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Mitigation Strategies Analysis for serde-rs/serde

Mitigation Strategy: Validate Deserialized Data

Mitigation Strategy: Validate Deserialized Data

  • Description:

    1. Identify Data Constraints: For each field in your serde deserialized structs, define clear constraints based on your application's business logic. This includes data type, allowed ranges, formats, and relationships between fields.
    2. Implement Validation Functions: Create dedicated functions or use validation libraries (like validator-rs) to check these constraints after deserialization.
    3. Apply Validation: Call these validation functions immediately after deserializing data from any external source (e.g., API requests, file uploads, configuration files).
    4. Handle Validation Errors: If validation fails, return an error to the user or log the issue appropriately. Do not proceed with processing invalid data.

  • List of Threats Mitigated:

    • Data Injection (High Severity): Prevents malicious data from being processed by the application, potentially leading to code execution, data breaches, or unauthorized actions.
    • Logic Errors (Medium Severity): Reduces the risk of application logic failing due to unexpected or invalid data formats, leading to incorrect behavior or crashes.
    • Data Corruption (Medium Severity): Prevents invalid data from being persisted or propagated within the system, maintaining data integrity.

  • Impact:

    • Data Injection: High risk reduction. Validation acts as a critical barrier against malicious payloads.
    • Logic Errors: Medium risk reduction. Significantly reduces errors caused by malformed input.
    • Data Corruption: Medium risk reduction. Helps maintain data consistency and reliability.

  • Currently Implemented: Yes, partially implemented in API request handlers. Input validation is performed for key API endpoints using custom validation functions.

  • Missing Implementation: Validation is not consistently applied across all data deserialization points, particularly in background task processing and configuration file parsing. Validation rules need to be more comprehensive and formalized.

Mitigation Strategy: Define Strict Data Schemas

Mitigation Strategy: Define Strict Data Schemas

  • Description:

    1. Choose Specific Types: Select the most restrictive data types possible for your struct fields. Use u32 instead of i64 for non-negative integers, String with length limits instead of unbounded strings, enums for limited value sets.
    2. Utilize Option<T> and Result<T, E>: Explicitly represent optional data with Option<T> and handle potential errors during deserialization or processing with Result<T, E>.
    3. Leverage Enums: Use Rust enums to define a closed set of allowed values for fields, restricting the possible input space.

  • List of Threats Mitigated:

    • Data Injection (Medium Severity): Reduces the attack surface by limiting the possible input values and types, making it harder to inject unexpected data.
    • Logic Errors (Medium Severity): Improves code clarity and reduces logic errors by making data types and constraints explicit in the code.
    • Type Confusion (Low Severity): Minimizes the risk of type-related errors during deserialization and processing.

  • Impact:

    • Data Injection: Medium risk reduction. Makes exploitation slightly harder by narrowing the input space.
    • Logic Errors: Medium risk reduction. Improves code robustness and reduces type-related bugs.
    • Type Confusion: Low risk reduction. Primarily improves code maintainability and clarity.

  • Currently Implemented: Yes, generally implemented in new data structures. Existing code base is being refactored to adopt stricter types where feasible.

  • Missing Implementation: Consistent application of strict schemas across all modules. Some legacy data structures still use less specific types than necessary.

Mitigation Strategy: Utilize deny_unknown_fields Attribute

Mitigation Strategy: Utilize deny_unknown_fields Attribute

  • Description:

    1. Add Attribute: Include #[serde(deny_unknown_fields)] attribute to all structs that are deserialized from external sources, especially JSON or other formats where extra fields might be present.
    2. Test Deserialization: Ensure that deserialization fails gracefully with an error message when unknown fields are encountered in the input data.
    3. Review Error Handling: Implement proper error handling to catch deserialization errors caused by unknown fields and respond appropriately (e.g., reject the request, log the error).

  • List of Threats Mitigated:

    • Data Injection (Medium Severity): Prevents attackers from injecting extra fields that might be ignored by the application but could be processed by underlying systems or logged in a way that causes harm.
    • Parameter Pollution (Medium Severity): Mitigates parameter pollution attacks where extra parameters are added to manipulate application behavior.
    • Logic Errors (Low Severity): Helps detect discrepancies between expected data structure and actual input, potentially catching configuration errors or API changes.

  • Impact:

    • Data Injection: Medium risk reduction. Prevents silent injection of unexpected data through extra fields.
    • Parameter Pollution: Medium risk reduction. Makes parameter pollution attacks less effective.
    • Logic Errors: Low risk reduction. Primarily improves robustness and error detection.

  • Currently Implemented: Yes, implemented for all new API request structs and configuration file parsing.

  • Missing Implementation: Retroactively applying deny_unknown_fields to all existing structs in the codebase. Need to audit and update legacy data structures.

Mitigation Strategy: Review Serde Attributes and Configurations

Mitigation Strategy: Review Serde Attributes and Configurations

  • Description:

    1. Code Review: Conduct code reviews specifically focusing on serde attributes and custom serialization/deserialization logic.
    2. Attribute Audit: Systematically review all uses of serde attributes (e.g., rename, default, skip_serializing_if, with) to ensure they are used correctly and securely.
    3. Custom Function Scrutiny: Carefully examine any custom serialization/deserialization functions (with attribute) for potential vulnerabilities (e.g., buffer overflows, logic errors, insecure operations).
    4. Documentation: Document the intended behavior and security implications of complex serde configurations and custom functions.

  • List of Threats Mitigated:

    • Logic Errors (Medium Severity): Reduces the risk of misconfigured serde attributes or flawed custom functions leading to unexpected behavior or vulnerabilities.
    • Information Disclosure (Low Severity): Prevents unintentional exposure of sensitive data due to incorrect serialization configurations.
    • Data Corruption (Low Severity): Minimizes the risk of data corruption caused by incorrect serialization/deserialization logic.

  • Impact:

    • Logic Errors: Medium risk reduction. Improves code correctness and reduces configuration-related bugs.
    • Information Disclosure: Low risk reduction. Prevents minor information leaks due to serialization issues.
    • Data Corruption: Low risk reduction. Enhances data integrity by ensuring correct serialization.

  • Currently Implemented: Yes, code reviews are standard practice, including review of serde usage.

  • Missing Implementation: Specific checklist or guidelines for reviewing serde attributes and custom functions during code reviews could be formalized.