Broken Authentication in Dynamodb

Broken Authentication in DynamoDB environments often stems from misconfigured access controls and improper credential handling. Unlike traditional RDBMS systems with built-in authentication mechanisms, DynamoDB relies on AWS Identity and Access Management (IAM) for access control, creating unique vulnerability patterns.

The most common attack vector involves overly permissive IAM policies. Developers frequently grant wildcard access to DynamoDB tables, such as:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "dynamodb:*",
      "Resource": "*"
    }
  ]
}

This policy allows any authenticated AWS user to perform any DynamoDB operation on any table, effectively bypassing authentication at the database level. Attackers who compromise a single credential can access the entire DynamoDB environment.

Another prevalent issue is hard-coded credentials in application code. Developers sometimes embed AWS access keys directly in source code:

import boto3

# Vulnerable: hard-coded credentials
dynamodb = boto3.resource(
    'dynamodb',
    aws_access_key_id='AKIAXXXXXXXXXXXXXXXX',
    aws_secret_access_key='xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
)

When these credentials are exposed through code repositories or configuration files, attackers gain direct API access to DynamoDB without needing to compromise the application itself.

Cross-account access misconfigurations create another attack surface. Organizations sometimes grant DynamoDB access to external accounts or services without proper scoping:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "dynamodb:GetItem",
        "dynamodb:PutItem",
        "dynamodb:UpdateItem",
        "dynamodb:DeleteItem"
      ],
      "Resource": "arn:aws:dynamodb:*:*:table/*"
    }
  ]
}

This allows any AWS account to access any DynamoDB table in the organization, creating a massive authentication bypass opportunity.

Time-based token vulnerabilities also affect DynamoDB applications. When using temporary credentials from STS (Security Token Service), improper token handling can lead to authentication bypasses:

// Vulnerable: token expiration not properly checked
const sts = new AWS.STS();
const params = { DurationSeconds: 3600 };
sts.getFederationToken(params, (err, data) => {
    if (err) {
        console.error(err);
    } else {
        // Token might be expired, but code proceeds anyway
        const dynamodb = new AWS.DynamoDB({
            accessKeyId: data.Credentials.AccessKeyId,
            secretAccessKey: data.Credentials.SecretAccessKey,
            sessionToken: data.Credentials.SessionToken
        });
    }
});

These patterns create authentication gaps that attackers can exploit to access sensitive DynamoDB data without proper authorization.

Detecting Broken Authentication in DynamoDB requires examining both IAM policies and application code patterns. middleBrick's DynamoDB-specific scanning identifies these vulnerabilities through multiple approaches.

Policy analysis examines IAM permissions for over-privileged access patterns. The scanner detects wildcard permissions, overly broad resource specifications, and missing explicit deny statements. For example, middleBrick flags policies like:

{
  "Effect": "Allow",
  "Action": "dynamodb:*",
  "Resource": "arn:aws:dynamodb:*:*:table/*"
}

The scanner assigns severity based on the breadth of permissions and identifies specific tables that could be compromised.

Credential analysis searches for hard-coded AWS keys in application code and configuration files. middleBrick uses pattern matching to identify AWS access key formats and flags them for review:

# middleBrick scan output example
DynamoDB Authentication Risk: C (73/100)
- Hard-coded credentials found in src/config/aws.js
- IAM policy allows wildcard DynamoDB access
- Cross-account access enabled without restrictions

Findings:
1. Critical: Hard-coded AWS access key detected
   Location: src/config/aws.js:12
   Recommendation: Use IAM roles or environment variables

2. High: Overly permissive IAM policy
   Resource: * (all DynamoDB tables)
   Recommendation: Implement least privilege principle

The scanner also tests for credential exposure through API endpoints. By attempting authenticated requests with different credential sets, middleBrick can identify whether applications properly validate DynamoDB permissions before processing requests.

Cross-account access detection examines trust relationships and external access policies. middleBrick identifies when DynamoDB tables are accessible from unauthorized AWS accounts or when federation configurations allow broader access than intended.

For applications using DynamoDB through API Gateway, middleBrick tests whether authentication mechanisms properly validate DynamoDB permissions. This includes checking for missing IAM role validation and improper credential forwarding from client applications to DynamoDB.

The scanner's LLM/AI security module also detects when applications use DynamoDB to store or process sensitive AI model data without proper authentication controls, a growing concern as more applications integrate AI capabilities.

Remediating Broken Authentication in DynamoDB requires implementing proper IAM controls and credential management practices. The following code examples demonstrate secure DynamoDB authentication patterns.

First, implement least-privilege IAM policies that grant only necessary permissions:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "dynamodb:GetItem",
        "dynamodb:PutItem",
        "dynamodb:UpdateItem",
        "dynamodb:DeleteItem"
      ],
      "Resource": "arn:aws:dynamodb:us-east-1:123456789012:table/Orders"
    }
  ]
}

This policy restricts access to a specific table and specific operations, preventing privilege escalation through broader DynamoDB permissions.

For application code, use IAM roles instead of hard-coded credentials:

import boto3
import os

# Secure: use IAM roles or environment variables
dynamodb = boto3.resource('dynamodb')

# Or with explicit configuration using environment variables
table = dynamodb.Table(os.environ['DYNAMODB_TABLE_NAME'])

# Secure credential handling
response = table.get_item(
    Key={'id': 'order123'},
    ReturnConsumedCapacity='TOTAL'
)

This approach eliminates credential exposure in source code and leverages AWS's built-in authentication mechanisms.

Implement temporary credential rotation for applications that require short-lived access:

const AWS = require('aws-sdk');
const sts = new AWS.STS({ region: 'us-east-1' });

async function getDynamoClient() {
    const params = {
        DurationSeconds: 900, // 15 minutes
        RoleArn: process.env.DYNAMODB_ROLE_ARN,
        RoleSessionName: 'dynamo-client-session'
    };

    try {
        const data = await sts.assumeRole(params).promise();
        return new AWS.DynamoDB({
            accessKeyId: data.Credentials.AccessKeyId,
            secretAccessKey: data.Credentials.SecretAccessKey,
            sessionToken: data.Credentials.SessionToken,
            region: 'us-east-1'
        });
    } catch (error) {
        console.error('Credential rotation failed:', error);
        throw error;
    }
}

This pattern ensures credentials expire regularly, limiting the window for potential exploitation.

For cross-account access, implement explicit trust relationships with specific conditions:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "AWS": "arn:aws:iam::123456789012:root"
      },
      "Action": "sts:AssumeRole",
      "Condition": {
        "StringEquals": {
          "aws:SourceAccount": "123456789012"
        }
      }
    }
  ]
}

This restricts cross-account access to specific source accounts, preventing unauthorized external access.

Implement monitoring and alerting for authentication anomalies:

import boto3
from datetime import datetime, timedelta

cloudwatch = boto3.client('cloudwatch')

# Monitor for unusual DynamoDB access patterns
def monitor_dynamodb_access():
    # Check for multiple failed access attempts
    # Alert on unusual access times or locations
    # Monitor for privilege escalation attempts
    pass

# Log all DynamoDB access attempts for audit trail
logging.basicConfig(level=logging.INFO)
logging.info(f"DynamoDB access attempt at {datetime.now()}")

These remediation patterns significantly reduce Broken Authentication risks in DynamoDB environments by implementing proper access controls and credential management.