Brute Force Attack in Axum with Dynamodb

A brute force attack against an Axum API that uses DynamoDB as a persistence layer typically targets authentication or session endpoints. When login or token verification logic relies on DynamoDB lookups without adequate rate controls, an attacker can systematically guess credentials or session identifiers. Each guess results in a DynamoDB query, and if those queries are unthrottled, the backend will attempt to satisfy every request within the 5–15 second scan window that middleBrick provides for unauthenticated testing.

In this combination, Axum handles HTTP routing and deserialization while DynamoDB supplies the user store. If the application implements naive iteration (e.g., scanning a table to find a matching username) or constructs queries with easily guessable keys, the attack surface expands. DynamoDB’s provisioned capacity and eventual consistency do not prevent high request volumes from reaching the service; they only shape latency patterns. middleBrick’s 12 parallel security checks include rate limiting and authentication testing, which can surface whether Axum endpoints backed by DynamoDB enforce sufficient request throttling and avoid timing differences that leak existence of accounts.

The risk is not that DynamoDB is inherently weak, but that Axum application code may not enforce per-entity or per-client attempt limits. Without such controls, an attacker can issue many requests that individually succeed but collectively compromise confidentiality or availability. Because DynamoDB charges scale with read capacity units, poorly bounded brute force attempts can also lead to cost increases. middleBrick’s findings in this context highlight missing rate limiting and weak authentication controls, mapping them to OWASP API Top 10 and common misconfigurations in serverless or cloud-native stacks.

To remediate brute force risks in an Axum service using DynamoDB, enforce strict rate limiting at the route level and ensure DynamoDB queries are keyed by exact identifiers. Prefer conditional writes and exponential backoff, and avoid any client-enumeration patterns. The following examples show concrete Axum handlers with DynamoDB code that align with secure design.

First, configure a rate limiter using axum::extract::State to share state across requests. This state can integrate with middleware that tracks attempts per IP or per user identifier before allowing a DynamoDB lookup.

use axum::{routing::post, Router, extract::State};
use std::sync::Arc;
use tokio::sync::Mutex;
use std::collections::HashMap;

struct RateLimiter {
    attempts: Mutex>,
    max_attempts: u32,
    window_secs: u64,
}

impl RateLimiter {
    fn allow(&self, key: &str) -> bool {
        let mut attempts = self.attempts.blocking_lock();
        let entry = attempts.entry(key.to_string()).or_insert(0);
        *entry += 1;
        *entry <= self.max_attempts
    }
}

async fn login_handler(
    State(limiter): State>,
    axum::Json(payload): axum::Json,
) -> Result, (axum::http::StatusCode, String)> {
    if !limiter.allow(&payload.username) {
        return Err((axum::http::StatusCode::TOO_MANY_REQUESTS, "rate limit exceeded".into()));
    }
    // Proceed to exact-key DynamoDB lookup
    Ok(axum::Json(LoginResponse { token: "example".into() }))
}

Second, implement the DynamoDB client with exact-key get_item and conditional writes to avoid scan operations. Use the official AWS SDK for Rust (aws-sdk-dynamodb) and supply a partition key that uniquely identifies the user, ensuring constant-time lookup regardless of attacker input.

use aws_sdk_dynamodb::Client;
use aws_sdk_dynamodb::types::AttributeValue;

async fn verify_user(client: &Client, username: &str, expected_password_hash: &str) -> bool {
    let pk = format!("USER#{}", username);
    let resp = client.get_item()
        .table_name("users")
        .key("pk", AttributeValue::S(pk))
        .send()
        .await
        .ok()
        .and_then(|out| out.item)
        .and_then(|item| item.get("password_hash").cloned())
        .and_then(|v| v.as_s().ok())
        .map(|stored| stored == expected_password_hash)
        .unwrap_or(false);
    resp
}

Third, apply exponential backoff on failed attempts and use conditional writes for operations that mutate authentication state. This prevents timing-based side channels and ensures that retries from legitimate clients do not amplify load on DynamoDB.

use aws_sdk_dynamodb::types::ConditionalOperator;

async fn record_failed_attempt(client: &Client, username: &str) -> Result<(), aws_sdk_dynamodb::Error> {
    let pk = format!("USER#{}", username);
    client.update_item()
        .table_name("users")
        .key("pk", AttributeValue::S(pk))
        .update_expression("SET failed_attempts = if_not_exists(failed_attempts, :start) + :inc")
        .condition_expression("attribute_exists(pk)")
        .set_expression_attribute_values({
            let mut m = std::collections::HashMap::new();
            m.insert(":start".into(), AttributeValue::N("0".into()));
            m.insert(":inc".into(), AttributeValue::N("1".into()));
            m
        })
        .send()
        .await?;
    Ok(())
}

By combining Axum middleware-driven rate limiting with precise DynamoDB keyed access and defensive update patterns, the API reduces the effectiveness of brute force attacks. These practices align with the remediation guidance that middleBrick provides in its findings, emphasizing configuration and code-level controls rather than runtime blocking.