Password Spraying in Chi with Dynamodb

Password spraying is an authentication attack where a small number of common passwords are attempted against many accounts. In Chi, applications that store user credentials in Amazon DynamoDB can inadvertently enable or amplify this pattern when access controls and query design do not limit per-account or per-IP request rates.

DynamoDB itself does not perform authentication; it is a database. The vulnerability arises when application logic in Chi queries DynamoDB to validate a username or email with a provided password, and does so in a way that allows high request rates without throttling or adaptive responses. For example, a poorly designed sign-in function might query DynamoDB for a user record by username and then perform client-side password comparison. If this endpoint is exposed without rate limiting, an attacker can iterate over a list of common passwords across many usernames, staying below per-account lockout thresholds while still attempting hundreds of passwords per minute.

When DynamoDB is used as the backing store for identity data in Chi, lack of fine-grained access controls (such as row-level constraints) can also enable enumeration. If an API allows querying users by attributes like email without strict authorization checks, an attacker can confirm the existence of valid accounts before launching a spray attack. Because DynamoDB responses can differ subtly between existing and non-existing keys, timing or status differences may leak information that aids the attacker in refining their password list.

The risk is compounded when session or token handling in Chi relies on predictable patterns or when Multi-Factor Authentication (MFA) is not enforced. An attacker who successfully authenticates with a reused password gains access to whatever permissions that account holds in the application. Because DynamoDB stores data long-term, credential exposure from prior breaches can be leveraged in spray campaigns against Chi services that do not rotate passwords or enforce regular re-authentication.

middleBrick scans such API endpoints in Chi that interact with DynamoDB and flags findings like missing rate limiting, weak input validation, and excessive agency in AI-assisted authentication flows. The scan checks authentication mechanisms, input validation, rate limiting, and LLM/AI security to highlight where an attacker might abuse DynamoDB-backed logic for password spraying.

Remediation focuses on reducing the attack surface for password spraying by enforcing rate limits, avoiding user enumeration, and using secure patterns when accessing DynamoDB in Chi.

• Implement request-level throttling for sign-in endpoints. Use a token bucket or sliding window counter keyed by user identifier or IP address to limit attempts per time window.
• Always return the same generic error message and HTTP status code for invalid credentials, regardless of whether the username exists. This prevents account enumeration via timing or status differences.
• Use DynamoDB conditional writes and atomic counters to track failed attempts per user or IP, and enforce progressive delays or lockouts after thresholds are crossed.
• Prefer server-side password hashing with strong KMS-backed keys and avoid client-side comparison that requires reading the password hash from DynamoDB for each attempt. Where necessary, use parameterized queries with placeholders to avoid injection and ensure consistent response shapes.

Example secure sign-in handler in Chi using DynamoDB DocumentClient with rate-limiting considerations:

import { DynamoDBClient, GetCommand } from "@aws-sdk/client-dynamodb";
import { unmarshall } from "@aws-sdk/util-dynamodb";
import { fromCognitoIdentityPool } from "@aws-sdk/credential-providers";

const ddb = new DynamoDBClient({
  region: "us-east-1",
  credentials: fromCognitoIdentityPool({ IdentityPoolId: process.env.IDENTITY_POOL_ID }),
});

export async function chiSignIn(username, providedPassword, requestIP) {
  // Rate limiting check (pseudocode; implement with Redis or DynamoDB atomic counters)
  const canProceed = await checkRateLimit(requestIP, username);
  if (!canProceed) {
    return { error: "Authentication failed" };
  }

  const cmd = new GetCommand({
    TableName: process.env.USERS_TABLE,
    Key: { username: { S: username } },
  });

  try {
    const { Item } = await ddb.send(cmd);
    const user = unmarshall(Item);

    // Always perform hashing and comparison; avoid leaking existence via timing differences
    const isValid = await verifyPassword(providedPassword, user.password_hash);
    if (!isValid) {
      await recordFailedAttempt(requestIP, username); // atomic counter in DynamoDB
      return { error: "Authentication failed" };
    }

    // Reset failed attempts on success
    await resetFailedAttempts(requestIP, username);
    return { sessionToken: createSession(user) };
  } catch (err) {
    // Generic response for any error to prevent enumeration
    return { error: "Authentication failed" };
  }
}

async function verifyPassword(input, storedHash) {
  // Use a strong KDF (e.g., Argon2id) via a vetted library
  // This is a placeholder for actual implementation
  return await kdfCompare(input, storedHash);
}

For continuous protection in production, integrate the middleBrick CLI to scan your Chi endpoints that interact with DynamoDB. Use middlebrick scan <url> to get a security risk score and prioritized findings. Teams using the Pro plan can enable continuous monitoring so DynamoDB-backed authentication flows are rescanned on a schedule, with alerts sent to Slack or Teams when risk scores degrade.

In CI/CD, the middleBrick GitHub Action can enforce a minimum score before merges, ensuring that changes to DynamoDB access patterns in Chi do not introduce new weaknesses. The MCP Server allows AI coding assistants to trigger scans directly, helping developers validate security early without leaving their editor.