Distributed Denial Of Service in Axum with Dynamodb

Axum is a Rust web framework that encourages asynchronous handlers and non-blocking I/O. When an Axum service makes synchronous or poorly managed calls to Amazon DynamoDB, the API surface can become susceptible to distributed denial of service (DDoS) conditions. The risk is not in the protocol itself, but in how requests interact with downstream latency, retry behavior, and resource saturation.

In a black-box scan, middleBrick tests unauthenticated endpoints and checks for missing rate controls and error handling patterns that can amplify DDoS impact. If an endpoint accepts user input and uses it directly as a DynamoDB key or filter without validation, an attacker can craft requests that generate excessive internal retries, hot partitions, or long-running queries. DynamoDB returns throttling responses when provisioned capacity is exceeded; however, an Axum handler that does not respect Retry-After headers or does not implement backpressure can continue to spawn tasks, exhausting thread pools or runtime resources.

Consider an endpoint that queries a DynamoDB table by a user identifier without pagination or timeout constraints:

async fn get_user_profile(uid: String, client: &aws_sdk_dynamodb::Client) -> Result<UserProfile, (StatusCode, String)> {
    let output = client.get_item()
        .table_name("UserProfiles")
        .set_key(Some(uid.clone().into()))
        .send()
        .await
        .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
    // deserialize and return
}

If the provided uid maps to a hot partition key, DynamoDB may throttle requests. If the Axum handler retries aggressively or blocks the runtime thread while waiting, concurrent request volume can grow, leading to queue saturation. middleBrick’s checks for rate limiting and input validation highlight such patterns, noting that missing request-level timeouts and missing concurrency limits are observable risk factors.

Additionally, unauthenticated LLM endpoints (if present) combined with DynamoDB integrations can expose system prompts or operational patterns that aid an attacker in crafting scalable abuse scenarios. middleBrick’s LLM/AI Security checks specifically test for system prompt leakage and active prompt injection to ensure that integration details are not inadvertently disclosed through API responses.

The scanner evaluates 12 security checks in parallel, including Authentication, Input Validation, Rate Limiting, and Data Exposure. For this Axum + DynamoDB combination, findings often surface around missing rate limiting on key paths, lack of paging controls, and missing timeouts. These findings are mapped to OWASP API Top 10 and provide prioritized remediation guidance without implying automatic fixes.

To reduce the DDoS surface when Axum interacts with DynamoDB, apply defensive patterns at the handler, client, and infrastructure levels. The goal is to limit resource consumption, enforce backpressure, and fail gracefully under load.

1. Add request validation and size limits

Validate identifiers before they reach DynamoDB. Reject malformed or excessively long keys early.

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

fn validate_uid(uid: &str) -> bool {
    !uid.is_empty() && uid.len() <= 128 && uid.chars().all(|c| c.is_ascii_alphanumeric() || c == '-' || c == '_')
}

Use this validation in your handler:

async fn get_user_profile(
    Path(uid): Path<String>,
    client: Extension<aws_sdk_dynamodb::Client>,
) -> Result<Json<UserProfile>, (StatusCode, String)> {
    if !validate_uid(&uid) {
        return Err((StatusCode::BAD_REQUEST, "Invalid user identifier".to_string()));
    }
    // safe to proceed
}

2. Configure timeouts and backoff

Set timeouts on the AWS SDK client and use exponential backoff to avoid prolonged blocking and retry storms. With the official SDK, you can customize the client’s timeout configuration:

use aws_sdk_dynamodb::config::{Config, Region, BehaviorVersion};
use aws_sdk_dynamodb::Client;
use aws_smithy_client::erase::DynConnector;
use aws_smithy_client::timeout::TimeoutConfig;

let timeout_config = TimeoutConfig::new()
    .with_operation_timeout(std::time::Duration::from_secs(2));
let config = Config::builder()
    .region(Region::new("us-east-1"))
    .behavior_version(BehaviorVersion::latest())
    .timeout_config(timeout_config)
    .build();
let client = Client::from_conf(config);

3. Enforce rate limiting and concurrency caps

Use Axum middleware or tower-layer patterns to limit requests per IP and restrict concurrent DynamoDB calls. For example, a simple rate limit with governor:

use governor::{Quota, RateLimiter};
use std::num::NonZeroU32;
use std::time::Duration;

let quota = Quota::per_second(NonZeroU32::new(100).unwrap()).allow_burst(NonZeroU32::new(200).unwrap());
let limiter = RateLimiter::new(quota);

Apply the limiter inside your route or as a tower layer to prevent bursts that can amplify DynamoDB throttling into service-wide degradation.

4. Use pagination and projection

Avoid scanning large datasets. If queries must iterate, use DynamoDB pagination with a page size limit and project only required attributes.

async fn scan_users(client: &aws_sdk_dynamodb::Client) -> Result<Vec<UserSummary>, (StatusCode, String)> {
    let mut paginator = client.scan().table_name("Users").into_paginator();
    let mut items = Vec::new();
    while let Some(page) = paginator.next().await {
        let page = page.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
        for item in page.items.unwrap_or_default() {
            // extract only needed fields
            let id: String = item.get("id").and_then(|v| v.as_s().ok()).map(|s| s.to_string()).unwrap_or_default();
            items.push(UserSummary { id });
        }
        if items.len() > 1000 { break; } // safety cap
    }
    Ok(items)
}

5. Respect Retry-After and implement circuit breaking

When DynamoDB returns a throttling response, propagate appropriate HTTP status and avoid retries that worsen congestion. Use a circuit breaker pattern to temporarily back off when error rates exceed a threshold. While Axum does not provide a built-in circuit breaker, integrate crates like tough or implement a lightweight state guard to reduce load during sustained throttling.

These steps align with middleBrick’s remediation guidance, which emphasizes input validation, rate limiting, and timeouts. The scanner’s findings map to compliance frameworks and provide prioritized guidance without claiming to patch or block issues automatically.