Sql Injection in Fiber with Dynamodb

SQL Injection is commonly associated with SQL databases, but similar injection concepts can manifest when user-controlled input is used to construct low-level queries or key expressions in NoSQL workflows. In a Fiber application that uses the AWS SDK for DynamoDB, risk arises when request parameters are directly interpolated into key condition expressions, filter expressions, or attribute names without validation or escaping. Because DynamoDB operations are typically built programmatically, developers may inadvertently concatenate user input into expression strings, creating injection-like paths that can bypass intended access controls or read unintended data.

Consider a route like /users/:userId/orders where the handler builds a QueryInput using raw URL parameters. If the partition key value or filter expression is constructed via string concatenation, an attacker can supply values such as userId="user123 OR attribute = 'admin'" or embed reserved words and operators that change the semantics of the request. While DynamoDB does not support classic multi-statement SQL injection, injection-style input can lead to privilege escalation via BOLA/IDOR (e.g., accessing other users’ records), data exposure through unintended filter evaluation, or bypassing rate-limiting logic encoded in application logic. These patterns are especially dangerous when combined with unauthenticated endpoints or over-privileged IAM roles, as findings from the Authentication and BOLA/IDOR checks can indicate.

middleBrick’s 12 parallel security checks identify these patterns by correlating OpenAPI/Swagger specifications (with full $ref resolution) against runtime behavior. For DynamoDB-heavy APIs, the scanner flags risky expression construction and highlights findings under Input Validation, Property Authorization, and Authentication. Because DynamoDB relies on expression syntax for filtering and key selection, improper handling of user input can map to OWASP API Top 10 A01:2023 (Broken Object Level Authorization) and A05:2023 (Security Misconfiguration). The LLM/AI Security checks further ensure that prompt injection and jailbreak probes do not leak instructions or sensitive context that could be chained with API misuse.

To prevent injection-style issues with DynamoDB in Fiber, use the AWS SDK’s built-in expression builders and parameter binding rather than string concatenation. For Query operations, supply explicit key condition expressions with placeholder values and pass expression attribute values separately. This ensures that user input is treated strictly as data, not executable expression syntax.

Example: Safe Query with Expression Attribute Values

const { DynamoDBClient, QueryCommand } = require("@aws-sdk/client-dynamodb");
const { marshall, unmarshall } = require("@aws-sdk/util-dynamodb");

const client = new DynamoDBClient({ region: "us-east-1" });

app.get("/users/:userId/orders", async (req, res) => {
  const userId = req.params.userId;
  // Validate and sanitize input before use
  if (typeof userId !== "string" || !/^[a-zA-Z0-9_-]{1,64}$/.test(userId)) {
    return res.status(400).json({ error: "Invalid userId" });
  }

  const params = {
    TableName: process.env.TABLE_NAME,
    KeyConditionExpression: "#pk = :uid",
    ExpressionAttributeNames: { "#pk": "userId" },
    ExpressionAttributeValues: marshall({ ":uid": userId }),
  };

  try {
    const command = new QueryCommand(params);
    const response = await client.send(command);
    const items = response.Items.map(unmarshall);
    res.json(items);
  } catch (err) {
    console.error(err);
    res.status(500).json({ error: "Internal server error" });
  }
});

For Scan operations or more complex filtering, use FilterExpression with placeholders and avoid injecting raw attribute names. If dynamic attribute names are required (e.g., custom metadata keys), maintain an allowlist and use ExpressionAttributeNames with strict validation.

Example: Safe Scan with FilterExpression

const { ScanCommand } = require("@aws-sdk/client-dynamodb");

app.get("/search", async (req, res) => {
  const { status } = req.query;
  const allowedStatus = new Set(["pending", "completed", "cancelled"]);
  if (!status || !allowedStatus.has(status)) {
    return res.status(400).json({ error: "Invalid status" });
  }

  const params = {
    TableName: process.env.TABLE_NAME,
    FilterExpression: "#st = :status",
    ExpressionAttributeNames: { "#st": "status" },
    ExpressionAttributeValues: marshall({ ":status": status }),
  };

  try {
    const command = new ScanCommand(params);
    const response = await client.send(command);
    const items = response.Items.map(unmarshall);
    res.json(items);
  } catch (err) {
    console.error(err);
    res.status(500).json({ error: "Internal server error" });
  }
});

Additionally, apply robust input validation, use middleware in Fiber to normalize and reject unexpected query parameters, and enforce least-privilege IAM policies so that even if injection-style input were to reach DynamoDB, the impact would be limited. middleBrick’s Pro plan supports continuous monitoring and CI/CD integration to catch regressions early, while the CLI can be used locally to validate endpoint definitions before deployment.