to cause a client-side DoS by consuming excessive browser resources.
  • Impact:
    • Client-Side Performance Issues: Moderately reduces the risk of client-side performance problems by ensuring blurhashes are reasonably complex for decoding.
    • Client-Side DoS: Minimally reduces the risk of client-side DoS, as this is a less likely scenario.
  • Currently Implemented:
    • Implicit Limit through Default Values: Default component counts (4x4) implicitly limit client-side decoding complexity to a reasonable level.
  • Missing Implementation:
    • Explicit Client-Side Limit Enforcement: No explicit enforcement of client-side component count limits beyond the default values. Consider adding configuration options or different generation profiles for client-side vs. server-side blurhash usage.
    • Documentation: Client-side component count recommendations are not yet formally documented.

Mitigation Strategy: Evaluate Suitability for Highly Sensitive Images

  • Mitigation Strategy: Evaluate Suitability for Highly Sensitive Images
  • Description:
    1. Assess Data Sensitivity: For each use case where blurhash is considered, carefully assess the sensitivity of the images being represented.
    2. Consider Information Leakage: Understand that blurhash, while blurry, still encodes a reduced representation of the image's color and structure. Evaluate if even this minimal information leakage is acceptable for highly sensitive data.
    3. Choose Alternatives if Necessary: If the images are extremely sensitive and any information leakage is unacceptable, consider alternative approaches that do not involve any form of image representation, even blurred ones. For example, use generic placeholders or restrict access entirely.
  • Threats Mitigated:
    • Information Leakage (Low Severity, Context Dependent): Blurhash could potentially leak minimal information about the original image's content, which might be a concern in extremely sensitive contexts.
  • Impact:
    • Information Leakage: Reduces the risk of unintended information leakage by prompting careful consideration of blurhash's suitability for sensitive data.
  • Currently Implemented:
    • None: No specific process is in place to evaluate the suitability of blurhash for sensitive images.
  • Missing Implementation:
    • Sensitivity Assessment Guidelines: Develop guidelines or a checklist to help developers assess the sensitivity of images and determine if blurhash is appropriate.

Mitigation Strategy: Minimize Component Count for Sensitive Images (If Used)

  • Mitigation Strategy: Minimize Component Count for Sensitive Images (If Used)
  • Description:
    1. Identify Sensitive Image Use Cases: Identify specific use cases where blurhash is used for images that are considered somewhat sensitive (even if not highly sensitive).
    2. Reduce Component Count for Sensitive Cases: For these identified use cases, configure blurhash generation to use lower component counts than the default or recommended values. Lower component counts result in a more abstract and less revealing blurhash.
    3. Balance Blur Quality and Privacy: Find a balance between blur quality (still recognizable as a placeholder) and privacy by experimenting with different lower component counts.
  • Threats Mitigated:
    • Information Leakage (Low Severity, Context Dependent): For images with some level of sensitivity, minimizing component count further reduces the already minimal risk of information leakage from the blurhash.
  • Impact:
    • Information Leakage: Minimally reduces the risk of information leakage for sensitive images by making the blurhash more abstract.
  • Currently Implemented:
    • None: No specific configuration or logic to minimize component counts for sensitive images.
  • Missing Implementation:
    • Configuration for Sensitive Image Handling: Implement configuration options or logic to allow specifying lower component counts for blurhash generation in specific contexts where images are considered more sensitive.