Rate Limiting Bypass in Axum with Firestore

Rate limiting is a control that restricts the number of requests a client can make to an endpoint within a defined time window. When an Axum service uses Firestore as a backend datastore without enforcing robust rate limiting at the application or infrastructure layer, certain patterns can allow a client to exceed intended request limits. This can occur when rate limiting is implemented only in Firestore operations (for example, by counting document writes) and not at the HTTP handler level, or when limits are applied per-IP but identifiers are not validated or are trivially spoofed.

In an Axum application, if rate limiting logic relies on Firestore reads/writes to track request counts, an attacker may exploit timing or transaction behaviors to avoid incrementing the counter correctly. For example, concurrent requests may not serialize as expected if the implementation uses separate read-modify-write cycles without atomic increments, allowing multiple requests to slip through before the limit is enforced. Additionally, if the identifier used to key the rate limit (e.g., API key, user ID, or IP) is missing, empty, or predictable, an attacker can rotate identifiers to bypass per-entity limits.

Another bypass scenario involves unauthenticated endpoints that expose Firestore-backed functionality without applying rate limits because the handler assumes limits are enforced by an external gateway. If the external layer is misconfigured or absent, Axum routes that directly query Firestore can be invoked at high volume. Because Firestore has its own quotas, an attacker may also probe whether quota exhaustion responses differ from application-level rate limit responses, using these differences to infer whether application controls are present.

An Axum handler that constructs Firestore queries dynamically based on user input without validating or normalizing identifiers can further weaken rate limiting. For instance, using raw path parameters to form document keys without canonicalization may allow equivalent keys that evade uniqueness checks. If the handler does not enforce strict schema validation before issuing Firestore operations, malformed or unexpected inputs might route to different logical counters or bypass intended constraints.

These combinations illustrate how a design that couples Axum routing with Firestore data access can unintentionally weaken rate limiting when limits are not enforced consistently, atomically, and early in the request lifecycle. Detection typically involves sending sequences of rapid requests with varying identifiers and observing whether limits are consistently applied across the stack.

To remediate rate limiting bypass risks in an Axum service that uses Firestore, enforce limits at the HTTP handler layer using a robust algorithm (such as token bucket or sliding window), and make the limiting key explicit and validated. Do not rely on Firestore operations alone to enforce rate limits, and ensure that identifiers are normalized and non-spoofable where possible.

Example Axum handler with consistent rate limiting using a token bucket stored in memory (for single-instance deployments) before calling Firestore. For distributed deployments, replace the in-memory store with a shared, atomic store that supports conditional increments (e.g., a rate limiting service or a transactional counter with compare-and-swap semantics).

use axum::{routing::get, Router};
use std::net::SocketAddr;
use std::sync::Arc;
use tokio::sync::Mutex;
use std::collections::HashMap;
use std::time::{Duration, Instant};

#[derive(Clone)]
struct RateLimiter {
    limits: Arc>>, // key -> (request_count, window_start)
    max_requests: usize,
    window: Duration,
}

impl RateLimiter {
    fn new(max_requests: usize, window: Duration) -> Self {
        Self {
            limits: Arc::new(Mutex::new(HashMap::new())),
            max_requests,
            window,
        }
    }

    async fn allow(&self, key: String) -> bool {
        let mut limits = self.limits.lock().await;
        let now = Instant::now();
        let entry = limits.entry(key.clone()).or_insert((0, now));
        if now.duration_since(entry.1) > self.window {
            entry.0 = 1;
            entry.1 = now;
            true
        } else if entry.0 < self.max_requests {
            entry.0 += 1;
            true
        } else {
            false
        }
    }
}

async fn handler(
    RateLimiter limiter: Arc<RateLimiter>,
    axum::extract::State(limiter): axum::extract::State<Arc<RateLimiter>>,
    axum::extract::Path(api_key): axum::extract::Path<String>,
) -> Result<axum::response::IntoResponse, (axum::http::StatusCode, String)> {
    let key = api_key.trim().to_lowercase(); // canonicalize key
    if key.is_empty() {
        return Err((axum::http::StatusCode::BAD_REQUEST, "missing key".into()));
    }
    if limiter.allow(key).await {
        // Proceed to Firestore operations
        Ok(axum::response::IntoResponse::into_response("OK"))
    } else {
        Err((axum::http::StatusCode::TOO_MANY_REQUESTS, "rate limit exceeded".into()))
    }
}

#[tokio::main]
async fn main() {
    let limiter = Arc::new(RateLimiter::new(10, Duration::from_secs(60)));
    let app = Router::new()
        .route("/api/:api_key/data", get(handler))
        .with_state(limiter);
    let addr = SocketAddr::from(([127, 0, 0, 1], 3000));
    axum::Server::bind(&addr)
        .serve(app.into_make_service())
        .await
        .unwrap();
}

When using Firestore, perform operations only after the rate limit check passes. Use Firestore transactions or batched writes where multiple writes must remain consistent, and prefer server-side counters if your deployment model requires strong consistency across instances. The following example shows a Firestore document update after a successful rate limit check, using the official Firestore client for Rust bindings (conceptual, adjust to the current SDK):

use firestore::FirestoreDb; // conceptual client
use axum::{routing::post, Json};

async fn firestore_protected_handler(
    limiter: State<Arc<RateLimiter>>,
    db: State<FirestoreDb>,
    Json(payload): Json<serde_json::Value>,
    Path(api_key): Path<String>,
) -> Result<Json<serde_json::Value>, (StatusCode, String)> {
    let key = api_key.to_lowercase();
    if !limiter.allow(key.clone()).await {
        return Err((StatusCode::TOO_MANY_REQUESTS, "rate limit exceeded".into()));
    }
    // Safe to proceed: record the operation in Firestore
    let doc_id = format!("requests/{}", uuid::Uuid::new_v4());
    let _ = db
        .create_obj(&doc_id, &serde_json::json!({ "api_key": key, "ts": chrono::Utc::now() }))
        .await
        .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
    Ok(Json(serde_json::json!({ "status": "recorded" })))
}

For distributed environments, avoid relying on in-memory counters. Instead, use a dedicated rate limiting backend that supports atomic increments and TTL, or implement a Firestore-based counter with optimistic concurrency control to prevent race conditions. Validate and sanitize all inputs used to identify rate limit entities, and ensure that unauthenticated endpoints explicitly apply limits to mitigate bypass risks introduced by misconfigured external layers.