Timing Attack in Chi with Dynamodb

A timing attack in the context of Chi (a Scala HTTP library) when interacting with DynamoDB arises from observable differences in response time caused by conditional logic that depends on secret-sensitive data, such as HMAC comparisons or user existence checks. In Chi, routes are defined as functions that compose middleware and business logic, and if a route first queries DynamoDB to retrieve a user record and then performs a constant-time comparison of a token or signature, the branching behavior introduces measurable timing variance.

DynamoDB itself does not introduce timing variance at the service level for read operations under normal conditions; however, how an application processes the retrieved data in Chi determines whether a timing side channel exists. For example, if a login endpoint retrieves an item by partition key and then compares a provided HMAC of a secret using a non-constant-time string comparison, an attacker can infer information about the expected HMAC by measuring response times. This is an application-level concern, but because Chi is often used to build API services that rely on DynamoDB as a backing store, the combination is common in production systems.

The attack flow typically involves an attacker sending many authentication requests with slightly altered tokens or parameters and observing response times. In Chi, middleware that performs early returns or short-circuits on validation failures can reduce timing variance, but if a DynamoDB GetItem is executed before a secret comparison, the network RTT and service processing become part of the observable timing. When the comparison occurs after the DynamoDB response, subtle differences in processing path length—such as failing early versus processing a full comparison—can be measurable over the network, especially in co-located environments or with sufficient request volume.

To illustrate, consider an endpoint that retrieves a user from DynamoDB and then compares a signature in Chi route logic. A vulnerable pattern might look like:

import cats.effect.IO
import software.amazon.awssdk.services.dynamodb.DynamoDbAsyncClient
import software.amazon.awssdk.services.dynamodb.model.GetItemRequest
import java.util.concurrent.CompletableFuture

// Non‑constant-time comparison after DynamoDB fetch
val getUserAndValidate = (userId: String, providedSig: String) => for {
  client ← IO.pure(DynamoDbAsyncClient.create())
  req = GetItemRequest.builder().tableName("users").key(Map("user_id").asJava).build()
  item ← IO.blocking(CompletableFuture.supplyAsync(() => client.getItem(req)).toScala)
  // … extract secret from item
  storedSig = "expected_value"
  _ ← if (storedSig == providedSig) IO.unit else IO.raiseError(new Exception("invalid"))
} yield ()

The comparison storedSig == providedSig is not constant-time, and although the DynamoDB call precedes it, the combined operation can still leak information over repeated requests. Chi’s composability makes it straightforward to structure routes so that early validation is skipped when a DynamoDB fetch is required, inadvertently coupling timing to secret-dependent branches. Attackers can exploit this by observing whether requests that trigger a DynamoDB read and a full comparison take longer than those that fail early due to malformed input.

Middleware design in Chi can either mitigate or exacerbate this risk. For example, placing validation before any I/O ensures that timing does not depend on external factors, but if the validation requires data from DynamoDB, you must ensure that the comparison logic itself is constant-time and that the execution path length does not vary with secret data. Using cryptographic libraries that provide constant-time comparison functions and ensuring that DynamoDB access patterns do not diverge based on secret values are essential practices when combining Chi and DynamoDB in security-sensitive endpoints.

Remediation focuses on two aspects: ensuring that no secret-dependent branching occurs after DynamoDB responses and using constant-time comparison for any cryptographic material. In Chi, structure routes so that input validation and constant-time checks occur before any DynamoDB interaction where possible. If data from DynamoDB is required for authorization, avoid using the retrieved secret in a way that changes execution time based on its value.

Use a constant-time comparison library (e.g., Java’s MessageDigest.isEqual for byte arrays) and ensure that all DynamoDB interactions follow a predictable pattern regardless of input. Below is a revised Chi route that performs early validation, uses a constant-time comparison, and keeps DynamoDB access after the check to the extent feasible:

import cats.effect.IO
import cats.implicits._
import org.http4s.{Request, Response, Status}
import org.http4s.dsl.io._
import software.amazon.awssdk.services.dynamodb.DynamoDbAsyncClient
import software.amazon.awssdk.services.dynamodb.model.GetItemRequest
import java.util.concurrent.CompletableFuture
import javax.crypto.Mac
import java.util.Arrays

// Constant‑time comparison helper
def constantTimeEquals(a: Array[Byte], b: Array[Byte]): Boolean =
  MessageDigest.isEqual(a, b)

// Chi route example with safer ordering
val authRoute = HttpRoutes[IO] {
  case req @ POST -> Root / "login" =>
    for {
      // Parse and validate input early
      body ← req.as[LoginRequest]
      if body.userId.nonEmpty && body.token.length == 64
      // Early validation to avoid unnecessary I/O
      client ← IO.pure(DynamoDbAsyncClient.create())
      reqDb = GetItemRequest.builder()
        .tableName("users")
        .key(Map("user_id" -> new AttributeValue(body.userId)).asJava)
        .build()
      itemFuture ← IO.blocking(CompletableFuture.supplyAsync(() ⇒ client.getItem(reqDb)).toScala)
      item = itemFuture.item()
      storedToken = item.get("auth_token").s()
      // Use constant‑time comparison for secrets
      isValid ← if (constantTimeEquals(storedToken.getBytes("UTF-8"), body.token.getBytes("UTF-8"))) {
        IO.pure(true)
      } else {
        IO.pure(false)
      }
      resp ← if (isValid) Ok() else Forbidden()
    } yield resp
}.orNotFound

Additional DynamoDB-specific practices include: using query filters that do not depend on secret values, ensuring partition key access patterns remain consistent, and avoiding conditional updates that depend on secret comparisons. In Chi middleware, you can also log timing outliers for monitoring, but remediation must focus on making execution paths and comparisons independent of secret data.

For production, combine this with infrastructure-level controls such as encrypted connections and fine-grained IAM policies, but remember that middleBrick DETECTS and REPORTS — it does NOT fix, patch, block, or remediate. It provides findings with remediation GUIDANCE to help developers address issues like timing side channels when integrating Chi and DynamoDB.