Injection Flaws in Axum with Dynamodb

Injection flaws in an Axum service that uses DynamoDB typically arise when untrusted input is used to construct low-level DynamoDB expressions or raw query parameters. Axum is a Rust web framework that encourages strong typing, but developers can still build endpoints that directly interpolate user input into DynamoDB key conditions, filter expressions, or table names. When input is not validated or parameterized, attackers can manipulate these values to produce unintended query behavior, leading to data exposure or unauthorized access.

For example, a developer might construct a KeyConditionExpression by concatenating a user-supplied partition key value without proper validation. If the input includes special characters or crafted payloads, it can change the logical structure of the request or bypass intended filters. DynamoDB’s conditional expressions and attribute value placeholders are designed to prevent injection, but misuse of these features—such as building expression strings dynamically—can reintroduce risk.

Another vector involves table or index names being derived from user-controlled data. An attacker could attempt to reference unexpected tables, potentially accessing data not intended for the caller. Inadequate input validation in Axum route parameters or query deserialization can allow such manipulation to reach the DynamoDB client layer.

These issues map to the OWASP API Top 10 category Broken Object Level Authorization (BOLA) and Injection, and they can be identified during unauthenticated scans when endpoints expose query parameters that directly influence DynamoDB request construction. middleBrick’s checks for BOLA/IDOR and Input Validation are particularly effective at surfacing these patterns, providing severity-ranked findings and remediation guidance.

To prevent injection flaws when using DynamoDB with Axum, always use parameterized expressions and avoid string concatenation for critical query components. Leverage the AWS SDK’s built-in expression builders and placeholder syntax to ensure user input is treated strictly as data, not executable expression logic.

Below is a safe pattern for querying a DynamoDB table using the official AWS SDK for Rust. It uses expression attribute values and condition placeholders to prevent injection:

use aws_sdk_dynamodb::types::AttributeValue;
use aws_sdk_dynamodb::Client;

async fn get_item_by_id(client: &Client, table_name: &str, user_id: &str) -> Result<(), aws_sdk_dynamodb::Error> {
    let response = client
        .get_item()
        .table_name(table_name)
        .key("id", AttributeValue::S(user_id.to_string()))
        .send()
        .await?;
    // handle response
    Ok(())
}

For queries with key conditions, use the expression attribute values pattern instead of interpolating values into the key condition string:

use aws_sdk_dynamodb::types::AttributeValue;
use aws_sdk_dynamodb::Client;

async fn query_items(client: &Client, table_name: &str, partition_key: &str, min_value: i64) -> Result<(), aws_sdk_dynamodb::Error> {
    let response = client
        .query()
        .table_name(table_name)
        .key_condition_expression("partition_key = :pk AND numeric_attr > :min_val")
        .expression_attribute_values(
            ":pk",
            &AttributeValue::S(partition_key.to_string()),
        )
        .expression_attribute_values(
            ":min_val",
            &AttributeValue::N(min_value.to_string()),
        )
        .send()
        .await?;
    // handle response
    Ok(())
}

Never construct table or index names from user input. If dynamic table selection is required, validate the name against a strict allowlist before using it:

fn is_valid_table_name(name: &str) -> bool {
    matches!(name, "users" | "orders" | "audit_log")
}

async fn safe_query(client: &Client, table_candidate: &str, pk: &str) -> Result<(), String> {
    if !is_valid_table_name(table_candidate) {
        return Err("invalid table".into());
    }
    client.get_item()
        .table_name(table_candidate)
        .key("id", AttributeValue::S(pk.to_string()))
        .send()
        .await
        .map(|_| ())
        .map_err(|e| e.to_string())
}

Additionally, apply input validation at the Axum extractor layer to reject malformed or unexpected values before they reach the DynamoDB client. Use strong types and validation libraries to enforce constraints on identifiers, numeric ranges, and string patterns.

middleBrick’s scans can help detect endpoints that build DynamoDB expressions from raw input, providing findings tied to specific checks such as Input Validation and BOLA/IDOR. The reports include severity assessments and remediation guidance, helping teams prioritize fixes.