Distributed Denial Of Service in Axum with Firestore

Axum is a Rust web framework that encourages asynchronous handlers and non-blocking I/O. When an Axum service performs Firestore operations, each request typically holds an asynchronous runtime resource (e.g., a Tokio task and network connection) while waiting for remote responses. If Firestore calls are unbounded or lack per-request timeouts and concurrency limits, an attacker can send many concurrent requests that keep server-side tasks alive for long durations. This consumes limited runtime resources such as memory and thread pool capacity, leading to request queue buildup and elevated latency for legitimate traffic. The vulnerability is not in Firestore itself, but in how Axum services orchestrate and throttle access to Firestore under load.

OpenAPI/Swagger spec analysis can highlight endpoints that invoke Firestore without explicit rate limiting or request-size constraints. middleBrick runs 12 security checks in parallel, including Rate Limiting and Input Validation, to identify unbounded or poorly bounded Firestore interactions in Axum APIs. For example, an endpoint that accepts a broad query object and forwards unbounded filter criteria to Firestore may allow an attacker to craft queries that trigger full collection scans, increasing Firestore read operations and prolonging handler execution. When combined with missing request validation, this can amplify resource consumption and contribute to a distributed denial of service condition.

SSRF findings are also relevant: if an Axum endpoint accepts user input that influences Firestore URLs or paths, an attacker may force the server to make unintended external calls, tying up resources further. middleBrick tests SSRF and highlights endpoints where untrusted input reaches Firestore-related logic. Because Firestore operations are often latency-sensitive, any uncontrolled recursion or retry behavior in Axum code can exacerbate denial-of-service conditions. The scanner correlates findings across categories—Rate Limiting, Input Validation, and SSRF—to provide a prioritized view of how the Axum + Firestore stack may be abused to exhaust service capacity.

Apply strict input validation and schema constraints before constructing Firestore queries in Axum. Use strongly typed structs and validate with a crate like validator to ensure only expected fields and bounded values reach Firestore. This reduces the chance of expensive queries and prevents malformed requests from consuming resources.

use axum::{routing::get, Router};
use serde::Deserialize;
use validator::Validate;

#[derive(Deserialize, Validate)]
struct QueryParams {
    #[validate(length(max = 100))]
    tag: Option,
    #[validate(range(min = 1, max = 50))]
    limit: Option,
}

async fn search_handlers(
    params: Result,
) -> Result {
    let params = params.map_err(|_| axum::http::StatusCode::BAD_REQUEST)?;
    // Build Firestore query using validated params only
    Ok("ok".to_string())
}

fn build_router() -> Router {
    Router::new().route("/search", get(search_handlers))
}

Set explicit timeouts and concurrency limits on Firestore calls within Axum handlers. Use tokio::time::timeout to bound individual requests and a semaphore to restrict concurrent Firestore operations, preventing resource exhaustion under high load.

use std::sync::Arc;
use tokio::sync::Semaphore;
use std::time::Duration;
use firestore_rs::{FirestoreDb, FirestoreQuery};

async fn query_firestore_with_limits(
    db: Arc<FirestoreDb>,
    sem: Arc<Semaphore>,
    collection: &str,
) -> Result<Vec<serde_json::Value>, &'static str> {
    let _permit = sem.acquire().await.ok_or("semaphore closed")?;
    let timeout_duration = Duration::from_secs(5);
    tokio::time::timeout(timeout_duration, async move {
        let query: FirestoreQuery = db.collection(collection).limit(100).into();
        db.run_query(query).await.map_err(|_| "db error")
    })
    .await
    .map_err(|_| "timeout")?
}

// In your Axum handler:
// let results = query_firestore_with_limits(db, semaphore, "items").await?;

Enforce rate limiting at the route level using tower-based middleware or a per-route pattern to control request bursts destined for Firestore. Combine request-size caps and pagination to avoid large queries that increase processing time and Firestore read costs.

use axum::extract::State;
use std::collections::HashMap;

struct AppState {
    rate_limiter: crate::Limiter, // application-level token bucket or sliding window
}

async fn guarded_endpoint(
    State(state): State<Arc<AppState>>,
) -> Result<String, (axum::http::StatusCode, String)> {
    state.rate_limiter
        .check()
        .map_err(|_| (axum::http::StatusCode::TOO_MANY_REQUESTS, "rate limit exceeded".into()))?;
    // proceed to Firestore call
    Ok("ok".into())
}

middleBrick scans Axum endpoints to confirm that these controls are present. The dashboard can track how often rate limiting and timeouts are triggered, helping you tune thresholds. The CLI provides JSON output for automation, and the GitHub Action can fail a build if a Firestore endpoint lacks explicit timeout or concurrency guards.