Password Spraying in Adonisjs with Dynamodb

Password spraying is an attack technique where one password is tried against many accounts to avoid account lockouts. When an AdonisJS application uses Amazon DynamoDB as its user store and lacks adequate protections, this pattern can be exercised against the authentication endpoint.

In AdonisJS, the default behavior of many community authentication packages is to return a consistent response time and a generic message such as “Invalid credentials” regardless of whether the username exists. If the application does not enforce rate limiting or progressive delays, an attacker can send many requests with different usernames and a single password (e.g., Password123) to enumerate valid user accounts without triggering account lockout mechanisms.

DynamoDB itself does not introduce the vulnerability, but its characteristics can amplify risk if the application relies on query patterns that are predictable. For example, if usernames map directly to partition keys, an attacker can probe known usernames with low request costs per attempt. If the application does not uniformly handle missing items, timing differences between a missing user and a user with an incorrect password may be detectable. These timing signals, combined with a lack of rate limiting, enable an attacker to infer account existence and refine their password choices.

Because AdonisJS typically runs on Node.js, the asynchronous nature of DynamoDB SDK calls can introduce variable latency depending on network conditions and provisioned capacity. An attacker can measure response times to distinguish between a nonexistent user (which may resolve faster) and a user that requires an additional password hash comparison. Without constant-time comparison logic and strict rate limiting, password spraying becomes a practical threat vector, especially in environments where usernames are predictable or exposed through other means.

OWASP API Security Top 10 mappings include API1:2023 Broken Object Level Authorization and API3:2023 Excessive Data Exposure when user enumeration leaks information through timing or response differences. These issues are not inherent to DynamoDB, but they emerge from the interaction between the framework’auth flow, the storage behavior, and missing controls.

Remediation focuses on eliminating timing differences, enforcing rate limits, and ensuring that user existence does not leak through response metadata. Below are concrete patterns and code examples for AdonisJS applications using DynamoDB.

1. Constant-time response and safe error messaging

Ensure that authentication responses are uniform and do not reveal whether a user exists. Use a constant-time password verification routine and return the same generic message for all failures.

// pseudo-implementation outline
import { timingSafeEqual } from 'crypto';

async function verifyPassword(inputPasswordHash, storedPasswordHash) {
  // Use timing-safe comparison where possible
  if (inputPasswordHash.length !== storedPasswordHash.length) {
    return false;
  }
  return timingSafeEqual(Buffer.from(inputPasswordHash), Buffer.from(storedPasswordHash));
}

2. Rate limiting and progressive delays

Apply rate limiting at the route level and consider adding small, randomized delays on failures to reduce timing distinguishability.

import { middleware } from '@adonisjs/core';

export const authRateLimiter = middleware(async (ctx, next) => {
  const identifier = ctx.request.ip();
  // Implement a sliding window counter using DynamoDB conditional writes
  const allowed = await checkAndIncrementRate(identifier);
  if (!allowed) {
    ctx.response.status(429).json({ message: 'Too many requests' });
    return;
  }
  await next();
});

async function checkAndIncrementRate(key) {
  // Example DynamoDB update with condition to enforce rate limits
  const params = {
    TableName: 'RateLimits',
    Key: { id: { S: key } },
    UpdateExpression: 'SET count = if_not_exists(count, :zero) + :inc, lastSeen = :now',
    ConditionExpression: 'attribute_not_exists(count) OR count < :threshold',
    ExpressionAttributeValues: {
      ':inc': { N: '1' },
      ':zero': { N: '0' },
      ':now': { N: Date.now().toString() },
      ':threshold': { N: '10' }
    },
    ReturnValues: 'UPDATED_NEW'
  };
  try {
    await dynamoDb.send(new UpdateCommand(params));
    return true;
  } catch (err) {
    if (err.name === 'ConditionalCheckFailedException') {
      return false;
    }
    throw err;
  }
}

3. Uniform handling of missing users

When querying DynamoDB, ensure that missing users undergo the same computational steps as valid users (e.g., hashing the input password with a dummy salt) to normalize timing.

import { DynamoDBClient, GetCommand } from '@aws-sdk/client-dynamodb';

const ddb = new DynamoDBClient({});

async function getUserOrDummy(username) {
  const cmd = new GetCommand({
    TableName: 'Users',
    Key: { username: { S: username } }
  });
  const res = await ddb.send(cmd);
  if (!res.Item) {
    // Return a deterministic dummy item to keep processing time consistent
    return { passwordHash: { S: 'dummy_hash_placeholder' }, salt: { S: 'dummy_salt' } };
  }
  return res.Item;
}

export async function authenticate(ctx) {
  const { username, password } = ctx.request.body();
  const user = await getUserOrDummy(username);
  const computed = await hashPassword(password, user.salt.S);
  const isValid = await verifyPassword(computed, user.passwordHash.S);
  if (!isValid) {
    ctx.response.status(401).json({ message: 'Invalid credentials' });
    return;
  }
  ctx.response.json({ token: 'safe-token' });
}

4. Secure storage and least privilege

Ensure that the DynamoDB table enforces least privilege for the application role and that encryption at rest is enabled. Avoid storing raw secrets or reversible data in DynamoDB.

# Example IAM policy condition: restrict access by partition key principal
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "dynamodb:GetItem",
        "dynamodb:UpdateItem"
      ],
      "Resource": "arn:aws:dynamodb:region:account:table/Users",
      "Condition": {
        "ForAllValues:StringEquals": {
          "dynamodb:LeadingKeys": ["${cognito-identity.amazonaws.com:sub}"]
        }
      }
    }
  ]
}

5. Continuous monitoring and compliance mapping

Use the middleBrick CLI to scan your authentication endpoints regularly. The scanner checks for authentication weaknesses, BOLA/IDOR risks, and input validation issues that could facilitate password spraying. For teams requiring deeper visibility, the Pro plan offers continuous monitoring and GitHub Action integration to fail builds if risk scores degrade.

# Example CLI usage
middlebrick scan https://api.example.com/openapi.json

Findings can map to OWASP API Top 10, SOC2, and GDPR requirements when you integrate scanning into your CI/CD pipeline.