Brute Force Attack in Actix with Dynamodb

A brute force attack against an Actix web service that uses DynamoDB as a backend typically exploits the absence of server-side rate limiting or account lockout. In this combination, each authentication or password reset attempt performs a read or query operation against DynamoDB. If the Actix handler does not enforce per-identifier throttling, an attacker can send many requests rapidly, driving up DynamoDB read capacity and probing valid user identifiers or passwords without detection.

Because DynamoDB charges and scales with request volume, unchecked requests can increase costs and, depending on provisioned capacity, may not surface throttling errors immediately, allowing sustained attempts. The Actix endpoint may return generic timing differences (e.g., user exists vs. invalid credentials), enabling attacker-side timing analysis to infer valid accounts. If the API also lacks exponential backoff or adaptive controls, the unauthenticated attack surface remains open, and findings from an unauthenticated scan will flag missing rate controls as a high-severity issue aligned with OWASP API Top 10 #3 (Broken Object Level Authorization) and ineffective Rate Limiting.

Additionally, if DynamoDB stores password hashes with low work factors or the Actix login handler does not enforce constant-time comparison, the combination weakens resilience against offline dictionary attacks. Findings will highlight insecure authentication flows and recommend server-side protections such as per-user rate limits, token-based lockout, and robust hashing, emphasizing that the scanner detects and reports these gaps rather than remediating them.

Apply rate limiting and backoff at the Actix layer before issuing any DynamoDB request, and ensure consistent error paths to avoid user enumeration. Below are concrete, realistic examples for an Actix handler that signs in a user while protecting DynamoDB usage.

First, add per-identifier rate limiting using a token-bucket stored in Redis (or another shared store) so that repeated attempts for the same username are throttled regardless of backend latency. Then use exponential backoff on DynamoDB operations to reduce pressure on provisioned capacity and avoid triggering throttling storms.

use actix_web::{web, HttpResponse, Result};
use aws_sdk_dynamodb::Client as DynamoDbClient;
use redis::Client as RedisClient;
use std::time::Duration;
use tokio::time::sleep;

async fn login_handler(
    body: web::Json,
    redis_client: web::Data,
    dynamodb: web::Data,
) -> Result {
    let username = &body.username;
    let conn = redis_client.get_connection().map_err(|_| HttpResponse::ServiceUnavailable().finish())?;

    // Per-identifier rate limit: allow 5 attempts per minute
    let key = format!("rl:{}", username);
    let count: usize = redis::cmd("INCR")
        .arg(&key)
        .query(&conn)
        .unwrap_or(0usize);
    if count == 1 {
        redis::cmd("EXPIRE")
            .arg(&key)
            .arg(60)
            .query(&conn)
            .unwrap_or(());
    }
    if count > 5 {
        return Ok(HttpResponse::TooManyRequests().json(serde_json::json!({
            "error": "rate limit exceeded"
        })));
    }

    // Exponential backoff helper
    async fn with_backoff(attempts: u32, mut op: F) -> Option
    where
        F: FnMut() -> Option,
    {
        let mut delay = Duration::from_millis(100);
        for _ in 0..attempts {
            if let Some(val) = op() {
                return Some(val);
            }
            sleep(delay).await;
            delay *= 2;
        }
        None
    }

    let username_clone = username.clone();
    let user = with_backoff(5, || {
        dynamodb
            .get_item()
            .table_name("users")
            .key("username", aws_sdk_dynamodb::types::AttributeValue::S(username_clone.clone()))
            .send()
            .await
            .ok()?
            ..item()
            .cloned()
    })
    .await;

    match user {
        Some(item) => {
            // Constant-time comparison placeholder; use subtle-digest crate in practice
            let valid = false; // verify_hash(&item["password_hash"], &body.password)
            if valid {
                Ok(HttpResponse::Ok().json(serde_json::json!({"status": "ok"})))
            } else {
                Ok(HttpResponse::Unauthorized().json(serde_json::json!({"error": "invalid credentials"})))
            }
        }
        None => {
            // Always run a dummy hash operation to keep timing consistent
            // subtle::eq(&[0u8; 32], &[1u8; 32]).unwrap_or(());
            Ok(HttpResponse::Unauthorized().json(serde_json::json!({"error": "invalid credentials"})))
        }
    }
}

#[derive(serde::Deserialize)]
struct LoginRequest {
    username: String,
    password: String,
}

This example demonstrates per-username rate limiting before any DynamoDB call and uses exponential backoff on the DynamoDB get_item operation to avoid driving excessive read requests. It also preserves a consistent code path for missing users to reduce timing-based enumeration. For production, ensure password hashing uses a memory-hard function (e.g., Argon2id) and that the rate-limiting store is shared across Actix workers.

In a middleBrick scan of such an endpoint, findings would map the missing or weak rate limiting to the Rate Limiting and Authentication checks, with remediation guidance aligned to OWASP API Top 10 and common compliance frameworks. If you automate scans, the CLI tool (middlebrick scan ) can integrate into scripts, while the GitHub Action can enforce a minimum score threshold in CI/CD to prevent regressions.