Sql Injection in Actix with Dynamodb

SQL Injection is commonly associated with relational databases, but injection-style flaws can also manifest when user-controlled input is passed into query-building logic for NoSQL databases such as Amazon DynamoDB. In an Actix web service, if user input is directly interpolated into DynamoDB expression attribute values or used to construct partiql-like conditional expressions without validation, an attacker can manipulate query semantics to bypass authentication, enumerate records, or extract unintended data.

DynamoDB itself does not use SQL; however, many developers treat its expression syntax like a query language and mistakenly believe that NoSQL operations are immune to injection. In the Actix context, this risk arises when route parameters, query strings, or JSON payload fields are passed into DynamoDB KeyConditionExpression or FilterExpression without proper parameterization. For example, concatenating a table name or key schema fragment from user input can lead to schema probing or data exposure across partitions.

An Actix handler that builds expressions using string formatting is vulnerable even though DynamoDB API calls are made through the AWS SDK. Consider a login endpoint that constructs a KeyConditionExpression using string concatenation: an attacker can provide a malicious value that changes the logical structure of the expression, potentially matching multiple items or bypassing the intended partition key constraint. Since DynamoDB responses differ from SQL errors, the application may leak information through timing differences or response content, aiding enumeration.

When combined with middleBrick’s unauthenticated scan, such endpoints are tested for input validation issues and improper authorization across the API surface. The tool checks whether inputs are sanitized before being used in DynamoDB expressions and maps findings to the OWASP API Top 10 and CWE categories relevant to injection and authorization flaws. This is particularly important for APIs exposed without authentication, where black-box probing can reveal injection-like behaviors without credentials.

Leveraging the LLM/AI Security checks available in middleBrick, the scanner also probes for prompt injection and output risks if the API interacts with language models, ensuring that no indirect channel allows injected content to influence AI behavior or leak through model responses. While DynamoDB does not execute code, improper input handling can still lead to sensitive data exposure or privilege escalation when combined with other misconfigurations.

To secure an Actix service that interacts with DynamoDB, always treat user input as untrusted and avoid string interpolation for building DynamoDB expressions. Use the AWS SDK’s built-in expression builders and parameter binding features to ensure values are properly escaped and structure is validated before transmission.

Below is a safe pattern for querying a DynamoDB table in Actix using the official AWS SDK for Rust. The example uses ExpressionAttributeNames and ExpressionAttributeValues to separate structure from data, preventing injection-like manipulation of key conditions.

use aws_sdk_dynamodb::types::AttributeValue;
use aws_sdk_dynamodb::Client;
use actix_web::{web, HttpResponse};

async fn get_user(
    client: web::Data,
    path: web::Path,
) -> HttpResponse {
    let user_id = path.into_inner();
    // Validate format before using (e.g., UUID or integer pattern)
    if !user_id.chars().all(|c| c.is_alphanumeric() || c == '-' || c == '_') {
        return HttpResponse::BadRequest().body("Invalid user_id");
    }

    let resp = client
        .query()
        .table_name("Users")
        .key_condition_expression("pk = :uid")
        .expression_attribute_names({
            let mut map = std::collections::HashMap::new();
            map.insert("#usr", "user");
            map
        })
        .expression_attribute_values({
            let mut map = std::collections::HashMap::new();
            map.insert(":uid".to_string(), AttributeValue::S(user_id));
            map
        })
        .send()
        .await;

    match resp {
        Ok(output) => {
            // Process items safely
            HttpResponse::Ok().json(output.items().unwrap_or(&vec![]))
        }
        Err(e) => {
            // Avoid exposing raw errors that may aid attackers
            HttpResponse::InternalServerError().body("Query failed")
        }
    }
}

Additional remediation practices include:

• Validate and sanitize all inputs against strict allowlists before using them in KeyConditionExpression, FilterExpression, or table names.
• Use middleware in Actix to normalize and log suspicious patterns that resemble injection attempts.
• Apply least-privilege IAM roles to the DynamoDB credentials used by the Actix service to limit the impact of any successful exploit.
• Monitor response sizes and error patterns to detect enumeration attempts that may not raise application errors but could expose data.

For continuous assurance, integrate the CLI (middlebrick scan <url>) or GitHub Action to scan your API endpoints regularly. The Pro plan enables continuous monitoring so that any regression in input handling or authorization is flagged early, and findings align with compliance frameworks such as OWASP API Top 10 and SOC2.