Formula Injection in Adonisjs with Dynamodb

Formula Injection occurs when untrusted data is interpreted as a formula or expression by downstream systems, spreadsheets, or parsers. In an AdonisJS application that stores and later renders user input into formats consumed by spreadsheet software or internal parsers, unsanitized input can lead to unintended execution paths or data leakage. When that data is persisted in DynamoDB and later retrieved without strict validation or output encoding, the risk shifts from direct code execution to logic manipulation and data integrity issues.

DynamoDB itself does not evaluate formulas, but the combination of AdonisJS route handling, Eloquent-like ORM usage (via Lucid), and downstream export or integration layers can create a chain of trust issues. For example, if an application accepts a formula field via request parameters, stores it directly in a DynamoDB table using the AWS SDK or an ODM layer, and later includes that value in CSV or spreadsheet exports, the formula may be interpreted by the receiving application. Attackers can inject payloads such as =cmd|' /C calc'!A0 or =HYPERLINK("http://malicious.example.com") to trigger unwanted behavior in downstream viewers.

In AdonisJS, routes and controllers often map request payloads directly to database records. If input sanitization is omitted, a request like POST /records { "name": "Test", "formula": "=1+1" } may be stored as-is in DynamoDB. Later, when an admin dashboard or export feature reads the item from DynamoDB and renders it in a web view or spreadsheet, the formula may be evaluated depending on the client application. This is especially dangerous when combined with weak access controls, as BOLA or IDOR flaws may allow unauthorized users to craft malicious entries that affect other consumers of the data.

The DynamoDB data model stores data as attribute-value pairs with no inherent type enforcement for formulas. This flexibility means that an application layer must enforce strict validation. If AdonisJS validation rules do not explicitly reject or encode formula-like strings in certain contexts, the stored data remains potentially hazardous. Additionally, when using DynamoDB streams or integrations with other services, the injected formula may propagate to external systems, increasing the blast radius. The use of unauthenticated endpoints or misconfigured resource policies can further expose these stored values to broader consumption.

Real-world examples include injection via CSV export features, where a value such as =EXP(1) triggers calculation in spreadsheet software, or injection into JSON-based configuration exports that are later parsed by internal tooling. While DynamoDB does not execute these formulas, the ecosystem around it — including export pipelines, admin panels, and third-party integrations — may treat them as executable instructions. Proper encoding, context-aware output handling, and strict input validation within AdonisJS controllers are essential to break this chain.

To mitigate Formula Injection when using DynamoDB with AdonisJS, focus on input validation, output encoding, and strict schema design. Begin by defining clear validation rules in your AdonisJS controller or schema layer to reject or sanitize values that begin with characters commonly used in formulas, such as =, +, -, and @. This prevents malicious payloads from being stored in the first place.

Validation and Sanitization

Use AdonisJS schema validation to enforce constraints on fields that could be interpreted as formulas. For example, you can create a validation rule that disallows leading formula characters:

import { schema, rules } from '@ioc:Adonis/Core/Validator'

const recordSchema = schema.create({
  formula: schema.string.optional({}, [
    rules.trim(),
    rules.pattern(new RegExp('^[^"]=["]')), // Reject leading '='
    rules.maxLength(255),
  ]),
})

export default class RecordsController {
  public async store({ request, response }) {
    const payload = request.validate({ schema: recordSchema })
    // proceed to save
  }
}

Safe DynamoDB Integration

When interacting with DynamoDB, use the AWS SDK directly or an ODM layer consistently. Always treat user input as untrusted when constructing parameters. Below is a safe example of storing a record while ensuring the formula field is normalized or rejected before reaching DynamoDB:

import { DynamoDBDocumentClient, PutCommand } from '@aws-sdk/lib-dynamodb'
import { ddbClient } from 'src/utils/ddb'

export async function saveRecord(data: { name: string; formula?: string }) {
  const safeFormula = data.formula ? sanitizeFormula(data.formula) : undefined
  const params = {
    TableName: process.env.DYNAMODB_TABLE,
    Item: {
      id: `record-${Date.now()}`,
      name: data.name,
      formula: safeFormula,
    },
  }
  await ddbClient.send(new PutCommand(params))
  return params.Item
}

function sanitizeFormula(value: string): string | undefined {
  const trimmed = value.trim()
  if (/^[=\+\-@]/.test(trimmed)) {
    throw new Error('Invalid formula: potentially malicious input')
  }
  return trimmed
}

Output Encoding and Context-Aware Rendering

When retrieving items from DynamoDB for display or export, ensure that any field that could have been user-supplied is encoded based on the target context. For CSV exports, wrap values in quotes and escape existing quotes. For HTML views, use context-specific escaping utilities provided by AdonisJS or underlying templating engines.

// Example CSV export helper
export function toCSV(items: Array<{ formula?: string }>) {
  return items.map(item => {
    const formula = item.formula || ''
    const escaped = formula.includes(',') || formula.includes('=') ? `"${formula.replace(/"/g, '""')}"` : formula
    return escaped
  }).join('\n')
}

Least Privilege and Data Flow Controls

Configure DynamoDB resource policies and IAM roles to limit who can write potentially dangerous fields. If certain roles or services do not need to write formula-like data, deny those actions at the identity level. Within AdonisJS, use middleware to verify ownership or scopes before allowing updates to sensitive fields, reducing the impact of IDOR or BOLA issues that could amplify formula injection effects.

Monitoring and Logging

Log rejected inputs and validation failures related to formula fields to detect probing attempts. Correlate these logs with DynamoDB conditional writes or pre-put validation logic to ensure only safe data persists. Regularly review access patterns via DynamoDB Streams if enabled, looking for anomalous write patterns targeting formula-related attributes.