Injection Flaws in Loopback with Dynamodb

Loopback is a popular Node.js framework for building APIs quickly, and it often integrates with AWS DynamoDB as a persistence store. When user-controlled input is used to construct DynamoDB operations without strict validation or parameterization, injection-style flaws can occur. Although DynamoDB does not support SQL, injection in this context refers to unsafe construction of API parameters, malformed condition expressions, or misuse of the SDK that allows unintended data access or behavior.

In a Loopback application, models are typically defined with Loopback datasource connectors that wrap the AWS SDK. If a developer builds query filters or key conditions by concatenating user input into objects or expression strings, the unvalidated input can affect which items are retrieved or modified. For example, a path traversal or unexpected attribute name could lead to reading or overwriting items outside the intended scope, effectively an Insecure Direct Object Reference (IDOR) or Broken Object Level Authorization (BOLA) pattern that is exacerbated by DynamoDB’s flat key schema.

Real-world risk patterns include constructing a KeyConditionExpression or FilterExpression by embedding user input directly into strings. If the input includes comparison operators or logical constructs, it may change the semantics of the query. Consider an endpoint that retrieves a user’s profile by userId provided as a URL parameter:

app.models.User.find({ where: { userId: req.query.userId } });

If the connector builds a low-level DynamoDB GetItem or Query call without strict schema checks, an attacker could supply a value like "userId": {"S": "123"} or exploit reserved keywords to cause unexpected behavior. More critically, insufficient validation of sort keys can allow vertical privilege escalation when an attacker manipulates the range key to access other users’ records, a BOLA/IDOR vector.

Another injection surface is the use of ExpressionAttributeNames and ExpressionAttributeValues. If these are constructed dynamically from user input without normalization, an attacker may inject malicious placeholder names or values that overwrite intended attributes. For example, an attacker could provide an attribute name that maps to a sensitive system attribute, or a value that changes the intent of a ConditionExpression in a PutItem or UpdateItem call.

Because middleBrick tests the unauthenticated attack surface and checks properties like Input Validation and Property Authorization, it can surface these risks when query parameters, headers, or body fields enable unsafe DynamoDB operations. The scanner does not modify the API; it identifies where user-controlled data reaches critical operations such as Query, Scan, GetItem, and UpdateItem, and reports findings with severity and remediation guidance.

Compliance mappings such as OWASP API Top 10 (2023) A1: Injection and A7: Identification and Authentication Failures are relevant here. These findings also align with SOC2 and GDPR considerations around unauthorized data access. middleBrick’s per-category breakdowns help teams understand how DynamoDB-specific logic contributes to the overall risk score.

To secure Loopback integrations with DynamoDB, validate and sanitize all inputs that form part of DynamoDB requests. Use strong schema validation for model properties and avoid building expression strings from concatenated user input. Prefer typed parameters and reserved keyword handling to reduce injection risk.

1. Validate and type-check inputs

Ensure incoming IDs and keys conform to expected formats and types before they reach the datasource. For string IDs, enforce length and character rules. For numeric keys, parse and validate explicitly.

const userId = req.query.userId;
if (typeof userId !== 'string' || !/^[a-zA-Z0-9_-]{1,64}$/.test(userId)) {
  throw new Error('Invalid userId');
}

2. Use ExpressionAttributeNames and ExpressionAttributeValues safely

When using expressions, map placeholder names to attribute names via ExpressionAttributeNames and values via ExpressionAttributeValues. Do not allow user input to dictate placeholder keys directly. Instead, normalize attribute names and use a controlled mapping.

const attributeNameMap = {
  'owner': 'ownerId',
  'status': 'recordStatus'
};
const safeAttr = attributeNameMap[userSuppliedKey];
if (!safeAttr) {
  throw new Error('Invalid attribute key');
}
const params = {
  TableName: 'UserRecords',
  Key: {
    userId: { S: userId },
    sortKey: { S: req.body.sortKey }
  },
  UpdateExpression: `SET #${safeAttr} = :val`,
  ExpressionAttributeNames: { '#': safeAttr },
  ExpressionAttributeValues: { ':val': { S: req.body.status } }
};
dynamodb.update(params, (err, data) => { /* handle */ });

3. Avoid dynamic key condition expressions

Do not construct KeyConditionExpression from raw user strings. Use a whitelist of allowed partition and sort key conditions, and map them to known safe patterns.

const allowedIndex = new Set(['createdAt', 'updatedAt']);
const sortKey = req.query.sortKey;
if (!allowedIndex.has(sortKey)) {
  throw new Error('Unsupported sort key');
}
const params = {
  TableName: 'UserRecords',
  KeyConditionExpression: 'userId = :uid AND ' + sortKey + ' BETWEEN :start AND :end',
  ExpressionAttributeValues: {
    ':uid': { S: userId },
    ':start': { S: '2024-01-01T00:00:00Z' },
    ':end': { S: '2024-12-31T23:59:59Z' }
  }
};

4. Prefer SDK condition operators over string building

When updating items, use the AWS SDK’s condition operators programmatically instead of building ConditionExpression from user input. This prevents injection of logical operators that alter intent.

const AWS = require('aws-sdk');
const dynamodb = new AWS.DynamoDB.DocumentClient();
const params = {
  TableName: 'UserRecords',
  Key: {
    userId: userId,
    sortKey: 'meta'
  },
  Update: {
    Action: 'PUT',
    Value: { status: 'active' }
  },
  ConditionExpression: 'attribute_exists(userId) AND begins_with(sortKey, :prefix)',
  ExpressionAttributeValues: {
    ':prefix': 'meta'
  }
};
dynamodb.update(params, function(err, data) {
  if (err) console.error(err);
  else console.log(data);
});

5. Enforce model-level scoping in Loopback

Leverage Loopback’s built-in ACLs and role-based access controls to restrict which users can query or modify items. Combine this with DynamoDB fine-grained permissions to ensure that even if an injection-like flaw is attempted, the backend enforces boundaries.

// In model JSON ACLs
"acls": [
  {
    "accessType": "EXECUTE",
    "principalType": "ROLE",
    "principalId": "$authenticated",
    "permission": "ALLOW",
    "property": "find"
  }
]

6. Use middleBrick to validate your endpoints

Run middleBrick scans to surface DynamoDB-related input validation and property authorization issues. The scanner checks unauthenticated attack surfaces and maps findings to frameworks such as OWASP API Top 10. For continuous coverage, use the Pro plan to enable ongoing monitoring and CI/CD integration with the GitHub Action, which can fail builds when risk thresholds are exceeded.