mitigations.md

Mitigation Strategies Analysis for serde-rs/serde

Mitigation Strategy: serde Configuration - deny_unknown_fields

Description:

1. Identify Structs: Identify all Rust structs that are used with serde for deserialization (i.e., those that derive Deserialize).
2. Apply Attribute: Add the #[serde(deny_unknown_fields)] attribute to each of these structs. This is a single-line change per struct.

   #[derive(Deserialize)]
   #[serde(deny_unknown_fields)]
   struct MyData {
       // ... fields ...
   }

3. Test: After applying the attribute, thoroughly test your application to ensure that it still functions correctly with valid input. Also, test with input that includes unknown fields to verify that the application now correctly rejects such input.

Threats Mitigated:

• Data Tampering/Injection - High Severity: Prevents attackers from injecting extra fields that might be misinterpreted by your application logic, even if serde handles them safely. This is a key defense against a common attack pattern.
• Logic Errors - Medium Severity: Helps prevent unexpected behavior caused by the presence of unexpected data.

Impact:

• Data Tampering: Very effectively prevents data tampering via unknown fields.
• Logic Errors: Reduces the risk of logic errors caused by unexpected fields.

Currently Implemented: Partially implemented. Applied to most structs used for API data deserialization (src/api/models.rs).

Missing Implementation:

• Not consistently applied to structs used for internal data representation (src/internal/models.rs).
• Not applied to structs used for configuration file parsing (src/config.rs).

Mitigation Strategy: Avoid deserialize_any (When Possible)

Description:

1. Review Usage: Examine your code for uses of serde's deserialize_any method (often encountered when working with serde_json::Value).
2. Refactor to Strong Types: If possible, refactor your code to use strongly-typed deserialization. This means defining specific structs or enums that match the expected data structure and using deserialize (or the derived Deserialize implementation) instead of deserialize_any.
3. Justify and Validate (If Unavoidable): If deserialize_any is absolutely necessary, clearly document why it's needed. Immediately after deserialization, implement extremely rigorous type checking and validation to ensure the resulting data is handled safely. Consider using a match statement to exhaustively handle all possible types that could be produced.

Threats Mitigated:

• Type Confusion - Medium Severity: Reduces the risk of type confusion vulnerabilities, where the application misinterprets the type of deserialized data.
• Logic Errors - Medium Severity: Helps prevent unexpected behavior caused by unanticipated data types.
• Data Tampering - Medium Severity: Makes it harder for attackers to inject data that will be misinterpreted due to incorrect type assumptions.

Impact:

• Type Confusion: Significantly reduces the risk of type confusion.
• Logic Errors: Reduces the risk of logic errors related to data types.
• Data Tampering: Adds a layer of defense against data tampering.

Currently Implemented: Mostly avoided. Strongly-typed deserialization is preferred throughout the project.

Missing Implementation:

• One instance of deserialize_any is used in a legacy module (src/legacy/parser.rs) for handling a dynamic data format. This needs to be reviewed and potentially refactored.

Mitigation Strategy: Custom Deserializers (with Caution)

Description:

1. Minimize Usage: Avoid writing custom Deserialize implementations unless absolutely necessary. Prefer using serde's derive macro whenever possible.
2. Thorough Input Validation: If you must write a custom deserializer, perform extremely thorough input validation within the deserializer itself. Check for:
   • Expected data types.
   • Valid ranges for numerical values.
   • Expected string lengths and patterns.
   • Presence or absence of required fields.
   • Any other constraints specific to the data format.
3. Handle Errors Gracefully: Return appropriate serde::de::Error values for any invalid input. Do not panic or crash.
4. Fuzz Testing: Mandatory: Fuzz test your custom deserializer extensively using a tool like cargo fuzz. This is crucial to uncover potential vulnerabilities.
5. Code Review: Have another developer carefully review your custom deserializer code for potential security issues.

Threats Mitigated:

• Unknown Vulnerabilities - Variable Severity (Low to Critical): Custom deserializers are a potential source of vulnerabilities if not implemented carefully. Thorough validation and fuzzing are essential.
• Denial of Service (DoS) - High Severity: Prevents malicious input from causing crashes or excessive resource consumption within the deserializer.
• Data Tampering/Injection - High Severity: Allows for fine-grained control over input validation, preventing various forms of data tampering.
• Logic Errors - Medium Severity: Reduces the risk of logic errors caused by unexpected input.

Impact:

• Unknown Vulnerabilities: The impact depends heavily on the quality of the implementation. Good validation and fuzzing are critical.
• DoS: Can significantly reduce the risk of DoS attacks.
• Data Tampering: Provides strong protection against data tampering if implemented correctly.
• Logic Errors: Reduces the risk of logic errors.

Currently Implemented: A few custom deserializers exist for handling specific data formats (src/data/custom_format.rs).

Missing Implementation:

• The existing custom deserializers have not been fuzz tested. This is a critical gap.
• Code review for security vulnerabilities has not been consistently performed for these deserializers.

Mitigation Strategy: Resource Limits - Recursion Depth (via Custom Deserializer)

Description:

1. Identify Recursive Structures: Identify data structures that can be recursively nested (e.g., trees, linked lists represented in JSON).
2. Determine Maximum Depth: Determine a reasonable maximum depth for these structures based on legitimate use cases.
3. Implement Depth Limiting (Custom Deserializer): Create a custom Deserializer implementation that tracks the current recursion depth and returns an error if the maximum depth is exceeded. This is the most precise and serde-integrated approach.
   • Create a struct that wraps the inner Deserializer and maintains max_depth and current_depth fields.
   • Implement the Deserializer trait for your wrapper struct.
   • In the visit_map and visit_seq methods (and any other methods that handle nested structures), increment current_depth before delegating to the inner deserializer.
   • Decrement current_depth after the inner deserializer returns.
   • If current_depth exceeds max_depth, return a serde::de::Error.
4. Test: Thoroughly test the depth limiting mechanism with both valid and maliciously nested inputs.

Threats Mitigated:

• Denial of Service (DoS) - High Severity: Prevents stack overflow errors caused by deeply nested input, which can lead to application crashes. This is a direct mitigation using serde's extension points.

Impact:

• DoS: Significantly reduces the risk of DoS attacks caused by stack overflows.

Currently Implemented: Not implemented.

Missing Implementation:

• No recursion depth limiting is currently in place. This is a potential vulnerability for data structures that could be deeply nested. This needs a custom Deserializer implementation.