Password Spraying in Actix with Dynamodb

Password spraying is an authentication attack where a single password is attempted against many accounts. When an Actix web service uses DynamoDB as its user store without protective measures, the combination can expose endpoints to enumeration and rate-limit bypass. In an unauthenticated scan, middleBrick tests for BOLA/IDOR and authentication weaknesses across the attack surface, including scenarios where login routes do not uniformly enforce delays or lockouts.

Consider an Actix handler that queries DynamoDB directly by username to retrieve a user record before verifying the provided password. If the handler returns different HTTP statuses or timing behavior depending on whether the username exists, an attacker can enumerate valid accounts. For example, a 404 for unknown users and a 200 with a failed-password message for known users makes the endpoint trivially enumerable via password spraying. Even when a generic error message is used, insufficient rate limiting on the login route allows an attacker to send many requests per second, attempting a small password across hundreds of accounts to find valid combinations.

DynamoDB itself does not introduce the vulnerability, but the way an Actix application integrates with it can amplify risk. If the application performs client-side filtering or conditional checks after retrieving multiple items (e.g., scanning a table for matching attributes), it may leak timing differences or expose behavior via error responses. An attacker conducting password spraying may also probe for inconsistent handling of credentials, such as missing lockout counters or one-time tokens, especially when authentication logic is distributed across middleware and service layers.

middleBrick’s authentication and rate-limiting checks highlight these risks by analyzing the unauthenticated API surface. It looks for endpoints where credentials are accepted, how errors are surfaced, and whether controls like captcha or progressive delays are present. In the context of Actix and DynamoDB, findings often point to missing server-side rate limiting, lack of per-user attempt tracking, and non-constant-time comparison of credentials, which together enable efficient spraying campaigns aligned with the OWASP API Top 10 and common attack patterns like credential stuffing.

To reduce the risk of password spraying when using DynamoDB with Actix, implement uniform authentication flows and robust rate controls. Always treat login requests the same regardless of user existence, use constant-time comparison where feasible, and enforce per-account and per-IP rate limits at the application or API gateway level. The following examples illustrate secure patterns for integrating DynamoDB within an Actix service.

First, define a DynamoDB client and a user record structure that avoids leaking existence through timing or status differences:

use aws_sdk_dynamodb::Client;
use serde::{Deserialize, Serialize};

#[derive(Debug, Serialize, Deserialize)]
struct User {
    username: String,
    password_hash: String,
    failed_attempts: i64,
    locked_until: Option,
}

async fn get_user(client: &Client, username: &str) -> Result<Option<User>, aws_sdk_dynamodb::Error> {
    let resp = client
        .get_item()
        .table_name("users")
        .key("username", aws_sdk_dynamodb::types::AttributeValue::S(username.to_string()))
        .send()
        .await?;
    Ok(resp.item.and_then(|item| {
        serde_dynamodb::from_hashmap(item).ok()
    }))
}

Second, ensure the login handler uses a fixed response shape and incorporates rate limiting before any DynamoDB interaction:

use actix_web::{web, HttpResponse};
use std::time::{SystemTime, UNIX_EPOCH};

async fn login(
    client: web::Data<Client>,
    form: web::Form<LoginForm>,
) -> HttpResponse {
    // Simple in-memory rate limiter example; use Redis or a distributed store in production
    static mut ATTEMPTS: std::collections::HashMap<String, (u64, u64)> = std::collections::HashMap::new();
    let now = SystemTime::now().duration_since(UNIX_EPOCH).unwrap().as_secs();
    let key = format("{}:{}", form.username, form.ip);
    unsafe {
        let entry = ATTEMPTS.entry(key.clone()).or_insert((0, now));
        if now - entry.1 < 60 {
            if entry.0 > 10 {
                return HttpResponse::TooManyRequests().json("Rate limit exceeded");
            }
            entry.0 += 1;
        } else {
            *entry = (1, now);
        }
    }

    match get_user(&client, &form.username).await {
        Ok(Some(user)) => {
            // Constant-time comparison placeholder; use subtle::eq in production
            let valid = subtle::eq(&form.password.as_bytes(), user.password_hash.as_bytes());
            if valid {
                HttpResponse::Ok().json("Authenticated")
            } else {
                HttpResponse::Unauthorized().json("Invalid credentials")
            }
        }
        _ => HttpResponse::Unauthorized().json("Invalid credentials"),
    }
}

Third, consider using DynamoDB conditional updates to implement atomic failed-attempt tracking and account lockout without introducing race conditions:

async fn record_failed_attempt(client: &Client, username: &str) -> Result<(), aws_sdk_dynamodb::Error> {
    client.update_item()
        .table_name("users")
        .key("username", AttributeValue::S(username.to_string()))
        .set_update_expression(Some("SET failed_attempts = if_not_exists(failed_attempts, :zero) + :inc"))
        .condition_expression(Some("attribute_not_exists(locked_until) OR locked_until < :now"))
        .expression_attribute_values(":inc", AttributeValue::N("1"))
        .expression_attribute_values(":zero", AttributeValue::N("0"))
        .expression_attribute_values(":now", AttributeValue::N("0")) // replace with actual timestamp logic
        .send()
        .await?;
    Ok(())
}

These patterns help ensure that authentication behavior does not leak information via status codes or timing, and that DynamoDB interactions are protected against high-volume spraying attempts. middleBrick’s scans can validate that such mitigations are present by checking for consistent error handling, rate-limiting headers, and secure authentication workflows.