Attack Surface: Raw SQL Injection
Description: Execution of arbitrary SQL commands provided by an attacker, bypassing Diesel's type-safe query builder.
Diesel's Contribution: Diesel provides functions (sql_query, execute) that, if misused, allow raw SQL execution. This is the primary way Diesel can introduce SQL injection vulnerabilities. The core design of Diesel attempts to prevent this, but these functions offer an escape hatch that must be used with extreme caution.
Example:
// VULNERABLE CODE:
let user_input = req.params().get("id").unwrap(); // Untrusted input
let query = format!("SELECT * FROM users WHERE id = {}", user_input);
let results = diesel::sql_query(query).load::<User>(&mut connection);An attacker could provide 1; DROP TABLE users; -- as the id parameter.
Impact: Complete database compromise, data theft, data modification, data deletion, denial of service.
Risk Severity: Critical
Mitigation Strategies:
* Never use sql_query or execute with string formatting that incorporates any untrusted input. This is paramount.
* Always use Diesel's query builder for constructing queries. This ensures proper parameterization.
* If raw SQL must be used (extremely rare and discouraged), use diesel::sql_query with bound parameters:
rust // SAFER (but still strongly prefer the query builder): let user_input: i32 = req.params().get("id").unwrap().parse().unwrap(); // Validate and parse! let results = diesel::sql_query("SELECT * FROM users WHERE id = ?") .bind::<diesel::sql_types::Integer, _>(user_input) .load::<User>(&mut connection);
* Input Validation: Always validate and sanitize all user input, even when using the query builder, for defense-in-depth.
* Principle of Least Privilege: Ensure that database user has only required permissions.
Attack Surface: Dynamic Table/Column Names (Indirect Injection)
Description: Allowing user input to influence table or column names used in queries, leading to potential information disclosure or other database manipulation. Diesel's Contribution: Diesel does not automatically sanitize table or column identifiers. The query builder primarily focuses on parameterizing values, not the structural elements of the query (table and column names). This is a crucial distinction. Example:
// VULNERABLE CODE:
let user_supplied_table = req.params().get("table").unwrap(); // Untrusted input
let query = format!("SELECT * FROM {}", user_supplied_table); // Directly using the input
let results = diesel::sql_query(query).load::<SomeType>(&mut connection);An attacker could provide a different table name (e.g., admin_credentials) to access data they shouldn't.
Impact: Information disclosure (accessing unintended tables/columns), potential for more severe attacks depending on the database and application logic.
Risk Severity: High
Mitigation Strategies:
* Avoid using user input to directly construct table or column names. This is the best approach.
* Whitelist Allowed Identifiers: If dynamic table/column selection is absolutely required, maintain a strict whitelist of allowed values and validate user input against this whitelist before passing it to Diesel.
* Use an Enum or Similar: Represent allowed table/column choices with an enum or a similar type-safe construct, rather than using raw strings. This prevents arbitrary input and leverages Rust's type system.
* Re-evaluate Design: Seriously consider alternative design patterns that don't require dynamic table/column selection. Often, a well-designed schema can avoid this need entirely, significantly reducing risk.
Attack Surface: Migration Script Injection
Description: Execution of malicious SQL code embedded within database migration scripts.
Diesel's Contribution: Diesel's migration system executes SQL scripts. If these scripts are sourced from untrusted locations or are not properly reviewed, they can contain malicious code that Diesel will execute. Diesel provides the mechanism for running migrations; the vulnerability lies in the content of those migrations.
Example: An attacker could submit a pull request with a migration file containing DROP TABLE users; or insert malicious SQL to create a backdoor user.
Impact: Database compromise, data loss, data modification, denial of service, potential for complete system compromise.
Risk Severity: High
Mitigation Strategies:
* Thoroughly Review All Migrations: Carefully review and vet all database migration scripts before applying them, especially those from external contributors or generated automatically. Manual code review is essential.
* Automated Code Analysis (for SQL): Consider using automated code analysis tools specifically designed to scan SQL scripts for potentially malicious patterns (e.g., dynamic SQL, suspicious commands).
* Controlled Migration Deployment: Implement a controlled and auditable process for deploying migrations to production, with appropriate approvals, testing, and rollback capabilities.
* Never Execute Migrations from Untrusted Sources: Do not run migration scripts downloaded from the internet or provided by untrusted users without extensive scrutiny.