Skip to content

Latest commit

 

History

History
44 lines (39 loc) · 4.72 KB

attack-surface.md

File metadata and controls

44 lines (39 loc) · 4.72 KB

Attack Surface Analysis for diesel-rs/diesel

Attack Surface: Raw SQL Injection

  • Description: Vulnerability arising from directly embedding user-controlled input into raw SQL queries executed via Diesel, without proper sanitization or parameterization. This bypasses Diesel's built-in safety mechanisms.
  • How Diesel Contributes: Diesel provides features like sql_query, execute, and query that allow developers to execute arbitrary SQL. Misuse of these features by directly interpolating user input into SQL strings creates a direct pathway for SQL injection.
  • Example: 
    let table_name = // User input from request
let query = format!("SELECT COUNT(*) FROM {}", table_name); // Vulnerable!
diesel::sql_query(query).get_result::<i64>(conn);
    If a malicious user provides input like users; DROP TABLE users; --, the query becomes SELECT COUNT(*) FROM users; DROP TABLE users; --. This executes the intended count query and the malicious DROP TABLE command.
  • Impact: Critical - Full database compromise, unauthorized data access, data modification or deletion, account takeover, denial of service, potential for remote code execution in some database configurations.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Eliminate Raw SQL Usage: Prioritize using Diesel's Query Builder for all database interactions. It is designed to prevent SQL injection by using parameterization automatically.
    • Mandatory Parameterized Queries for Raw SQL: If raw SQL is absolutely unavoidable, strictly enforce the use of Diesel's parameter binding features (bind::<Type, _>). Never directly interpolate user input into raw SQL strings.
    • Strict Input Validation: Even with parameter binding, implement robust input validation to limit the allowed characters, length, and format of user inputs as a defense-in-depth measure.
    • Security Code Reviews: Conduct thorough security code reviews, specifically focusing on any code sections that utilize raw SQL features, to ensure proper parameterization and prevent injection vulnerabilities.

Attack Surface: Logic Errors in Query Construction

  • Description: Vulnerabilities resulting from flawed logic in Diesel query construction, leading to unintended data access, modification, or circumvention of authorization controls. While not SQL injection, these errors can have severe security implications.
  • How Diesel Contributes: Diesel's query builder provides powerful tools, but the security of the application still depends on the developer's correct implementation of query logic. Incorrect joins, missing filters, or flawed conditions can create vulnerabilities.
  • Example: 
    let item_id = // User input from request
let items = items::table
    .inner_join(users::table)
    .select((items::all_columns, users::username))
    .filter(items::id.eq(item_id)) // Missing authorization filter!
    .load::<(Item, String)>(conn);
    This query joins items and users and filters by item_id from user input. However, it lacks a filter to ensure the user is authorized to access the item. An attacker could potentially access any item by knowing its item_id, regardless of permissions.
  • Impact: High - Unauthorized data access, privilege escalation, information disclosure, potential for data modification depending on the application logic built around the flawed query.
  • Risk Severity: High
  • Mitigation Strategies:
    • Implement Authorization in Queries: Integrate authorization checks directly into Diesel queries. Filter results based on user roles, permissions, ownership, or other relevant authorization criteria.
    • Principle of Least Privilege in Data Access: Design queries to retrieve only the minimum necessary data. Avoid overly broad queries that might expose sensitive information beyond what is required for the intended operation.
    • Comprehensive Testing (Unit & Integration): Develop thorough unit and integration tests that specifically cover database interactions, including authorization scenarios and edge cases, to identify logic errors in query construction.
    • Security-Focused Code Reviews: Conduct code reviews with a strong focus on security, specifically examining query logic to ensure it correctly enforces intended data access and authorization policies. Pay close attention to filters, joins, and conditions.
    • Utilize Diesel's Type System: Leverage Diesel's strong type system and compile-time checks to catch potential errors in query construction early in the development lifecycle.