Broken Authentication in Axum with Dynamodb

Broken Authentication in an Axum service that uses DynamoDB typically occurs when session or token handling, credential verification, or identity checks are implemented in a way that leaks secrets or accepts untrusted input. Axum is a web framework for Rust that encourages composing middleware and extractors; if authentication logic is implemented as a custom extractor or handler without strict validation, it can expose timing differences or logic flaws when interacting with DynamoDB.

DynamoDB itself is a managed NoSQL store; it does not provide session management or authentication primitives. Therefore, authentication brokenness arises from how an Axum application reads and interprets data from DynamoDB. Common patterns include:

• Reading a user record by an ID derived from a token or session cookie without verifying ownership or scope.
• Comparing hashes or tokens in application code rather than using constant-time comparisons, leading to timing side channels.
• Using DynamoDB conditional writes or update expressions incorrectly, allowing privilege escalation if versioning or expected attribute checks are bypassed.
• Storing or transmitting secrets (e.g., API keys used to access DynamoDB) in logs or error messages, which can be exfiltrated through error responses.

An illustrative scenario: an Axum handler uses a JSON Web Token (JWT) to identify a user, extracts a user ID from the token, and performs a GetItem on DynamoDB using that ID. If the handler trusts the user-supplied identifier without re-validating that the authenticated subject owns that record, it enables Broken Object Level Authorization (BOLA), a subset of Broken Authentication. An attacker who can guess or enumerate valid IDs can access other users’ data even when JWT validation succeeds.

Additionally, misconfigured CORS or session cookie attributes in the Axum stack can expose authentication tokens to cross-origin contexts, making stolen credentials reusable across origins. DynamoDB Streams or exported data to downstream systems can further amplify exposure if sensitive fields are not encrypted at rest or in transit.

Because DynamoDB is often used for high-throughput profiles or session stores, developers sometimes implement caching or batching logic in Axum that inadvertently retains authentication context across requests. For example, reusing an AWS SDK client across handlers is safe, but storing per-request credentials in application state can lead to cross-request contamination. These design choices mean Broken Authentication findings often map to the broader categories in frameworks such as OWASP API Top 10 (e.g., API1:2023 Broken Object Level Authorization) and compliance frameworks like SOC2 and GDPR.

To remediate Broken Authentication when using Axum with DynamoDB, focus on strict ownership checks, constant-time comparisons, secure session handling, and safe error management. Below are concrete patterns and code examples that reduce risk for this stack.

1) Always re-validate ownership on each request. Do not trust path parameters or JWT sub claims alone; cross-check with DynamoDB attributes such as a partition key that includes the user ID.

// Example Axum handler with strict ownership check using AWS SDK for Rust
use aws_sdk_dynamodb::Client;
use axum::{routing::get, Router};
use std::net::SocketAddr;

async fn get_user_profile(
    user_id: String,
    token_subject: String, // from validated JWT claim
    client: &Client,
) -> Result<impl axum::response::IntoResponse, (axum::http::StatusCode, String)> {
    if user_id != token_subject {
        return Err((axum::http::StatusCode::FORBIDDEN, "Unauthorized".into()));
    }
    let resp = client
        .get_item()
        .table_name("users")
        .key("user_id", aws_sdk_dynamodb::types::AttributeValue::S(user_id.clone()))
        .send()
        .await
        .map_err(|e| (axum::http::StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;

    match resp.item() {
        Some(item) => Ok((axum::http::StatusCode::OK, item.clone())),
        None => Err((axum::http::StatusCode::NOT_FOUND, "Not found".into())),
    }
}

#[tokio::main]
async fn main() {
    let config = aws_config::load_from_env().await;
    let client = Client::new(&config);
    let app = Router::new().route("/users/:user_id", get(move |user_id, token_subject| get_user_profile(user_id, token_subject, &client)));
    let addr = SocketAddr::from(([127, 0, 0, 1], 3000));
    axum::Server::bind(&addr).serve(app.into_make_service()).await.unwrap();
}

This pattern ensures that the subject derived from the token matches the key used in DynamoDB, mitigating BOLA.

2) Use constant-time comparison for secrets or tokens stored in DynamoDB. Avoid early-exit string comparisons that leak timing information.

use subtle::ConstantTimeEq;

fn verify_stored_token(stored: &str, provided: &str) -> bool {
    let stored_bytes = stored.as_bytes();
    let provided_bytes = provided.as_bytes();
    if stored_bytes.len() != provided_bytes.len() {
        return false;
    }
    stored_bytes.ct_eq(provided_bytes).into()
}

3) Protect credential material used by the AWS SDK. Do not store AWS access keys in DynamoDB rows; instead, rely on IAM roles for EC2/ECS or instance metadata. If you must use temporary tokens, encrypt them before storage and decrypt only within secure runtime memory.

4) Harden error messages and logging. Ensure DynamoDB errors do not disclose internal schema or keys. In Axum, use a centralized error handler to standardize responses.

use axum::{http::StatusCode, response::IntoResponse};

fn handle_error(err: aws_sdk_dynamodb::Error) -> impl IntoResponse {
    (StatusCode::INTERNAL_SERVER_ERROR, "Request failed")
}

5) Apply least-privilege IAM policies for the DynamoDB table used by Axum. Scope permissions to the specific table and actions required (e.g., GetItem, UpdateItem) and avoid broad `dynamodb:*` permissions. Combine with VPC endpoints or private links to reduce exposure surface.

6) Enable encryption at rest and in transit (HTTPS) for DynamoDB, and consider using DynamoDB encryption client for additional field-level protection of highly sensitive attributes. Rotate credentials regularly and audit access patterns via CloudTrail.