Password Spraying in Axum with Dynamodb

Password spraying is an authentication attack where a small number of passwords are tried against many accounts to avoid account lockouts. In Axum applications that rely on Amazon DynamoDB as the identity store, this pattern can be especially risky when endpoint behavior does not differentiate between a nonexistent user and a failed password. If your Axum routes perform a read from DynamoDB before validating the supplied password, an attacker can enumerate valid usernames or email addresses by observing timing differences or response semantics. Even when Axum returns a generic authentication failure, the combination of a predictable login endpoint and a DynamoDB-backed user table can allow iterative spraying across accounts.

DynamoDB-specific factors in Axum amplify the exposure. Because DynamoDB has strongly consistent and eventually consistent reads, an application may unintentionally rely on timing or consistency modes to infer account existence. If your Axum code queries by partition key (e.g., username) and does not enforce a constant-time flow for both missing and existing users, network and service latencies can leak information. Moreover, if Axum performs additional per-request operations such as fetching user metadata or generating one-time tokens from DynamoDB only for valid users, the behavioral footprint grows. Without proper mitigations like global rate limiting and uniform error handling, an attacker can correlate request patterns with DynamoDB provisioned capacity metrics to refine spraying campaigns.

Consider an Axum login handler that first retrieves an item from DynamoDB and then verifies the password. An example vulnerable pattern looks like this:

// Vulnerable pattern: user enumeration via timing and DynamoDB response
async fn login_handler(
    db: &aws_sdk_dynamodb::Client,
    username: String,
    password: String,
) -> Result<impl warp::Reply, warp::Rejection> {
    let key = json!({ "username": username });
    let output = db.get_item()
        .table_name("users")
        .set_key(Some(key))
        .send()
        .await?;

    if let Some(item) = output.item() {
        let stored_hash: String = item.get("password_hash")
            .and_then(|v| v.as_s().ok())
            .map(|s| s.to_string())
            .unwrap_or_default();
        if verify_password(&password, &stored_hash) {
            return Ok(warp::reply::json(&"OK"));
        }
    }
    // Generic failure but timing may differ
    Ok(warp::reply::json(&"Invalid credentials"))
}

An attacker can send password guesses for a set of candidate usernames and measure response times or error shapes. If existing accounts trigger different code paths (e.g., fetching extra attributes or secondary indexes), the observable surface widens. The same risk increases when Axum integrates features such as multi-factor authentication or password history checks that issue additional DynamoDB reads/writes only for valid users.

To align with best practices and reduce the effectiveness of password spraying, treat authentication as a single, uniform operation. Combine DynamoDB conditional writes for lockout metadata, enforce global rate limits across identities, and ensure that error responses and timing remain consistent regardless of user existence. middleBrick scans can surface these behavioral discrepancies by correlating authentication checks across the 12 security checks, including Rate Limiting and Authentication, and provide prioritized findings with remediation guidance.

Remediation focuses on making authentication behavior independent of account existence and on hardening DynamoDB interactions in Axum. Use a constant-time password verification flow: always perform a read (or a lightweight query) with the same shape, and run the password check regardless of whether the item exists. Return identical HTTP status codes and response shapes to prevent timing or body-based leakage.

Below is a hardened Axum login handler that mitigates enumeration via DynamoDB patterns:

use aws_sdk_dynamodb::Client;
use axum::{response::IntoResponse, Json};
use serde::{Deserialize, Serialize};

#[derive(Deserialize)]
struct LoginRequest {
    username: String,
    password: String,
}

#[derive(Serialize)]
struct LoginResponse {
    message: String,
}

// Constant-time verification stub
fn verify_password(input: &str, stored_hash: &str) -> bool {
    // Use a constant-time comparison in production (e.g., subtle::ConstantTimeEq)
    // This is illustrative
    input == stored_hash
}

async fn login_handler(
    db: Client,
    Json(payload): Json<LoginRequest>,
) -> impl IntoResponse {
    let key = json!({ "username": payload.username });
    let output = db.get_item()
        .table_name("users")
        .set_key(Some(key))
        .consistent_read(true) // prefer strongly consistent reads for uniform behavior
        .send()
        .await
        .map_err(|_| (axum::http::StatusCode::INTERNAL_SERVER_ERROR, ()))?;

    // Always derive a dummy hash to keep timing similar
    let dummy_hash = "{HASH_PLACEHOLDER}";
    let stored_hash = output.item()
        .and_then(|item| item.get("password_hash").and_then(|v| v.as_s().ok()))
        .unwrap_or(dummy_hash);

    if verify_password(&payload.password, stored_hash) {
        // Optionally fetch extra attributes in a second request to avoid branching on existence
        // Return success uniformly
        Json(LoginResponse { message: "Authenticated".to_string() }).into_response()
    } else {
        // Same shape and status for failure
        Json(LoginResponse { message: "Invalid credentials".to_string() }).into_response()
    }
}

Complement this with server-side protections: enable DynamoDB auto-scaling to reduce timing variability due to throttling, and use conditional writes for login metadata (e.g., failed attempt counters) to implement lockouts without exposing user existence. middleBrick’s GitHub Action can be added to CI/CD pipelines to fail builds if risk scores degrade, while the Pro plan supports continuous monitoring to detect anomalous authentication patterns across scans.

For long-term resilience, rotate credentials stored in DynamoDB, prefer parameterized queries to avoid injection, and validate input against schema constraints. The MCP Server allows you to scan APIs directly from development environments, helping to catch insecure authentication flows early. These measures reduce the likelihood that password spraying against Axum endpoints backed by DynamoDB will succeed or remain undetected.