Insecure Design in Axum with Dynamodb

Insecure design in an Axum service that uses DynamoDB often stems from modeling data and access patterns without considering authorization boundaries and input constraints at the API layer. A typical pattern is to store items with a partition key such as user_id and rely solely of application logic to enforce that a request for /users/{user_id}/profile only accesses items where the DynamoDB partition key matches the authenticated user. If the route parameter is used directly as the key without validating ownership context or type constraints, the design can enable BOLA/IDOR across users. For example, an attacker who can increment numeric IDs might iterate over user_id=1001, user_id=1002, and so on, retrieving profiles they should not see because the application never enforces that the requester’s subject matches the stored key.

Another insecure design pattern is overprivileged IAM associated with the service’s DynamoDB credentials. If the Axum runtime uses a broad write or read policy on the table, a compromised endpoint can lead to mass data exposure or destructive operations. Input validation gaps also contribute: missing length checks, absent denylists for special characters in keys, and unchecked attribute sizes can trigger unexpected behavior when items are projected into domain models. Together, these design choices—permissive key construction, weak authorization checks, and insufficient input validation—create an insecure design where DynamoDB’s fast lookup amplifies the reach of an API flaw, turning a local logic bug into a cross-user data exposure path.

Remediation focuses on modeling ownership into the key design, tightening IAM, and validating inputs before they reach DynamoDB. First, encode the authenticated subject into the partition key so that queries are naturally scoped. For example, use a composite key like user#12345 as the partition key and a sort key for the item type. This design ensures that a request to fetch a profile can only retrieve items where the key prefix matches the requester, aligning storage with authorization.

Second, enforce authorization explicitly in the handler even when the key is user-scoped. Validate route parameters against the authenticated identity and reject mismatches before constructing the DynamoDB key. Combine this with strict input validation for length, format, and allowed characters. Below is a concrete Axum handler that demonstrates these patterns using the official AWS SDK for Rust.

use aws_sdk_dynamodb::types::AttributeValue;
use aws_sdk_dynamodb::Client;
use axum::extract::State;
use serde::{Deserialize, Serialize};

#[derive(Debug, Deserialize)]
struct ProfileParams {
    user_id: String,
}

#[derive(Debug, Serialize, Deserialize)]
struct Profile {
    pk: String,
    sk: String,
    email: String,
}

async fn get_profile(
    State(client): State,
    State(table_name): State,
    params: axum::extract::Query,
    auth: Option, // your auth extractor
) -> Result, (axum::http::StatusCode, String)> {
    let auth = auth.ok_or((axum::http::StatusCode::UNAUTHORIZED, "missing auth"))?;

    // Ensure the authenticated user matches the requested user_id
    if auth.user_id != params.user_id {
        return Err((axum::http::StatusCode::FORBIDDEN, "cannot access other user data".into()));
    }

    // Use a composite key design: partition key encodes ownership
    let pk = format!("user#{}", params.user_id);
    let sk = "profile#current".to_string();

    let output = client
        .get_item()
        .table_name(&table_name)
        .key(
            "pk",
            AttributeValue::S(pk),
        )
        .key(
            "sk",
            AttributeValue::S(sk),
        )
        .send()
        .await
        .map_err(|e| (axum::http::StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;

    let item = output.item().ok_or((axum::http::StatusCode::NOT_FOUND, "profile not found".into()))?;

    // Safe projection into domain model
    let profile = Profile {
        pk: item.get("pk").and_then(|v| v.as_s().ok()).unwrap_or(&"".to_string()).clone(),
        sk: item.get("sk").and_then(|v| v.as_s().ok()).unwrap_or(&"".to_string()).clone(),
        email: item.get("email").and_then(|v| v.as_s().ok()).unwrap_or(&"".to_string()).clone(),
    };

    Ok(axum::Json(profile))
}

Third, apply least-privilege IAM for the DynamoDB credentials used by the Axum service. Create a policy that restricts actions to specific table ARNs and enforces condition keys such as dynamodb:LeadingKeys so that the role can only access items where the partition key begins with user# associated with its caller identity. This prevents a compromised token from reading or writing unrelated items even if the application logic has a bug.

Finally, add schema-level constraints and runtime guards. Define a Stream Record or use DynamoDB Streams with a Lambda or separate validator to detect anomalous patterns, such as spikes in GetItem for adjacent numeric user identifiers, which may indicate enumeration attempts. Combine these measures with rate limiting at the Axum layer to reduce abuse potential. Together, these design changes align storage patterns with authorization, validate inputs rigorously, and minimize the impact of misconfiguration or credential leakage.