Dictionary Attack in Axum with Dynamodb

A dictionary attack in an Axum API that uses DynamoDB as a persistence layer often arises from weak authentication endpoints and predictable user identifiers. When an endpoint such as /login or /users/{username} performs a lookup in DynamoDB without adequate protections, an attacker can systematically submit common usernames or emails to observe differences in response behavior. For example, an endpoint that queries GetItem on a users table keyed by username may return distinct HTTP status codes or response times for existing versus non-existent items, enabling an attacker to enumerate valid accounts without needing credentials.

In Axum, this typically manifests when route handlers directly forward user-supplied identifiers to DynamoDB and then conditionally render responses. A handler that calls GetItem and returns 200 with user data for found records and 404 for missing records creates an enumeration vector. Because DynamoDB charges per read and returns no data for missing keys when using GetItem, timing differences can be measurable, especially under network variance. An attacker running a dictionary attack may combine this with timing analysis or adaptive rate-limit probing to infer which usernames exist in the DynamoDB table.

The risk is compounded when the Axum application does not enforce uniform response times or consistent status codes for authentication flows. Without rate limiting at the API or application layer, an attacker can issue many requests per second, correlating successful responses with valid identities. This becomes particularly relevant for password reset or account confirmation endpoints that rely on DynamoDB lookups by email or username. Even when the API returns a generic message like “If the account exists, we have sent a reset link,” timing discrepancies or subtle behavioral differences may still leak information about the existence of a given username stored in DynamoDB.

Middleware and instrumentation in Axum can inadvertently expose stack traces or internal error messages when DynamoDB conditional checks fail or when item mutations encounter constraint violations. Such details can aid an attacker in refining a dictionary attack by narrowing the set of plausible usernames or understanding the data model. Because DynamoDB does not provide built-in account lockout or exponential backoff at the service level, the application must implement these controls explicitly to mitigate abuse.

To reduce the attack surface, developers should design Axum handlers to avoid revealing whether a username or email exists in DynamoDB. This includes using fixed-latency routines, constant-time comparison where applicable, and returning generic responses for authentication outcomes. Complementary network-level protections such as rate limiting and anomaly detection further reduce the feasibility of sustained dictionary attacks against DynamoDB-backed Axum services.

Remediation focuses on making authentication paths indistinguishable for valid and invalid users and enforcing strict input validation before DynamoDB operations. Below are concrete Axum handler patterns and supporting structures that reduce the risk of dictionary attacks when working with DynamoDB.

First, define a shared response type so that success and error paths return the same shape and HTTP status code:

use axum::{http::StatusCode, response::IntoResponse};
use serde::Serialize;

#[derive(Serialize)]
struct AuthResponse {
    message: String,
}

impl IntoResponse for AuthResponse {
    fn into_response(self) -> axum::response::Response {
        (StatusCode::OK, serde_json::to_string(&self).unwrap()).into_response()
    }
}

Use this in a login handler that queries DynamoDB via the AWS SDK for Rust. The handler performs a GetItem but always returns the same generic message and status code, preventing enumeration:

use aws_sdk_dynamodb::Client as DynamoDbClient;
use axum::{extract::State, Json};

async fn login_handler(
    State(dynamodb_client): State<DynamoDbClient>,
    Json(payload): Json<LoginPayload>,
) -> impl IntoResponse {
    let _ = dynamodb_client
        .get_item()
        .table_name("users")
        .key("username", aws_sdk_dynamodb::types::AttributeValue::S(payload.username.clone()).into())
        .send()
        .await;

    AuthResponse { message: "If the account exists, we have sent a reset link." }.into_response()
}

Second, apply rate limiting at the route or service layer to limit the number of requests per source identifier. Axum middleware can enforce this before any DynamoDB call is made:

use axum::middleware::Next;
use axum::extract::Request;
use std::net::SocketAddr;
use std::collections::HashMap;
use std::sync::{Arc, Mutex};

struct RateLimiter {
    counts: Mutex<HashMap<SocketAddr, u32>>,
    max_requests: u32,
}

impl RateLimiter {
    fn new(max_requests: u32) -> Arc<Self> {
        Arc::new(Self { counts: Mutex::new(HashMap::new()), max_requests })
    }

    fn allow(&self, addr: SocketAddr) -> bool {
        let mut counts = self.counts.lock().unwrap();
        let entry = counts.entry(addr).or_insert(0);
        if *entry < self.max_requests {
            *entry += 1;
            true
        } else {
            false
        }
    }
}

async fn rate_limit_middleware(
    limiter: Arc<RateLimiter>,
    request: Request,
    next: Next,
) -> impl IntoResponse {
    let addr = request.extensions().get::().copied().unwrap_or_default();
    if limiter.allow(addr) {
        next.run(request).await
    } else {
        (StatusCode::TOO_MANY_REQUESTS, "Rate limit exceeded").into_response()
    }
}

Third, validate and sanitize all inputs before using them as keys or expressions in DynamoDB queries. Avoid direct concatenation or interpolation that could lead to unexpected behavior or injection-like issues in the application logic:

use validator::Validate;

#[derive(Validate)]
struct LoginPayload {
    #[validate(length(min = 3, message = "Username too short"))]
    #[validate(regex(path = "RE_USERNAME", message = "Invalid characters"))]
    username: String,
}

// In handler:
async fn validated_login_handler(
    Json(payload): Json<LoginPayload>,
) -> Result<impl IntoResponse, (StatusCode, String)> {
    payload.validate().map_err(|e| (StatusCode::BAD_REQUEST, e.to_string()))?;
    // Proceed with DynamoDB call
    Ok(AuthResponse { message: "If the account exists, we have sent a reset link." }.into_response())
}

Finally, prefer parameterized queries and conditional updates rather than constructing expressions from raw user input. When updating or writing items, use DynamoDB’s condition expressions with strongly typed attribute values to avoid malformed updates that could expose internal states:

use aws_sdk_dynamodb::types::AttributeValue;

async fn update_user_status(
    client: &DynamoDbClient,
    username: &str,
    status: &str,
) -> Result<(), aws_sdk_dynamodb::Error> {
    client.update_item()
        .table_name("users")
        .key("username", AttributeValue::S(username.to_string()).into())
        .update_expression("SET #s = :val")
        .expression_attribute_names({ "#s".to_string(): "status".to_string() })
        .expression_attribute_values({
            ":val".to_string(): AttributeValue::S(status.to_string()).into()
        })
        .condition_expression("attribute_exists(username)")
        .send()
        .await?;
    Ok(())
}

These patterns reduce information leakage and ensure that DynamoDB-backed Axum endpoints do not become vectors for dictionary attacks. Defense in depth with rate limiting, uniform responses, input validation, and careful query construction is essential.