Api Rate Abuse in Actix with Mssql

Rate abuse in Actix web applications that use Microsoft SQL Server (Mssql) typically arises when repeated authenticated or unauthenticated HTTP requests trigger high-frequency database operations. Without explicit rate controls, an attacker can send many legitimate-looking queries that each execute Mssql statements, causing excessive load, potential lock contention, or costly query execution. This is especially risky when endpoints perform read-heavy operations or invoke scalar/Multi-result Mssql queries that do not enforce per-client throttling.

In an Actix service, routes are often mapped directly to handler functions that open a database connection, prepare T-SQL (e.g., via sqlx or diesel), and return results. If these handlers lack per-identity or global request-rate limits, an adversary can exploit the API by flooding the server with parallel requests. Each request issues one or more Mssql statements such as SELECT, INSERT, or parameterized batches. Because Mssql may hold locks or allocate memory for each batch, sustained high request rates can degrade response times for legitimate users and increase the risk of denial-of-service via resource exhaustion.

Moreover, when Actix endpoints expose search or filter functionality that dynamically constructs Mssql queries (e.g., building WHERE clauses from user input), repeated patterns can lead to heavy scans or index contention. Even if the database returns quickly, the cumulative effect of many concurrent requests can saturate connection pools or thread workers in Actix, creating a denial-of-service vector. The risk is compounded if authentication is weak or missing, enabling unauthenticated attackers to trigger rate-sensitive paths that should otherwise be limited to trusted clients.

middleBrick detects rate abuse by evaluating whether the API implements effective throttling and whether repeated patterns in Mssql interactions indicate exploitable endpoints. Findings include severity ratings and guidance on introducing per-user or global rate limits, backpressure strategies, and query optimization to reduce the load imposed by Mssql under high request volumes.

To mitigate rate abuse in Actix with Mssql, implement explicit rate limiting at the route level and optimize database interactions. Use Actix middleware such as actix-web-ratelimit or a token-bucket strategy backed by Redis to enforce per-identity and global limits. Combine this with query optimizations and safe Mssql patterns to reduce resource pressure.

1. Rate limiting in Actix routes

Apply a rate-limiter middleware to sensitive endpoints. For example, using actix-web-ratelimit with a Redis store:

use actix_web::{web, App, HttpServer, Responder};
use actix_ratelimit::{RateLimiter, MemoryStore};

async fn get_data() -> impl Responder {
    "OK"
}

#[actix_web::main]
async fn main() -> std::io::Result<()> {
    let store = MemoryStore::new();
    HttpServer::new(move || {
        App::new()
            .wrap(RateLimiter::new(store.clone()).with_rate(10, 1)) // 10 requests per second per key
            .route("/api/data", web::get().to(get_data))
    })
    .bind("127.0.0.1:8080")?
    .run()
    .await
}

2. Parameterized Mssql queries with controlled batch size

Always use parameterized queries to avoid injection and ensure plan reuse. Limit batch sizes and add server-side paging to keep Mssql work bounded per request:

use sqlx::{mssql::MssqlConnectOptions, ConnectOptions, Pool, Mssql};
use std::time::Duration;

async fn fetch_page(pool: &Pool<Mssql>, tenant_id: i32, page: i64, page_size: i64) -> sqlx::Result<Vec<MyItem>> {
    let items = sqlx::query_as!(
        MyItem,
        r#"
        SELECT Id, Name
        FROM Items
        WHERE TenantId = @P1
        ORDER BY Id
        OFFSET @P2 ROWS FETCH NEXT @P3 ROWS ONLY
        "#,
        tenant_id,
        (page - 1) * page_size,
        page_size
    )
    .fetch_all(pool)
    .await?;
    Ok(items)
}

fn build_pool() -> sqlx::Result<Pool<Mssql>> {
    let mut opts = MssqlConnectOptions::new("mssql://sa:Password123@localhost:1433");
    opts.max_connections(20)
        .connect_timeout(Duration::from_secs(5))
        .command_timeout(Duration::from_secs(30));
    Pool::connect_with(opts)
}

3. Server-side throttling with Mssql

Use Mssql to track request counts per key (e.g., API key or user ID) and reject or delay requests that exceed thresholds. This complements application-level rate limiting:

-- Example table to store request counts per window
CREATE TABLE ApiRateLog (
    Id BIGINT IDENTITY(1,1) PRIMARY KEY,
    ClientKey NVARCHAR(128) NOT NULL,
    WindowStart DATETIME2 NOT NULL,
    RequestCount INT NOT NULL
);
GO

-- Stored procedure to register a request and return current count
CREATE PROCEDURE dbo.RegisterRequest
    @ClientKey NVARCHAR(128),
    @WindowSeconds INT = 60
AS
BEGIN
    SET NOCOUNT ON;
    DECLARE @WindowStart DATETIME2 = DATEADD(SECOND, -@WindowSeconds, SYSUTCDATETIME());

    -- Clean old entries
    DELETE FROM ApiRateLog WHERE WindowStart < @WindowStart;

    DECLARE @CurrentCount INT;
    SELECT @CurrentCount = SUM(RequestCount) FROM ApiRateLog WHERE ClientKey = @ClientKey;

    IF @CurrentCount >= 100
    BEGIN
        THROW 50000, 'Rate limit exceeded', 1;
    END

    MERGE INTO ApiRateLog AS target
    USING (SELECT @ClientKey AS ClientKey, @WindowStart AS WindowStart) AS source
    ON target.ClientKey = source.ClientKey AND target.WindowStart = source.WindowStart
    WHEN MATCHED THEN
        UPDATE SET RequestCount = target.RequestCount + 1
    WHEN NOT MATCHED THEN
        INSERT (ClientKey, WindowStart, RequestCount) VALUES (source.ClientKey, source.WindowStart, 1);
END

Call this stored procedure from Actix handlers before executing the main query. If it throws an error, return HTTP 429 to the client. This ensures per-client windows are enforced close to the data store, reducing bursts that could overwhelm the application.