mitigations.md

Mitigation Strategies Analysis for diesel-rs/diesel

Mitigation Strategy: Prepared Statements and Query Builder Preference

• Description:

  1. Prioritize the Query Builder: Developers should primarily use Diesel's query builder methods (e.g., .filter(), .select(), .insert(), .update(), .delete()) for constructing database queries. These methods automatically generate parameterized SQL queries (prepared statements).
  2. Minimize sql_query: The sql_query function, which allows raw SQL execution, should be used only when absolutely necessary and with extreme caution.
  3. Safe Parameter Binding (for sql_query): If sql_query is unavoidable:
     • Never interpolate user-provided data directly into the SQL string.
     • Use Diesel's bind method to pass parameters separately. Specify the correct diesel::sql_types for each parameter.
     • Example (Correct):

       let user_id = get_user_input(); // Assume this gets user input
       let results = sql_query("SELECT * FROM users WHERE id = ?")
           .bind::<diesel::sql_types::Integer, _>(user_id)
           .load::<User>(&mut conn)?;

     • Example (Incorrect):

       let user_id = get_user_input();
       let results = sql_query(format!("SELECT * FROM users WHERE id = {}", user_id))
           .load::<User>(&mut conn)?;

• Threats Mitigated:

  • SQL Injection (Severity: Critical): The primary threat. Attackers can inject malicious SQL code to bypass authentication, steal data, modify data, or even execute arbitrary commands on the database server.

• Impact:

  • SQL Injection: Risk reduced from Critical to Very Low (assuming proper implementation).

• Currently Implemented:

  • Example: Query builder is used for 95% of queries. sql_query is used in src/legacy_reports.rs.

• Missing Implementation:

  • Example: The src/legacy_reports.rs code needs review and refactoring to use the query builder or safer parameter binding.

Mitigation Strategy: Schema Validation and Type Safety (Diesel-Specific Aspects)

• Description:

  1. Diesel Migrations: All database schema changes must be managed through Diesel's migration system. Developers should use diesel migration generate <migration_name> to create new migrations and diesel migration run to apply them.
  2. schema.rs Generation: After each migration, developers must run diesel print-schema > src/schema.rs to update the schema.rs file. This file should be committed to version control. The build process should fail if schema.rs is outdated.
  3. Enum Mapping (if applicable): If the database uses enums, ensure they are correctly mapped to Rust enums using Diesel's features (e.g., #[derive(DbEnum)]). Document the mapping clearly.

• Threats Mitigated:

  • Data Corruption (Severity: High): Mismatches between the application's expected schema and the actual schema can lead to incorrect data being written.
  • Application Errors/Crashes (Severity: Medium): Schema mismatches can cause runtime errors.

• Impact:

  • Data Corruption: Risk significantly reduced.
  • Application Errors/Crashes: Risk reduced.

• Currently Implemented:

  • Example: Diesel migrations are used consistently. schema.rs is generated and committed.

• Missing Implementation:

  • Example: Enum mapping is not explicitly documented.

Mitigation Strategy: Over-Fetching and Under-Fetching Prevention (Diesel-Specific)

• Description:

  1. Selective SELECT Statements: Use the .select() method in Diesel's query builder to explicitly specify the columns to retrieve. Avoid SELECT * unless all columns are genuinely required.
  2. Targeted Structs: Define Rust structs that represent only the data needed for a specific operation. Use these structs with .select() to avoid fetching unnecessary data.
  3. Association Management: Carefully manage database associations (e.g., belongs_to, has_many). Use eager loading (e.g., .load(), .get_results()) or lazy loading (e.g., .load_iter()) appropriately.
  4. Pagination: For queries that might return large result sets, implement pagination using Diesel's .limit() and .offset() methods.

• Threats Mitigated:

  • Data Exposure (Severity: Medium): Over-fetching can inadvertently expose sensitive data.
  • Performance Degradation (Severity: Medium): Fetching unnecessary data increases database load.
  • Application Errors (Severity: Low): Under-fetching can lead to errors.

• Impact:

  • Data Exposure: Risk reduced.
  • Performance Degradation: Risk reduced.
  • Application Errors: Risk reduced.

• Currently Implemented:

  • Example: .select() is used in some queries. Pagination is implemented for some list views.

• Missing Implementation:

  • Example: SELECT * is still used in many queries. Pagination is missing for several large data sets.

Mitigation Strategy: Transaction Management (Diesel-Specific)

• Description:

  1. Explicit Transactions: Use connection.transaction(|| { ... }) or connection.build_transaction().run(|| { ... }) to wrap database operations that must be executed atomically.
  2. Isolation Levels: When using build_transaction(), explicitly set the desired isolation level using methods like .read_committed(), .repeatable_read(), or .serializable(). Understand the implications of each level and document the choice.
  3. Error Handling within Transactions: Diesel's transaction method automatically handles rollbacks on error, ensure that custom error types implement the necessary traits for proper rollback.

• Threats Mitigated:

  • Data Inconsistency (Severity: High): Incorrect transaction handling can lead to partial updates.
  • Race Conditions (Severity: High): Concurrent transactions without proper isolation can lead to race conditions.

• Impact:

  • Data Inconsistency: Risk significantly reduced.
  • Race Conditions: Risk reduced.

• Currently Implemented:

  • Example: connection.transaction() is used for some multi-step operations.

• Missing Implementation:

  • Example: Isolation levels are not explicitly considered or documented.