Injection Flaws in Adonisjs with Dynamodb

AdonisJS is a Node.js web framework that encourages structured query building and input validation. When integrating with DynamoDB, injection risks shift from traditional SQL patterns to malformed API usage and unsafe data handling. DynamoDB is a schemaless NoSQL service, so injection-related issues typically manifest as unexpected query behavior, inefficient scans, or exposure of unintended data through malformed key conditions or expression attribute values.

In an AdonisJS application, developers often use raw DynamoDB API calls or an OGM-like layer. If user input is directly interpolated into request parameters, the unauthenticated attack surface includes endpoint manipulation and data leakage. For example, concatenating user-supplied strings into a KeyConditionExpression can cause the query to scan a broader partition than intended, effectively exposing other users’ data (a variant of Insecure Direct Object Reference). The framework’s request lifecycle, including middleware validation, does not automatically sanitize inputs for DynamoDB expression syntax, making it necessary to treat all inputs as untrusted.

Another injection-adjacent risk in this combination involves expression attribute values. Failing to use the : placeholder pattern and instead injecting values directly into expression strings can lead to malformed JSON parameter payloads sent to the DynamoDB service. While this does not produce classic SQL-style injection, it can cause unpredictable query results or errors that reveal internal schema details in response messages. Middleware that only checks for presence or type, but not semantic correctness of DynamoDB expressions, may pass unsafe data to the service.

LLM endpoints that are unintentionally exposed can also become a vector when an application stores or logs raw user prompts containing DynamoDB-formatted strings. If output scanning is not enabled, crafted inputs might attempt to elicit repeated or verbose service responses, increasing cost exposure or causing information leakage through error messages. The LLM/AI Security checks in middleBrick specifically probe for such patterns by testing how endpoints handle adversarial inputs designed to trigger misuse.

To detect these risks in practice, scanning unauthenticated endpoints with tools that understand DynamoDB expression patterns is valuable. A scan can surface missing input validation around partition key and sort key usage, over-permissive condition expressions, and missing authorization checks on read paths. These findings do not imply that DynamoDB is insecure by design, but that application-level usage must enforce strict schema-aware validation and avoid dynamic construction of expressions.

Remediation centers on strict input validation, using parameterized expressions, and isolating service calls behind controlled entry points. Always prefer the placeholder syntax provided by the AWS SDK for JavaScript, and never directly interpolate user input into expression strings.

import { DynamoDB } from "@aws-sdk/client-dynamodb";
import { marshall, unmarshall } from "@aws-sdk/util-dynamodb";

const db = new DynamoDB({ region: "us-east-1" });

export async function getUserById(userId) {
  // Validate and sanitize input: enforce expected format, length, charset
  if (typeof userId !== "string" || !/^user_[a-f0-9]{8}$/.test(userId)) {
    throw new Error("Invalid user identifier");
  }

  const params = {
    TableName: process.env.DYNAMODB_TABLE,
    Key: marshall({ pk: `USER#${userId}`, sk: `PROFILE` }),
    ExpressionAttributeNames: { "#meta": "meta" },
    ProjectionExpression: "#meta :email",
    ExpressionAttributeValues: marshall({ ":email": { S: "email" } }),
  };

  const command = new GetCommand(params);
  const response = await db.send(command);
  return response.Item ? unmarshall(response.Item) : null;
}

Key remediation practices include:

• Validate partition key and sort key formats against an allowlist before constructing key conditions.
• Use ExpressionAttributeNames for reserved keywords and ExpressionAttributeValues for all dynamic values.
• Avoid building FilterExpression or ConditionExpression via string concatenation; always use placeholders.
• Set reasonable result size limits and use paginators to prevent excessive data retrieval.
• Instrument request tracing to correlate incoming identifiers with DynamoDB key schemas, ensuring that access patterns align with authorization rules.

In AdonisJS, encapsulate DynamoDB interactions within services that enforce these rules centrally. This reduces the chance of inconsistent usage across controllers and makes it easier to apply continuous monitoring via the Pro plan, which can track scans over time and alert on deviations.