Password Spraying in Dynamodb

Password spraying targets authentication endpoints by attempting a small set of common passwords (e.g., "Password123", "admin") across many user accounts simultaneously. Unlike credential stuffing, which uses breached password pairs, spraying relies on weak, reused passwords. In DynamoDB-backed APIs, this attack often exploits two critical misconfigurations: excessive data exposure via unscrutinized query operations and missing rate limiting on authentication endpoints.

DynamoDB-specific attack patterns emerge when an application's authentication logic directly interfaces with a user table using operations like Scan or poorly constrained Query. For example, an API endpoint POST /auth/login might accept a username and password, then execute a DynamoDB Scan with a FilterExpression to find a matching user record:

const params = {
  TableName: 'Users',
  FilterExpression: 'username = :u AND password = :p',
  ExpressionAttributeValues: {
    ':u': { S: username },
    ':p': { S: hashedPassword }
  }
};
// Vulnerable: Scan reads entire table, applies filter client-side
dynamoDB.scan(params, (err, data) => { /* ... */ });

This pattern is dangerous for three reasons. First, Scan reads the entire table, which is inefficient and costly, but more critically, it may return multiple items if the filter is weak (e.g., case-insensitive comparison). Second, if the password field stores plaintext or weakly hashed values (e.g., MD5), an attacker can systematically test common passwords by observing response differences (e.g., "user not found" vs. "invalid password"). Third, without application-layer rate limiting, the API endpoint can be hammered with thousands of attempts per minute from a single IP.

A more subtle variant involves blind password spraying where the API returns generic error messages ("invalid credentials") for both non-existent users and wrong passwords. An attacker can still enumerate valid usernames by probing for timing differences or error codes in DynamoDB's response metadata (e.g., ConsumedCapacity). If the Scan operation on a non-existent user returns fewer read capacity units than on a matching user, side-channel information leaks.

Finally, if the DynamoDB table uses a partition key of username but the API accepts a user_id instead, an attacker might bypass the partition key constraint by injecting a FilterExpression that scans across all partitions. This is especially risky if the OpenAPI spec exposes query parameters that map directly to DynamoDB filter expressions without server-side validation.

Detecting password spraying vulnerabilities in DynamoDB-backed APIs requires examining both the runtime behavior of authentication endpoints and the API contract (OpenAPI spec). middleBrick's black-box scanning approach tests for these issues without credentials by focusing on three core checks:

• Authentication (BOLA/IDOR): The scanner probes the login endpoint with a sequence of common passwords for a single valid username (or a list of enumerated usernames) to determine if the system locks out accounts or throttles requests. A lack of response variation or consistent HTTP 200 responses with vague error messages indicates susceptibility.
• Rate Limiting: middleBrick sends rapid-fire authentication requests (e.g., 100 attempts in 10 seconds) and observes HTTP status codes (429 Too Many Requests) or response delays. DynamoDB itself does not enforce rate limits at the table level; this must be implemented at the API layer (API Gateway, ALB, or application code). Missing throttling is a clear risk.
• Data Exposure: The scanner attempts to manipulate query parameters (e.g., ?filter=password='') to induce DynamoDB Scan operations that return excessive user records. If an API endpoint returns more than one user record for a given query, it may allow username enumeration or bulk credential harvesting.

middleBrick also analyzes the OpenAPI/Swagger specification for dangerous patterns. For example, if the spec defines a POST /users/search operation with a filterExpression parameter that maps directly to DynamoDB's filter syntax, the scanner flags this as a potential data exposure vector. The spec analysis resolves $ref to identify reused schemas that might accept arbitrary DynamoDB conditions.

To run a scan yourself, use the middleBrick CLI:

middlebrick scan https://api.example.com/auth/login --output json

The resulting report will include a per-category breakdown. A high-risk finding in the "Authentication" or "Rate Limiting" categories, combined with "Data Exposure" evidence, indicates a password spraying vulnerability. The Pro plan's continuous monitoring can track this score over time and alert via Slack if the risk increases after a deployment.

Remediation must address both the DynamoDB access pattern and the API's rate-limiting controls. Never store passwords in plaintext; always use a strong, adaptive hash like bcrypt or Argon2. The following fixes are DynamoDB-specific:

1. Replace Scan with Query and Enforce Partition Key Usage

Restructure your user table so that the partition key is the username (or a canonical email). Then, use Query with a KeyConditionExpression instead of Scan. This limits the operation to a single partition and prevents full-table reads.

// Secure: Query by partition key only
const params = {
  TableName: 'Users',
  KeyConditionExpression: 'username = :u',
  ExpressionAttributeValues: {
    ':u': { S: username }
  }
};
dynamoDB.query(params, (err, data) => {
  if (data.Items.length === 0) {
    return res.status(401).json({ error: 'Invalid credentials' });
  }
  const user = data.Items[0];
  // Verify password hash with bcrypt.compare()
});

This pattern eliminates the need for a password filter in DynamoDB, moving credential verification to application code where timing attacks can be mitigated (e.g., using constant-time comparison functions).

2. Apply Fine-Grained IAM Policies

Restrict the IAM role used by your application to only allow dynamodb:Query on the Users table with a specific partition key condition. Deny Scan operations entirely. Example IAM policy snippet:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "dynamodb:Query",
      "Resource": "arn:aws:dynamodb:*:*:table/Users",
      "Condition": {
        "ForAllValues:StringEquals": {
          "dynamodb:LeadingKeys": ["${aws:PrincipalTag/username}"]
        }
      }
    },
    {
      "Effect": "Deny",
      "Action": "dynamodb:Scan",
      "Resource": "arn:aws:dynamodb:*:*:table/Users"
    }
  ]
}

This policy ensures the application can only query items where the partition key matches the requester's assigned tag (enforced via IAM conditions).

3. Implement Rate Limiting at the API Layer

Use API Gateway's throttling settings or a middleware like express-rate-limit to restrict authentication attempts. For API Gateway, set a burst limit of 10 and a rate limit of 5 per IP for the /auth/login route. In Express.js:

const rateLimit = require('express-rate-limit');
const authLimiter = rateLimit({
  windowMs: 15 * 60 * 1000, // 15 minutes
  max: 5, // limit each IP to 5 requests per windowMs
  message: 'Too many login attempts, please try again later'
});
app.post('/auth/login', authLimiter, loginHandler);

4. Enable DynamoDB Encryption and Audit Logs

Encryption at rest (AWS-managed or KMS) protects data if the table is compromised, but does not prevent spraying. However, enabling DynamoDB Streams and CloudTrail logging allows you to detect abnormal query patterns (e.g., a spike in Scan calls) and trigger alerts. Combine this with middleBrick's Pro plan to correlate security scores with operational metrics.

Finally, integrate these checks into your CI/CD pipeline using the middleBrick GitHub Action. Configure it to fail a pull request if the authentication score drops below a threshold (e.g., B grade), ensuring regressions are caught before deployment.