Type Confusion in Adonisjs with Basic Auth

Type confusion in AdonisJS when combined with Basic Authentication can occur when the framework or application code misinterprets the type of a value derived from authentication inputs or user-supplied data. This typically arises when numeric identifiers, strings, or objects are handled inconsistently across layers, such as route parameters, request headers, or parsed authentication payloads. In AdonisJS, which relies on structured request handling and schema validation, a type confusion vulnerability may manifest if values extracted from Basic Auth credentials are implicitly cast or merged without strict type checks.

Consider an endpoint that accepts a user identifier via a route parameter and merges it with authenticated user data from Basic Auth. If the route parameter is parsed as a string but later compared or assigned to a numeric database ID field without validation, an attacker may manipulate the type to bypass ownership checks or elevate privileges. For example, an IDOR-prone route might look like:

Route.get('/users/:id/profile', async ({ params, auth }) => {
  const user = await User.findOrFail(params.id);
  return user;
});

If params.id is a string like "1" but the underlying ORM or comparison logic treats it loosely as a number, an attacker could supply ?id=1 OR 1=1 or exploit inconsistent serialization to access another user’s profile. When Basic Auth is involved, the risk compounds if the authenticated identity is stored in a loosely typed session or request context and later used in queries or policies without reconciliation of types.

Another scenario involves deserialization of authentication metadata. AdonisJS may parse Basic Auth headers into a user object, but if the application merges this object with query or body inputs that contain polymorphic fields (e.g., a field that can be either an ID or a reference object), type confusion can allow an attacker to inject unexpected structures. For instance, a request might include a JSON payload with a field that switches behavior based on its runtime type:

{
  "target": { "id": 123 },
  "targetType": "user"
}
{
  "target": { "id": "../config" },
  "targetType": "file"
}

If the server validates only the presence of targetType without enforcing strict schema types, it may inadvertently process a file path when targetType is a string but expected to be an enumerated literal. MiddleBrick’s LLM/AI Security checks highlight such patterns by detecting anomalies in how agent-like structures or tool calls are handled, which can be relevant when API behaviors shift based on type-driven logic.

Middleware and policy implementations that rely on loosely typed role or permission fields are also susceptible. If a user role fetched after Basic Auth is a string but compared against constants expecting a specific symbol or numeric flag, an attacker might supply a crafted string that evaluates unexpectedly due to JavaScript type coercion rules. This can lead to unauthorized access if the framework does not enforce strict equality checks or schema-bound validation on authenticated attributes.

To mitigate type confusion in AdonisJS with Basic Authentication, enforce strict input validation and typed handling of all authentication-derived data. Begin by validating the Basic Auth credentials using AdonisJS’s built-in schema checks, ensuring that usernames and passwords are treated as strings and that any derived user metadata conforms to expected types.

Use Joi or AdonisJS schema validation to explicitly define types for incoming data. For example, when extracting a user ID from route parameters, validate it as an integer before using it in database queries:

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

const userProfileSchema = schema.create({
  id: schema.number([() => 'normalize', 'trim']),
})

export default class UserController {
  public async show({ params, request }) {
    const payload = await request.validate({ schema: userProfileSchema })
    const user = await User.findOrFail(payload.id)
    return user
  }
}

When working with Basic Auth, explicitly parse and type the authenticated user object rather than relying on implicit context merging. Configure the auth provider to return a consistent user structure:

import { BaseAuthProvider } from '@ioc:Adonis/Addons/Auth'

export default class CustomAuthProvider extends BaseAuthProvider {
  protected async getUser(payload: any) {
    if (!payload || typeof payload !== 'object') return null
    const id = Number(payload.id)
    if (Number.isNaN(id)) return null
    return User.find(id)
  }
}

This ensures that IDs are always numbers and prevents string-based coercion attacks. Avoid merging authenticated user data with untrusted inputs without re-validation. Instead, treat Basic Auth as a source of identity and separately validate all other inputs.

Apply strict equality and type guards in policies and middleware. For instance, when checking ownership, compare IDs using === after confirming both sides are numbers:

const userId = Number(auth.user?.id)
const targetId = Number(params.id)

if (!Number.isSafeInteger(userId) || !Number.isSafeInteger(targetId)) {
  throw new Error('Invalid identifier type')
}

if (userId !== targetId) {
  throw new UnauthorizedException('Access denied')
}

Finally, combine these practices with continuous scanning using tools like MiddleBrick. The CLI tool (middlebrick scan <url>) can detect type confusion patterns in unauthenticated attack surfaces, while the Pro plan’s GitHub Action integration can fail builds if risky type-handling patterns are detected in API specifications or runtime behavior.