Api Rate Abuse in Axum with Mongodb

Rate abuse in an Axum service backed by MongoDB becomes likely when rate limiting is implemented only at the application layer or omitted entirely. Without a server-side or edge-enforced limit, an unauthenticated or under-limited endpoint can be called repeatedly, causing excessive MongoDB operations such as find, insert, or update. Each request consumes database connections and CPU, and poorly indexed queries can amplify the cost, leading to increased latency, connection pool exhaustion, and potential denial of service for legitimate users.

In a typical Axum handler, if requests are not bounded by a shared, distributed rate limiter, an attacker can exploit the unauthenticated attack surface to trigger high-frequency access patterns. For example, an endpoint that accepts user-supplied parameters and directly translates them into MongoDB queries may be vulnerable to parameter-driven load amplification if no request throttling is enforced. The absence of request validation or cost-sensitive query restrictions can further enable operations that scan large collections or perform aggregations, increasing read and write load on MongoDB.

Because middleBrick scans the unauthenticated attack surface, it can detect missing or weak rate limiting and highlight how an Axum + MongoDB stack may be abused. The scanner evaluates whether the API enforces limits on a per-client or global basis and checks whether high-cost endpoints are disproportionately exposed. Findings include evidence of missing controls and guidance on implementing rate limiting to reduce the attack surface and prevent resource exhaustion.

To mitigate rate abuse in Axum with MongoDB, enforce rate limiting at the service boundary and design MongoDB interactions to be efficient and bounded. Use tower-based rate limiting middleware in Axum to restrict requests per IP or API key, and combine this with MongoDB-side safeguards such as query timeouts, maximum document limits, and indexed fields to keep operations predictable.

Below are concrete Rust code examples for an Axum handler with MongoDB driver usage that incorporates these protections.

1. Rate limiting with tower and MongoDB query constraints

use axum::{routing::get, Router};
use mongodb::{Client, options::ClientOptions};
use std::net::SocketAddr;
use tower_http::rate_limit::{RateLimitLayer, RateLimitLayerConfig};
use tower_http::trace::TraceLayer;
use std::time::Duration;

async fn health_check() -> &'static str {
    "ok"
}

async fn get_items(
    mongodb_client: mongodb::Client,
    axum::extract::Query(params): axum::extract::Query::>,
) -> Result {
    let collection = mongodb_client.database("app").collection("items");
    // Enforce a reasonable limit on returned documents to prevent large scans
    let cursor = collection
        .find(params, mongodb::options::FindOptions::builder().limit(100).build())
        .await
        .map_err(|e| (axum::http::StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;

    let items: Vec = cursor
        .map(|doc| doc.map(|d| d.to_string()).unwrap_or_default())
        .collect::>()
        .await;

    Ok(format!("{:?}", items))
}

#[tokio::main]
async fn main() {
    // Configure MongoDB client with server selection timeout and socket timeouts
    let mut client_options = ClientOptions::parse("mongodb://localhost:27017")
        .await
        .expect("Failed to parse MongoDB URI");
    client_options.server_selection_timeout = Some(Duration::from_secs(5));
    client_options.connect_timeout = Some(Duration::from_secs(5));
    client_options.streams = Some(mongodb::options::StreamDescriptionOptions::builder()
        .max_connecting(10)
        .build());
    let mongodb_client = Client::with_options(client_options).expect("Valid client options");

    // Configure rate limiting: 10 requests per minute per IP
    let config = RateLimitLayerConfig::default()
        .layer_capacity(1000)
        .refill_interval(Duration::from_secs(60))
        .refill_amount(10);

    let app = Router::new()
        .route("/health", get(health_check))
        .route("/items", get(get_items))
        .layer(
            RateLimitLayer::new(config)
                .key_extractor(tower_http::rate_limit::KeyExtractors::single_ip()),
        )
        .layer(TraceLayer::new_for_http());

    let addr = SocketAddr::from(([127, 0, 0, 1], 3000));
    tracing::debug!(listening = %addr);
    axum::Server::bind(&addr)
        .serve(app.into_make_service())
        .await
        .unwrap();
}

2. Server-side timeouts and index-aware queries

Ensure MongoDB operations time out quickly and use indexes for filtered fields. The following example shows how to set a max execution time on reads and how to structure queries to leverage compound indexes, reducing the risk of long-running operations that can be exploited for resource exhaustion.

use mongodb::{bson::doc, options::FindOptions};
use std::time::Duration;

async fn search_items(
    collection: mongodb::Collection,
    filter: doc! { "category": "books" },
) -> mongodb::error::Result> {
    let mut options = FindOptions::builder()
        .max_time(Duration::from_secs(2)) // abort if operation exceeds 2 seconds
        .limit(50) // cap result size
        .build();

    // Ensure an index exists to prevent collection scans
    // db.items.createIndex({ category: 1, created_at: -1 })
    let cursor = collection.find(filter, options).await?;
    cursor.collect::>().await.into_iter().collect()
}

Additionally, apply middleware-level validation to reject requests with excessively large or malicious parameters before they reach MongoDB. Combine these controls with continuous scanning using tools like middleBrick to verify that rate limiting is correctly enforced and that high-cost endpoints remain protected.