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Threat Model Analysis for diesel-rs/diesel

Threat: SQL Injection via Raw SQL Misuse

  • Threat: SQL Injection via Raw SQL Misuse

    • Description: An attacker crafts malicious input that, when incorporated into a raw SQL query string without proper parameterization within Diesel's sql_query or similar functions, alters the intended query logic. The attacker bypasses Diesel's built-in protections by directly manipulating the SQL. This is the most direct and dangerous threat when using Diesel improperly.
    • Impact:
      • Data breach: Unauthorized access to sensitive data.
      • Data modification: Unauthorized alteration or deletion of data.
      • Data exfiltration: Copying of sensitive data.
      • Database takeover: Potential for complete control over the database.
    • Diesel Component Affected: diesel::sql_query, any custom code using execute with raw SQL strings, functions that accept and execute raw SQL fragments.
    • Risk Severity: Critical
    • Mitigation Strategies:
      • Primary: Avoid raw SQL entirely if possible. Use Diesel's query builder (DSL) for all database interactions. This is the strongest defense.
      • If raw SQL is absolutely necessary: Use diesel::sql_query exclusively with parameterized queries. Utilize Diesel's bind function (or equivalent) to safely pass user-provided data as parameters. Never concatenate user input directly into the SQL string.
      • Strict input validation and sanitization before any data is considered for a query (even with parameterization) – defense-in-depth.
      • Code reviews focusing on any use of raw SQL.

Threat: Second-Order SQL Injection (with Diesel involvement)

  • Threat: Second-Order SQL Injection (with Diesel involvement)

    • Description: Malicious data, previously stored in the database (possibly through a non-Diesel vector), is later retrieved and used unsafely within a Diesel query. While the initial injection might not be Diesel-related, the exploitation occurs through Diesel, typically via raw SQL or improper string concatenation within the query builder. The key is that Diesel is the vector of the second-order attack.
    • Impact: Same as standard SQL Injection (data breach, modification, exfiltration, database takeover).
    • Diesel Component Affected: diesel::sql_query, any function using raw SQL, potentially parts of the query builder if string concatenation with retrieved data is used.
    • Risk Severity: Critical
    • Mitigation Strategies:
      • Input Sanitization (at point of entry): Sanitize all user-provided data before storing it, regardless of the storage method.
      • Parameterized Queries (always): Even when using data retrieved from the database, always use parameterized queries if that data is incorporated into any subsequent Diesel query (raw or DSL).
      • Avoid String Concatenation: Do not build Diesel queries (even within the DSL) by concatenating strings that contain data retrieved from the database.
      • Defense in Depth: Combine input sanitization, parameterized queries, and careful handling of retrieved data.

Threat: Information Disclosure via Debugging Features

  • Threat: Information Disclosure via Debugging Features

    • Description: An attacker gains access to sensitive information (database schema, user data) because debug_query is accidentally left enabled in a production environment, or because overly verbose logging (configured to interact with Diesel) includes raw SQL queries or database responses. This is a direct consequence of misconfiguring Diesel or its interaction with logging.
    • Impact:
      • Exposure of database schema.
      • Leakage of sensitive data within queries/results.
      • Facilitates further attacks.
    • Diesel Component Affected: diesel::debug_query, logging configurations that interact with Diesel's query execution.
    • Risk Severity: High
    • Mitigation Strategies:
      • Disable debug_query in Production: Use conditional compilation (#[cfg(debug_assertions)]) to guarantee debug_query is only enabled during development.
      • Secure Logging: Implement a logging strategy that never logs raw SQL queries containing user data in production. Sanitize/redact sensitive information. Use structured logging.
      • Error Handling: Ensure error messages to users do not reveal internal database details. Catch and handle Diesel errors, providing generic messages to the user.

Threat: Unintended Data Exposure via Implicit Joins

  • Threat: Unintended Data Exposure via Implicit Joins

    • Description: An attacker, through crafted input or exploiting application logic, triggers queries that use implicit joins or relationships defined in Diesel models. These joins might inadvertently expose data from related tables that the user should not have access to, violating the application's authorization rules. This is a direct threat related to how Diesel handles relationships.
    • Impact:
      • Data breach: Unauthorized access to sensitive data from related tables.
      • Violation of data privacy.
    • Diesel Component Affected: Diesel's query builder, specifically when using associations and implicit joins (e.g., belonging_to, has_many, .load with associated models).
    • Risk Severity: High
    • Mitigation Strategies:
      • Explicit Selects: Always use .select() to explicitly specify the columns to be retrieved from each table in the query. Avoid default selections or implicit loading.
      • Careful Association Definition: Review and carefully define relationships between Diesel models. Ensure associations are correctly configured.
      • Authorization Checks: Implement authorization checks before executing queries with joins or associations. Verify user permissions.
      • Views: Consider using database views to restrict the data exposed to the application.
      • Thorough Testing: Conduct extensive testing, including security-focused tests, to ensure queries with joins only return intended data.