Rate Limiting Bypass in Axum with Cockroachdb

A Rate Limiting Bypass in an Axum service backed by Cockroachdb can occur when rate-limiting state is stored in the database and the implementation lacks proper concurrency controls or atomic increments. Axum is a Rust web framework that relies on middleware to enforce limits, and Cockroachdb is a distributed SQL database that provides strong consistency but does not automatically enforce rate limits at the SQL layer.

One common pattern is to store request counts per user or API key in a Cockroachdb table and increment a counter on each request. If the read-modify-write cycle is not atomic (for example, SELECT count, then UPDATE count WHERE id = $1), an attacker can issue rapid concurrent requests that race on the read phase, each reading the same current count, then each writing an incremented value. This race condition effectively allows the attacker to issue N concurrent requests while the counter is incremented only once per logical batch rather than N times, bypassing the intended limit.

Another bypass vector involves idempotency and retries. When Cockroachdb is used as a transactional store, client retries on transient errors can cause duplicate logical requests to be applied if the server does not deduplicate based on request identifiers. If Axum middleware does not integrate idempotency keys with the rate-limiting logic, an attacker can force repeated executions within the rate window despite the apparent enforcement at the middleware layer.

Distributed deployments exacerbate the issue. Because Cockroachdb spans multiple nodes, eventual consistency windows and transaction isolation nuances can allow slightly stale reads of the rate counter within short time windows. If Axum nodes use local caches or inconsistent transaction settings (for example, using `READ COMMITTED` without explicit locking), attackers can route requests to different nodes to exploit timing differences and increment the counter slower than expected per window.

These issues map to common implementation gaps rather than flaws in Axum or Cockroachdb themselves. The risk is not that the framework or database are insecure by design, but that the integration logic fails to enforce limits under concurrency, retries, and distribution. A thorough security scan using middleware-aware checks and database-aware validation is necessary to detect such bypasses in real-world deployments.

Remediation focuses on ensuring atomic increments, idempotent request handling, and strict transaction isolation when using Cockroachdb with Axum. Below are concrete, realistic code examples for Axum handlers and SQL logic that reduce the likelihood of a Rate Limiting Bypass.

First, use a dedicated table with a composite primary key to track request counts per key and window. The schema should enforce uniqueness and simplify atomic updates:

CREATE TABLE api_rate_log (
    key TEXT NOT NULL,
    window_start TIMESTAMPTZ NOT NULL,
    count INT NOT NULL DEFAULT 1,
    PRIMARY KEY (key, window_start)
);

In Axum, implement a handler that uses a single SQL upsert with an increment operation inside a serializable or repeatable-read transaction. Cockroachdb supports `UPSERT` and `INSERT ... ON CONFLICT DO UPDATE`, which ensures atomic increments:

use axum::{routing::post, Router};
use sqlx::postgres::PgPoolOptions;
use std::net::SocketAddr;

async fn handle_request(
    key: String,
    pool: &sqlx::PgPool,
) -> Result {
    let window_start = chrono::Utc::now().date_naive().and_hms_opt(0, 0, 0).unwrap();
    let result: Result<(i64,), sqlx::Error> = sqlx::query_as(
        "INSERT INTO api_rate_log (key, window_start, count) VALUES ($1, $2, 1) ON CONFLICT (key, window_start) DO UPDATE SET count = api_rate_log.count + 1 RETURNING count"
    )
    .bind(&key)
    .bind(window_start)
    .fetch_one(pool)
    .await?;

    if result.0 > 100 {
        return Err((axum::http::StatusCode::TOO_MANY_REQUESTS, "Rate limit exceeded".to_string()));
    }
    Ok(axum::http::StatusCode::OK)
}

#[tokio::main]
async fn main() {
    let pool = PgPoolOptions::new()
        .max_connections(5)
        .connect("postgresql://user:pass@localhost/db")
        .await
        .expect("pool");
    let app = Router::new().route("/api", post(move |req| handle_request("global_key".to_string(), &pool)));
    let addr = SocketAddr::from(([127, 0, 0, 1], 3000));
    axum::Server::bind(&addr).serve(app.into_make_service()).await.unwrap();
}

This approach places the increment inside the database transaction, removing the read-then-write race condition. Use serializable isolation if stricter guarantees are required, and ensure that the transaction retries handle Cockroachdb’s transaction conflict errors gracefully.

Second, integrate idempotency keys to mitigate retry-induced over-counting. Store processed idempotency keys with a TTL aligned to the rate window, and skip counting when a duplicate key is detected within the same window:

async fn handle_with_idempotency(
    key: String,
    idempotency_key: String,
    pool: &sqlx::PgPool,
) -> Result<(), (axum::http::StatusCode, String)> {
    let window_start = chrono::Utc::now().date_naive().and_homs_opt(0, 0, 0).unwrap();
    let tx = pool.begin().await.map_err(|e| (axum::http::StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;

    // Ensure idempotency key uniqueness within the window
    let idempotency_result: Result<(i64,), sqlx::Error> = sqlx::query_as(
        "INSERT INTO idempotency_keys (key, window_start) VALUES ($1, $2) ON CONFLICT DO NOTHING RETURNING 1"
    )
    .bind(&idempotency_key)
    .bind(window_start)
    .fetch_optional(&tx)
    .await?;

    if idempotency_result.is_none() {
        // Already processed this key in this window; do not increment rate count
        return Ok(());
    }

    let count_result: Result<(i64,), sqlx::Error> = sqlx::query_as(
        "UPDATE api_rate_log SET count = count + 1 WHERE key = $1 AND window_start = $2 RETURNING count"
    )
    .bind(&key)
    .bind(window_start)
    .fetch_one(&tx)
    .await?;

    if count_result.0 > 100 {
        tx.rollback().await.map_err(|e| (axum::http::StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
        return Err((axum::http::StatusCode::TOO_MANY_REQUESTS, "Rate limit exceeded".to_string()));
    }

    tx.commit().await.map_err(|e| (axum::http::StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
    Ok(())
}

Finally, prefer server-side enforcement by leveraging Cockroachdb constraints and avoiding client-side counting for critical limits. Combine this with short TTLs for in-memory caches if used, and validate that Axum middleware configurations align with the actual transaction boundaries. These concrete patterns reduce race conditions, retry amplification, and distribution-related bypasses specific to the Axum + Cockroachdb stack.