Sandbox Escape in Dynamodb

Amazon DynamoDB is a serverless NoSQL database that, by default, operates within a secure AWS-managed environment. However, sandbox escape vulnerabilities in DynamoDB contexts typically emerge through misconfigured IAM roles, overly permissive policies, or when DynamoDB is used as a data source for applications with insufficient input validation.

The most common DynamoDB sandbox escape pattern involves DynamoDB's PartiQL query language. When applications construct PartiQL queries using string concatenation with user input, attackers can escape the intended query context. For example, an application might validate that a user-provided ID is numeric, but fail to escape it in a query context:

const userId = req.params.userId; // User provides: 123 OR 1=1
const query = `SELECT * FROM "Users" WHERE id = ${userId}`;
const result = await dynamodbClient.send(new ExecuteStatementCommand({ Statement: query }));

This allows an attacker to retrieve all records instead of a single user's data. The escape occurs because the numeric validation passes, but the query context is not properly escaped.

Another DynamoDB-specific sandbox escape involves DynamoDB's PartiQL support for nested collections and attribute access. When applications fail to properly validate or escape attribute names in dynamic queries, attackers can access unintended data structures:

const attribute = req.query.attribute; // User provides: credentials.#token
const query = `SELECT ${attribute} FROM "Users" WHERE id = :id`;
const result = await dynamodbClient.send(new ExecuteStatementCommand({ Statement: query, Parameters: { ':id': userId } }));

This could allow access to sensitive attributes like credentials.#token if the application logic assumes the attribute name is safe.

DynamoDB's attribute-based access control (ABAC) can also create sandbox escape opportunities. When IAM policies use dynamic principal elements or conditions based on user attributes, insufficient validation can allow privilege escalation:

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

If the application allows username manipulation (e.g., through registration or profile updates), an attacker could potentially write to another user's table by crafting a username that resolves to an existing table name.

Server-side request forgery (SSRF) attacks targeting DynamoDB endpoints represent another sandbox escape vector. Applications that construct DynamoDB requests based on user input without proper validation can be manipulated to access unintended resources:

# Vulnerable code
region = request.args.get('region', 'us-east-1')
table_name = request.args.get('table', 'default-table')
endpoint = f"https://dynamodb.{region}.amazonaws.com"
client = boto3.client('dynamodb', region_name=region, endpoint_url=endpoint)

An attacker could manipulate the region parameter to target internal AWS services or bypass network controls.

Detecting sandbox escape vulnerabilities in DynamoDB environments requires a combination of static analysis, dynamic testing, and runtime monitoring. The detection approach must account for DynamoDB's unique query language (PartiQL), IAM integration, and serverless architecture.

Static code analysis should focus on identifying unsafe query construction patterns. Look for:

• String concatenation or template literals used to build PartiQL queries
• Direct use of user input in attribute names or table names
• Missing parameterization in ExecuteStatementCommand calls
• Dynamic IAM policy construction based on user input
• Unvalidated region or endpoint parameters in DynamoDB client configuration

Dynamic testing should include:

# Test for basic injection
curl -X POST "https://api.example.com/items" \
  -H "Content-Type: application/json" \
  -d '{"id": "1 OR 1=1", "data": "test"}'

Monitor for successful queries that return unexpected result sets or access patterns that deviate from normal user behavior.

middleBrick's DynamoDB-specific scanning capabilities include:

middleBrick's CLI tool can scan DynamoDB endpoints with:

middlebrick scan https://dynamodb.us-east-1.amazonaws.com \
  --target dynamodb \
  --output json

For applications using the AWS SDK, middleBrick's GitHub Action can be configured to scan before deployment:

- name: Scan DynamoDB API Security
  uses: middleBrick/middleBrick@v1
  with:
    target: dynamodb
    fail-on-score-below: 80
    output: json

Runtime monitoring should include CloudTrail integration to detect anomalous DynamoDB access patterns, such as:

• Unusual query volumes from specific principals
• Access to tables outside normal usage patterns
• Query patterns suggesting data exfiltration attempts
• Unusual PartiQL query structures

Remediating sandbox escape vulnerabilities in DynamoDB requires a defense-in-depth approach that combines secure coding practices, proper IAM configuration, and runtime protections. The following strategies address DynamoDB-specific attack vectors.

Query Parameterization

Always use parameterized queries instead of string concatenation. DynamoDB's ExecuteStatementCommand supports parameter binding:

// Vulnerable
const query = `SELECT * FROM "Users" WHERE id = ${userId}`;

// Secure
const query = 'SELECT * FROM "Users" WHERE id = :id';
const params = { ':id': userId };
const result = await dynamodbClient.send(new ExecuteStatementCommand({ Statement: query, Parameters: params }));

Input Validation and Whitelisting

Implement strict validation for all user inputs that affect DynamoDB operations:

def validate_dynamodb_input(value, input_type):
    if input_type == 'id':
        if not re.match(r'^[a-zA-Z0-9_-]{1,255}$', value):
            raise ValueError('Invalid ID format')
    elif input_type == 'table_name':
        if not re.match(r'^[a-zA-Z0-9_]{3,255}$', value):
            raise ValueError('Invalid table name')
    elif input_type == 'region':
        allowed_regions = ['us-east-1', 'us-west-2', 'eu-west-1']
        if value not in allowed_regions:
            raise ValueError('Invalid region')
    return value

Attribute Name Protection

Never use user input directly as attribute names. Instead, use a whitelist approach:

const allowedAttributes = new Set(['name', 'email', 'created_at']);
const attribute = req.query.attribute;
if (!allowedAttributes.has(attribute)) {
    throw new Error('Invalid attribute');
}
const query = `SELECT ${dynamo.escapeName(attribute)} FROM "Users" WHERE id = :id`;

Secure IAM Configuration

Implement least-privilege IAM policies with explicit resource ARNs:

{
  "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/users"
    }
  ]
}

Endpoint Validation

Validate and restrict DynamoDB endpoints to prevent SSRF:

from urllib.parse import urlparse

def validate_dynamodb_endpoint(endpoint):
    parsed = urlparse(endpoint)
    if not parsed.netloc.endswith('.amazonaws.com'):
        raise ValueError('Invalid endpoint')
    if not parsed.scheme == 'https':
        raise ValueError('Only HTTPS allowed')
    return endpoint

# Usage
endpoint = validate_dynamodb_endpoint(f"https://dynamodb.{region}.amazonaws.com")
client = boto3.client('dynamodb', region_name=region, endpoint_url=endpoint)

Runtime Protection

Implement query monitoring and anomaly detection:

class DynamoDBMonitor {
    constructor() {
        this.queryHistory = new Map();
        this.anomalyThreshold = 100; // queries per minute
    }

    async monitorQuery(query, params, userId) {
        const now = Date.now();
        const userKey = `${userId}:${now}`;
        
        // Log query
        this.queryHistory.set(userKey, { query, params, timestamp: now });
        
        // Cleanup old entries
        for (const [key, value] of this.queryHistory.entries()) {
            if (now - value.timestamp > 60000) {
                this.queryHistory.delete(key);
            }
        }
        
        // Check for anomalies
        const userQueries = Array.from(this.queryHistory.values())
            .filter(q => q.userId === userId)
            .length;
            
        if (userQueries > this.anomalyThreshold) {
            console.warn(`Suspicious activity from user ${userId}`);
            // Trigger alert or additional verification
        }
    }
}