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Mitigation Strategies Analysis for 99designs/gqlgen

Mitigation Strategy: Custom Input Validation in Resolvers

  • Description:

    1. Identify input fields in your GraphQL schema that require validation beyond gqlgen's basic type checking.
    2. Within the resolver functions generated by gqlgen for fields accepting input arguments:
      • Retrieve input arguments provided to the resolver.
      • Implement validation logic directly within the Go resolver code. Utilize Go's standard library or validation libraries to enforce rules such as:
        • Regular expression matching for string formats (e.g., email, phone number).
        • String length constraints.
        • Numeric range checks.
        • Allowed value whitelists.
        • Custom business rule validation.
      • If validation fails within the resolver, return an error using fmt.Errorf or a custom error type. gqlgen's error handling will manage this error and return it in the GraphQL response. Ensure error messages are user-friendly and avoid exposing sensitive server-side details.
    3. Thoroughly test resolvers with both valid and invalid inputs to confirm validation logic is correctly implemented within the gqlgen resolver context.

    • Threats Mitigated:

      • Injection Attacks (Medium Severity): Reduces risk by validating input before it's processed by backend logic accessed through gqlgen resolvers. While GraphQL's structure inherently limits some injection vectors, resolvers can still be vulnerable if they directly construct queries or commands from unsanitized input.
      • Data Integrity Issues (High Severity): Ensures data processed by gqlgen resolvers conforms to expected formats and constraints, preventing invalid data from impacting application state.
      • Business Logic Errors (Medium Severity): Catches invalid input early in the gqlgen resolver layer, preventing the application from proceeding with flawed data and potentially leading to incorrect business outcomes.

    • Impact:

      • Injection Attacks: Medium reduction (gqlgen resolvers are the entry point for data processing, validation here is crucial).
      • Data Integrity Issues: High reduction (directly addresses data validity within the gqlgen application).
      • Business Logic Errors: Medium reduction (prevents input-related errors handled by gqlgen resolvers).

    • Currently Implemented: Yes, partially implemented in user registration and profile update mutations within server/graph/resolver/user.go resolvers generated by gqlgen.

    • Missing Implementation: Validation needs to be extended to input fields in resolvers for product creation, order placement, comment submission, and other mutations defined in the GraphQL schema and handled by gqlgen resolvers.

Mitigation Strategy: Query Complexity Analysis and Limits (gqlgen Middleware)

  • Description:

    1. Define a query complexity scoring algorithm relevant to your GraphQL schema and the resolvers generated by gqlgen. This algorithm should assign costs to query elements (fields, arguments, directives) based on their potential resource consumption when resolved by gqlgen.
    2. Implement this complexity analysis as gqlgen middleware. Middleware in gqlgen intercepts and processes GraphQL requests before they reach resolvers.
    3. Within the gqlgen middleware:
      • Parse the incoming GraphQL query.
      • Traverse the query tree, calculating the complexity score using your defined algorithm.
      • Compare the calculated score against a pre-defined maximum complexity threshold.
      • If the threshold is exceeded, reject the query within the middleware and return a GraphQL error response to the client via gqlgen's error handling.
    4. Fine-tune the complexity algorithm and threshold based on performance testing of your gqlgen application and monitoring of resource usage during query execution.

    • Threats Mitigated:

      • Denial of Service (DoS) Attacks (High Severity): Prevents attackers from exploiting GraphQL's flexibility to send excessively complex queries that overload the server processing gqlgen requests.
      • Resource Exhaustion (High Severity): Protects server resources (CPU, memory, database connections) from being consumed by overly expensive queries handled by gqlgen resolvers, ensuring application stability.

    • Impact:

      • DoS Attacks: High reduction (gqlgen middleware effectively blocks overly complex queries).
      • Resource Exhaustion: High reduction (gqlgen middleware limits resource consumption by complex queries).

    • Currently Implemented: Yes, implemented as gqlgen middleware in server/middleware/complexity.go. This middleware is integrated into the gqlgen handler configuration.

    • Missing Implementation: The complexity algorithm in the gqlgen middleware is currently basic. It needs refinement to incorporate field-specific costs, especially for resolvers generated by gqlgen that perform computationally intensive operations or external API calls. The complexity threshold should be dynamically adjustable based on server load, potentially configurable within the gqlgen middleware.

Mitigation Strategy: Query Depth Limiting (gqlgen Middleware)

  • Description:

    1. Determine a maximum acceptable query depth for your application's GraphQL schema, considering typical use cases and performance implications for gqlgen resolvers.
    2. Implement query depth limiting as gqlgen middleware.
    3. Within the gqlgen middleware:
      • Parse the incoming GraphQL query.
      • Traverse the query tree to determine the maximum nesting depth of selections.
      • Compare the determined depth against the pre-defined maximum allowed depth.
      • If the query depth exceeds the limit, reject the query in the middleware and return a GraphQL error response via gqlgen's error handling.

    • Threats Mitigated:

      • Denial of Service (DoS) Attacks (Medium Severity): Limits the impact of deeply nested queries processed by gqlgen resolvers that can consume server resources, although less granular than complexity analysis.
      • Resource Exhaustion (Medium Severity): Reduces the risk of resource exhaustion from excessively deep queries handled by gqlgen resolvers.

    • Impact:

      • DoS Attacks: Medium reduction (gqlgen middleware provides a basic defense against depth-based DoS).
      • Resource Exhaustion: Medium reduction (gqlgen middleware helps limit resource usage from deep queries).

    • Currently Implemented: Yes, implemented within the same gqlgen middleware as complexity analysis in server/middleware/complexity.go. This middleware is part of the gqlgen handler.

    • Missing Implementation: While implemented in gqlgen middleware, the depth limit is static. Consider making it configurable per user role or API endpoint for more granular control within the gqlgen middleware configuration.

Mitigation Strategy: Data Loaders for N+1 Problem (gqlgen Resolvers)

  • Description:

    1. Identify resolvers generated by gqlgen that are prone to the N+1 problem. These are typically resolvers that fetch related data for lists of items.
    2. For each such gqlgen resolver:
      • Implement a data loader using a library like vektah/dataloaden.
      • Within the gqlgen resolver, instead of directly fetching related data for each item individually, use the data loader's Load function to enqueue keys (e.g., IDs) of the related data.
      • The data loader, configured outside the resolver but accessible within the gqlgen context, will batch these keys and execute a single efficient database query to fetch all related data in bulk.
      • The data loader will then automatically distribute the fetched data to the corresponding gqlgen resolvers based on the keys, ensuring efficient data retrieval.
    3. Ensure data loaders are correctly initialized and consistently used within relevant gqlgen resolvers to prevent N+1 queries.

    • Threats Mitigated:

      • Performance Degradation (High Severity): Prevents the N+1 problem, which can severely slow down query execution in gqlgen applications and degrade performance, especially under load.
      • Database Overload (High Severity): Reduces the number of database queries originating from gqlgen resolvers, preventing database overload and improving database scalability.
      • Indirect DoS (Medium Severity): By improving performance and reducing database load caused by gqlgen resolvers, data loaders indirectly contribute to DoS prevention.

    • Impact:

      • Performance Degradation: High reduction (data loaders significantly improve performance of gqlgen resolvers).
      • Database Overload: High reduction (data loaders drastically reduce database load from gqlgen queries).
      • Indirect DoS: Medium reduction (improves overall resilience of the gqlgen application).

    • Currently Implemented: Yes, data loaders are implemented for fetching user profiles related to posts and comments in server/dataloaders/user_loader.go and used in relevant resolvers generated by gqlgen in server/graph/resolver/post.go and server/graph/resolver/comment.go.

    • Missing Implementation: Data loaders need to be implemented for other relationships in the schema resolved by gqlgen, such as fetching orders related to users, products related to categories, etc. Expand data loader coverage to all areas prone to N+1 issues within the gqlgen application.

Mitigation Strategy: Custom Error Handling and Sanitization (gqlgen Error Presenter)

  • Description:

    1. Implement a custom error handler for your gqlgen application by configuring a custom ErrorPresenter in your gqlgen.Config. This allows you to intercept and modify errors before they are returned in the GraphQL response.
    2. Within the custom gqlgen ErrorPresenter function:
      • Inspect the error object passed to the presenter.
      • Log detailed error information server-side using a logging mechanism. Include stack traces and internal error details for debugging and monitoring purposes.
      • For client-facing error responses generated by gqlgen:
        • Sanitize error messages to remove any sensitive internal information that might be present in the original error.
        • Return generic, user-friendly error messages to the client via gqlgen's error response format. Examples: "An unexpected error occurred," "Invalid input," "Unauthorized access."
        • Consider adding error classification using GraphQL error extensions in the gqlgen response. This allows clients to programmatically handle different error types (e.g., "ValidationError," "AuthenticationError") without relying on potentially sensitive error message strings.

    • Threats Mitigated:

      • Information Disclosure (High Severity): Prevents leaking sensitive server-side information in GraphQL error responses generated by gqlgen, which could be exploited by attackers.
      • Security Misconfiguration (Medium Severity): Overrides default gqlgen error handling which might expose verbose error details, mitigating risks associated with default configurations.

    • Impact:

      • Information Disclosure: High reduction (gqlgen's ErrorPresenter directly controls error response content).
      • Security Misconfiguration: Medium reduction (custom ErrorPresenter overrides potentially insecure defaults in gqlgen).

    • Currently Implemented: Yes, a custom error presenter is implemented in server/graph/error_handler.go and configured in gqlgen.yml. It logs detailed errors and returns sanitized, generic error messages in gqlgen responses.

    • Missing Implementation: Enhance the custom gqlgen ErrorPresenter to categorize errors and return more specific, yet still safe, error types to the client via error extensions in the GraphQL response. This would improve client-side error handling without compromising security by revealing sensitive error details in message strings handled by gqlgen.