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attack-surface.md

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Attack Surface Analysis for dapperlib/dapper

Attack Surface: SQL Injection Vulnerabilities

  • Description: Attackers inject malicious SQL code through application inputs. When these inputs are incorporated into SQL queries executed by Dapper without proper parameterization, the malicious code is executed against the database.
  • Dapper Contribution: Dapper's core functionality is executing raw SQL queries. It provides parameterization as a feature, but does not enforce it. If developers bypass or misuse parameterization when using Dapper's Query, Execute, or similar methods, they directly create a SQL injection vulnerability. Dapper's simplicity and direct SQL access make it easy to introduce this vulnerability if best practices are not followed.
  • Example:
    • Vulnerable Code (String Interpolation): 
      string productId = Request.QueryString["productid"];
string sql = $"SELECT ProductName FROM Products WHERE ProductID = {productId}"; // String interpolation - vulnerable!
var productName = connection.QueryFirstOrDefault<string>(sql);
    • Attack: An attacker could provide a productid like 1; DROP TABLE Users; --. The resulting SQL becomes (simplified example): 
      SELECT ProductName FROM Products WHERE ProductID = 1; DROP TABLE Users; --
      This could lead to database schema modification or data loss, in addition to potential data breaches.
  • Impact:
    • Data Breach: Unauthorized access to sensitive data across multiple tables.
    • Data Modification/Deletion: Altering or deleting critical data, including entire tables.
    • Account Takeover: Potential for escalating privileges or compromising application accounts if database access is misused.
    • Remote Code Execution (in some database configurations): Possibility of executing arbitrary commands on the database server depending on database permissions and features.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Mandatory Parameterized Queries: Always use parameterized queries with Dapper. Utilize anonymous objects, DynamicParameters, or named parameters for all user-supplied input within SQL queries. Treat string concatenation or interpolation for SQL construction as a critical security flaw.
      • Example (Parameterized Query): 
        string productId = Request.QueryString["productid"];
string sql = "SELECT ProductName FROM Products WHERE ProductID = @ProductId";
var productName = connection.QueryFirstOrDefault<string>(sql, new { ProductId = productId });
    • Secure Code Reviews: Implement mandatory code reviews focusing specifically on Dapper usage and SQL query construction to identify and eliminate potential SQL injection vulnerabilities.
    • Static Analysis Security Testing (SAST): Employ SAST tools that can detect potential SQL injection vulnerabilities in code, especially around Dapper usage patterns.

Attack Surface: Information Disclosure through Verbose Error Messages (Production Exposure)

  • Description: In production environments, exposing detailed database error messages to end-users or publicly accessible logs can leak sensitive information about the database schema, query structure, or internal application logic. This information can be leveraged by attackers to plan more targeted attacks.
  • Dapper Contribution: Dapper directly interacts with the database and can propagate database exceptions. If global exception handling is not properly configured, or if development/debug settings are mistakenly active in production, verbose database error messages generated during Dapper operations can be exposed.
  • Example:
    • Scenario: A malformed SQL query (due to a bug or attempted manipulation) is executed via Dapper in a production environment.
    • Exposed Error Message (Production): Instead of a generic error page, the application displays a raw exception stack trace or a detailed database error message revealing table names, column names, parts of the query, and potentially database server version information. This is directly caused by insufficient error handling around Dapper operations in production.
  • Impact:
    • Database Schema Disclosure: Revealing table and column names, relationships, and data types.
    • Query Structure Leakage: Exposing parts of the SQL queries used by the application, aiding in understanding application logic and potential injection points.
    • Server Information Leakage: Potentially revealing database server version and internal paths in error messages.
    • Increased Attack Surface: Providing attackers with valuable reconnaissance information to refine and target attacks, especially SQL injection attempts.
  • Risk Severity: High (in Production environments)
  • Mitigation Strategies:
    • Production-Specific Error Handling: Implement robust, production-specific global exception handling that catches all exceptions from Dapper operations and other parts of the application. Log detailed error information securely for debugging (in secure logs), but present generic, user-friendly error messages to end-users in production.
    • Disable Verbose Database Error Reporting (Production): Configure the database server in production to minimize the verbosity of error messages returned to clients. Suppress detailed error messages and stack traces from being sent to application clients.
    • Secure Logging Practices: Ensure error logs are stored securely, access is strictly controlled, and logs are regularly reviewed for suspicious activity. Avoid logging sensitive data directly in error messages if possible; log error codes or identifiers for correlation with detailed logs stored securely.
    • Regular Penetration Testing: Conduct penetration testing, including error handling scenarios, in production-like environments to identify and remediate information disclosure vulnerabilities related to error messages.