Information Disclosure in Axum with Dynamodb

When building web services with the Axum framework in Rust, integrating Amazon DynamoDB as a persistence layer introduces an information disclosure risk if application responses inadvertently expose sensitive data or implementation details. Information disclosure occurs when an API returns more data than intended, reveals internal identifiers, stack traces, or metadata, or fails to enforce proper authorization checks before returning a DynamoDB item.

In Axum, handlers typically deserialize incoming requests into strongly-typed structures and then query DynamoDB using the AWS SDK for Rust. If a handler constructs a response by directly serializing a DynamoDB GetItem or Query output without filtering or redaction, fields such as internal primary keys, version attributes, or debugging information can be exposed to clients. For example, a user profile endpoint that retrieves an item by user ID and returns the full DynamoDB attribute map may expose internal partition key schemas or secondary index metadata that should remain internal.

Another common pattern in Axum involves using extractors like Json<T> to bind request bodies and then constructing DynamoDB expression attribute values dynamically. If error handling inadvertently returns raw AWS SDK errors or internal paths in production responses, an attacker can learn about service internals. DynamoDB-specific metadata, such as ApproximateCreationDateTime or internal attribute type descriptors (e.g., S for string, N for number), can be surfaced in serialized responses when SDK responses are not carefully sanitized.

Additionally, misconfigured route parameters or extractor usage can lead to IDOR-like scenarios where an authenticated user can access another user’s DynamoDB item by manipulating identifiers in the URL or query parameters. Since Axum does not enforce authorization at the framework level, developers must explicitly validate that the requesting user has the right to view or modify the requested resource. Without such checks, an endpoint like GET /users/{user_id} could return another user’s data if the handler only uses user_id as a key to query DynamoDB and does not compare it with the authenticated subject’s identity.

The interaction between Axum’s routing and extractor model and DynamoDB’s schema-less attribute structure amplifies the risk. Developers might store sensitive fields such as email addresses, phone numbers, or internal tokens in DynamoDB attributes and inadvertently include them in responses. Overly permissive IAM policies attached to the application’s AWS credentials can further exacerbate information disclosure by allowing broader read access than necessary, making it easier for a compromised application token to extract sensitive items.

To mitigate these risks, developers should design Axum handlers to project only necessary fields when reading from DynamoDB, use serialization layers that exclude sensitive attributes, and validate ownership or permissions before returning any data. Leveraging DynamoDB’s attribute-level permissions and avoiding direct exposure of SDK metadata in responses are essential steps in preventing information disclosure in this technology stack.

Remediation focuses on strict data projection, explicit authorization checks, and safe serialization when interacting with DynamoDB in Axum. Below are concrete, idiomatic code examples that demonstrate secure patterns.

First, define a minimal response structure that includes only the fields intended for the client. Avoid returning the full DynamoDB attribute map.

#[derive(serde::Serialize)]
struct UserProfile {
    user_id: String,
    display_name: String,
    email: String,
}

Next, implement a handler that retrieves an item from DynamoDB and maps it to the safe structure, ensuring no extra metadata is serialized.

use aws_sdk_dynamodb::Client;
use axum::{routing::get, Router, extract::Path, http::StatusCode};

async fn get_user_profile(
    Path(user_id): Path,
    client: Extension<Client>,
    auth: Extension<AuthState>,
) -> Result<Json<UserProfile>, (StatusCode, String)> {
    // Authorization: ensure the requesting user is allowed to view this profile
    if auth.current_user_id != user_id && !auth.is_admin {
        return Err((StatusCode::FORBIDDEN, "Access denied".to_string()));
    }

    let response = client
        .get_item()
        .table_name("Users")
        .key("user_id", aws_sdk_dynamodb::types::AttributeValue::S(user_id.clone()))
        .send()
        .await
        .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;

    let item = response.item().ok_or_else(|| (StatusCode::NOT_FOUND, "User not found".to_string()))?;

    let profile = UserProfile {
        user_id: item.get("user_id")
            .and_then(|v| v.as_s().ok())
            .cloned()
            .ok_or_else(|| (StatusCode::INTERNAL_SERVER_ERROR, "Invalid user_id".to_string()))?,
        display_name: item.get("display_name")
            .and_then(|v| v.as_s().ok())
            .cloned()
            .unwrap_or_default(),
        email: item.get("email")
            .and_then(|v| v.as_s().ok())
            .cloned()
            .unwrap_or_default(),
    };

    Ok(Json(profile))
}

This pattern ensures that only selected fields are exposed and that authorization is evaluated per request. It also avoids returning DynamoDB type descriptors by using .as_s().ok() to extract string values safely.

For list endpoints, use a similar projection strategy and avoid exposing internal keys or indexes.

async fn list_users(
    Extension<client>: Extension<Client>,
) -> Result<Json<Vec<UserProfile>>, (StatusCode, String)> {
    let response = client
        .scan()
        .table_name("Users")
        .select(aws_sdk_dynamodb::types::Select::SPECIFIC_ATTRIBUTES)
        .projection_expression("user_id, display_name, email")
        .send()
        .await
        .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;

    let users: Vec<UserProfile> = response.items().unwrap_or_default()
        .iter()
        .filter_map(|item| {
            Some(UserProfile {
                user_id: item.get("user_id")?.as_s().ok()?.to_string(),
                display_name: item.get("display_name")?.as_s().ok()?.to_string(),
                email: item.get("email")?.as_s().ok()?.to_string(),
            })
        })
        .collect();

    Ok(Json(users))
}

Additionally, configure the AWS SDK client with least-privilege IAM policies and prefer environment variables or secure secret stores for credentials. In production, combine these handler patterns with middleware that standardizes error responses to avoid leaking stack traces or internal paths.