Hallucination Attacks in Adonisjs with Dynamodb

Hallucination attacks in an AdonisJS application using DynamoDB occur when an attacker manipulates inputs or API interactions to produce incorrect, fabricated, or misleading responses from downstream data sources. Because AdonisJS often serves as a backend layer that queries DynamoDB and returns results to clients, gaps in validation, error handling, or schema assumptions can be leveraged to generate or amplify hallucinations.

DynamoDB’s schema-less design and flexible attribute types mean that application code must explicitly validate and coerce data. If AdonisJS routes or services assume a particular attribute exists or matches a specific type, an attacker can exploit missing checks by sending crafted payloads (e.g., missing keys, unexpected types, or nested objects) that cause the app to construct invalid queries or misinterpret responses. This can result in the application fabricating data to fill missing fields, misattributing ownership due to incorrect filtering, or exposing sensitive information through verbose error messages.

The risk is compounded when DynamoDB queries are built dynamically without strict whitelisting of field names or when conditional operators are derived from user input. For example, an attacker may provide a malformed sort key or partition key value that leads to inconsistent pagination or incomplete scans, prompting the AdonisJS layer to synthesize plausible but incorrect results to the client. Additionally, if the application merges partial results from multiple DynamoDB calls without strict reconciliation, inconsistencies across items can manifest as hallucinated relationships or attributes.

LLM/AI Security checks are particularly relevant here because hallucination attacks can also target AI features embedded in AdonisJS services. If the app integrates language models to enrich DynamoDB data or generate summaries, unchecked model outputs may introduce fabricated facts that align poorly with the source data. This is why middleBrick’s LLM/AI Security probes—such as system prompt leakage detection, active prompt injection testing, and output scanning for PII or executable code—are valuable: they help identify whether AI components inadvertently amplify hallucinations or expose sensitive DynamoDB-derived content.

In a typical AdonisJS + DynamoDB stack, the following real-world conditions increase susceptibility:

• Over-permissive CORS or unauthenticated endpoints that allow arbitrary query parameters.
• Missing validation on DynamoDB filter expressions, leading to unintended result sets.
• Improper error mapping that returns stack traces or internal query structure to the client.
• Dynamic construction of KeyConditionExpression or FilterExpression using string concatenation without schema enforcement.

By combining targeted validation, strict schema enforcement, and automated security scans, teams can detect and mitigate hallucination attack vectors before they impact production data integrity.

Remediation focuses on strict input validation, parameterized queries, and defensive coding patterns when interacting with DynamoDB in AdonisJS. Below are concrete, working examples that demonstrate how to implement these fixes.

1. Validate and coerce input against a known schema

Use an AdonisJS schema (e.g., Yup) to enforce expected types and required fields before constructing DynamoDB queries.

const { schema } = use('@ioc:Adonis/Core/Validator')
const yup = require('yup')

const itemSchema = yup.object({
  userId: yup.string().required().matches(/^usr_/),
  timestamp: yup.number().integer().required(),
  metadata: yup.object().shape({
    tags: yup.array().of(yup.string()),
    rating: yup.number().min(1).max(5)
  }).nullable()
})

export async function validateItem(data) {
  return itemSchema.validate(data)
}

2. Use parameterized KeyConditionExpression with whitelisted field names

Avoid dynamic concatenation; instead, map allowed sort keys to safe attribute names and use ExpressionAttributeNames.

const allowedSortKeys = new Set(['createdAt', 'updatedAt', 'score'])

export function buildKeyCondition(partitionKeyVal, sortKey, sortValue) {
  if (!allowedSortKeys.has(sortKey)) {
    throw new Error('Invalid sort key')
  }
  return {
    KeyConditionExpression: '#pk = :pk AND #sk = :sk',
    ExpressionAttributeNames: {
      '#pk': 'pk',
      '#sk': sortKey
    },
    ExpressionAttributeValues: {
      ':pk': { S: partitionKeyVal },
      ':sk': { S: String(sortValue) }
    }
  }
}

3. Apply FilterExpression safely with type checking

When filtering, validate filter criteria against an allowlist and use ExpressionAttributeNames to prevent injection.

const allowedFilterAttrs = new Set(['status', 'category', 'priority'])

export function buildFilter(filters) {
  const filterClauses = []
  const attributeNames = {}
  const attributeValues = {}

  let index = 0
  for (const [key, value] of Object.entries(filters)) {
    if (!allowedFilterAttrs.has(key)) continue
    index += 1
    const name = `#attr${index}`
    const val = `:val${index}`
    filterClauses.push(`${name} = ${val}`)
    attributeNames[name] = key
    attributeValues[val] = { S: String(value) }
  }

  const expression = filterClause.length > 0 ? filterClauses.join(' AND ') : undefined
  return expression
    ? {
        FilterExpression: expression,
        ExpressionAttributeNames: attributeNames,
        ExpressionAttributeValues: attributeValues
      }
    : {}
}

4. Handle DynamoDB responses defensively

Always check for missing or malformed attributes and avoid propagating incomplete data as complete.

export function safeItemFromDynamo(item) {
  if (!item || !item.pk?.S || !item.createdAt?.N) {
    return null
  }
  return {
    id: item.pk.S,
    createdAt: Number(item.createdAt.N),
    tags: item.tags?.L?.map(e => e.S) || [],
    rating: item.rating?.N ? Number(item.rating.N) : null
  }
}

5. Integrate validation into AdonisJS controller actions

Validate before querying and return consistent error shapes to avoid leaking internals.

import { HttpContextContract } from '@ioc:Adonis/Core/HttpContext'
import { validateItem } from 'App/Validators/ItemValidator'

export default class ItemsController {
  public async show({ request, response }: HttpContextContract) {
    const input = request.only(['userId', 'timestamp'])

    const validation = await validateItem(input)
    if (validation.error) {
      return response.badRequest({ error: 'Invalid input', details: validation.error.message })
    }

    const keyCondition = buildKeyCondition(
      validation.data.userId,
      validation.data.sortKey || 'createdAt',
      validation.data.timestamp
    )

    // Assume dynamoDbQuery is a safe wrapper returning items or []
    const items = await dynamoDbQuery(keyCondition)
    const safeItems = items.map(safeItemFromDynamo).filter(Boolean)

    return response.ok(safeItems)
  }
}

6. Leverage middleBrick for ongoing security checks

Use the CLI to scan endpoints and DynamoDB interaction patterns, and integrate the GitHub Action to fail builds if risk scores degrade. The MCP Server can help AI coding assistants surface risky query constructions during development.