Type Confusion in Axum with Dynamodb

Type confusion in an Axum service that uses DynamoDB typically arises when data deserialized from DynamoDB is mapped into Rust types without strict schema enforcement. DynamoDB stores values as loosely typed attribute-value pairs, which can represent strings, numbers, binary blobs, nested maps, or lists. If an Axum handler deserializes a DynamoDB item into a generic map or a loosely-typed struct, an attacker can supply values that change the expected runtime type, leading to logic bypasses, panics, or unexpected behavior.

Consider an endpoint that expects a numeric user_id but receives a string or a map from DynamoDB due to inconsistent item definitions or malicious injected data. Axum extractors deserialize the response body or query parameters; if the downstream Rust types do not precisely match the expected DynamoDB shape, deserialization libraries (such as aws_sdk_dynamodb combined with serde) may silently coerce types or produce incorrect structures. This mismatch enables injection of crafted payloads that change control flow or cause denial of service.

An example scenario: a DynamoDB item contains a field role that should always be a string. If the item is retrieved and deserialized into a Rust enum without validating the discriminant, a crafted DynamoDB record can set role to a number or nested map, causing the deserializer to map it to an unintended variant. In Axum, if authorization checks rely on this field without strict validation, an attacker can escalate privileges by changing the type and bypassing role-based access controls.

Combining Axum’s extractor model with DynamoDB’s schema flexibility increases risk when developers assume types are guaranteed. Without strict runtime validation or schema-bound deserialization, the attack surface includes query parameters, path segments, and DynamoDB responses. This can lead to security-relevant outcomes such as authentication bypass, information disclosure, or unstable runtime behavior, which middleBrick’s authentication and BOLA/IDOR checks are designed to surface.

Remediation focuses on enforcing strict schemas when deserializing DynamoDB items in Axum handlers, validating types before use, and avoiding generic maps for security-sensitive fields. Below are concrete, working examples for Axum with the AWS SDK for Rust.

1. Define precise Rust structs that mirror the expected DynamoDB attribute structure and derive Deserialize and Serialize with serde. Use #[serde(rename_all = "camelCase")] to match DynamoDB’s naming conventions when using document-style attribute values.

use aws_sdk_dynamodb::types::AttributeValue;
use serde::{Deserialize, Serialize};

#[derive(Debug, Clone, Deserialize, Serialize)]
struct User {
    #[serde(rename = "userId")]
    user_id: u64,
    #[serde(rename = "email")]
    email: String,
    #[serde(rename = "role")]
    role: String,
}

impl TryFrom<aws_sdk_dynamodb::types::Item> for User {
    type Error = String;

    fn try_from(item: aws_sdk_dynamodb::types::Item) -> Result<Self, Self::Error> {
        let user_id = item.get("userId")
            .and_then(AttributeValue::as_n)
            .ok_or("missing or invalid userId")?
            .parse()
            .map_err(|_| "invalid userId format")?;
        let email = item.get("email")
            .and_then(AttributeValue::as_s)
            .ok_or("missing or invalid email")?
            .to_string();
        let role = item.get("role")
            .and_then(AttributeValue::as_s)
            .ok_or("missing or invalid role")?
            .to_string();
        Ok(User { user_id, email, role })
    }
}

2. In your Axum handler, use strict extractors and validate the deserialized type before any authorization check. This ensures that type confusion cannot be exploited via malformed DynamoDB items.

use axum::{routing::get, Router};
use aws_sdk_dynamodb::Client;

async fn get_user(
    Path(user_id): Path<String>,
    client: Extension&lt;Client&gt;,
) -> Result&lt;impl IntoResponse, (StatusCode, String)&gt; {
    let item = client.get_item()
        .table_name(&quot;Users&quot;)
        .key(&quot;userId&quot;, AttributeValue::N(user_id.to_string()))
        .send()
        .await
        .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;

    let user = User::try_from(item.item.ok_or((StatusCode::NOT_FOUND, &quot;user not found&quot;))?)?;
    if user.role != &quot;admin&quot; {
        return Err((StatusCode::FORBIDDEN, &quot;insufficient permissions&quot;));
    }
    Ok(Json(user))
}

#[tokio::main]
async fn main() {
    let config = aws_config::load_from_env().await;
    let client = Client::new(&config);
    let app = Router::new()
        .route(&quot;/users/:user_id&quot;, get(get_user))
        .layer(Extension(client));
    axum::Server::bind(&quot;0.0.0.0:3000&quot;).serve(app.into_make_service()).await.unwrap();
}

3. For numeric or enumerated fields, perform explicit validation instead of relying on Serde’s default coercion. This prevents a string being interpreted as a number or an unexpected variant match. The try_from implementation above parses user_id manually and returns an error if the format is incorrect, which is safer than using AttributeValue::as_n followed by Serde’s automatic deserialization.

4. Apply middleware or request guards in Axum to reject malformed types early. For example, validate that path and query parameters conform to expected patterns before they reach business logic. This reduces the window for type confusion via injection vectors that originate from query strings or headers rather than DynamoDB.

These steps align with checks performed by middleBrick’s Authentication, BOLA/IDOR, and Input Validation scans, which help identify missing type guarantees and over-permissive deserialization in API endpoints.