Stack Overflow in Adonisjs with Jwt Tokens

When AdonisJS applications use JWT tokens for authentication without adequate safeguards, they can contribute to a Stack Overflow risk by allowing an attacker to trigger repeated, resource-intensive token validation or user lookup operations. This typically occurs when token verification logic is called on every request and is coupled with unbounded or inefficient database queries, such as fetching user records or permissions on each authentication cycle. In an unauthenticated scan, middleBrick tests for Authentication weaknesses and BOLA/IDOR patterns; if the API endpoints protected by JWT tokens also expose behavior that grows with user or token count, the service may become susceptible to resource exhaustion that can manifest as stack-related errors or degraded availability.

For example, consider an endpoint that decodes a JWT and then eagerly loads relationships or performs multiple database calls to validate scopes. An attacker who sends many crafted tokens can induce repeated, expensive operations, effectively amplifying the impact of weak token handling. middleBrick’s checks for Unsafe Consumption and Property Authorization highlight cases where authorization decisions rely on incomplete or costly data access patterns. Even though JWTs are stateless, the backend must still reconcile tokens with dynamic data, and if that reconciliation is unbounded, it contributes to a Stack Overflow–type risk profile.

middleBrick’s LLM/AI Security checks are relevant here because prompt injection and system prompt leakage tests verify whether token handling logic inadvertently exposes stack traces or framework internals in error responses. When a JWT validation failure results in verbose exceptions, attackers can learn about internal structures, making it easier to design payloads that exacerbate stack-related issues. The scanner’s Authentication and BFLA/Privilege Escalation checks ensure that token-based routes do not allow privilege escalation or unsafe consumption patterns that could magnify stack pressure under malformed or malicious input.

To reduce Stack Overflow risk when using JWT tokens in AdonisJS, ensure token validation is efficient, bounded, and fails safely without triggering heavy or recursive operations. Use AdonisJS provider utilities to verify signatures and decode payloads without performing repeated or chained database queries during authentication. Structure your guards so that token parsing is lightweight and authorization checks are performed only after essential user data is already available in a controlled context.

Example: Secure JWT verification and route guard in AdonisJS

import { HttpContextContract } from '@ioc:Adonis/Core/HttpContext'
import { schema } from '@ioc:Adonis/Core/Validator'
import { createTokens, verifyToken } from '@ioc:Adonis/Addons/Auth'

export default class AuthController {
  public async login({ auth, request, response }: HttpContextContract) {
    const email = request.validated('email')
    const password = request.validated('password')

    const user = await User.findBy('email', email)
    if (!user || !(await verifyPassword(password, user.password))) {
      return response.unauthorized()
    }

    const { accessToken, refreshToken } = createTokens(user)
    return response.ok({ accessToken, refreshToken })
  }

  public async profile({ auth, response }: HttpContextContract) {
    const user = auth.user!
    return response.ok(user.serialize())
  }
}

In this example, createTokens issues signed JWTs, and verifyToken performs signature checks without additional database work. The profile route relies on auth.user, which should be resolved once—typically via a provider or middleware that attaches the user to the context—and then reused safely. This avoids repeated token decoding and user lookups that can contribute to stack growth under high or malicious load.

Example: Configuring JWT provider to minimize stack pressure

import { HttpContextContract } from '@ioc:Adonis/Core/HttpContext'
import { exceptions } from '@poppinss/utils'

export class JwtProvider {
  public async handle(ctx: HttpContextContract, next: () => Promise) {
    const authHeader = ctx.request.header('authorization')
    if (!authHeader?.startsWith('Bearer ')) {
      return ctx.response.unauthorized()
    }

    const token = authHeader.split(' ')[1]
    try {
      const payload = verifyToken(token) // bounded, constant-time verification
      ctx.auth.user = await this.getUserById(payload.sub) // single, indexed lookup
      await next()
    } catch (error) {
      // Avoid exposing stack details in production
      ctx.response.status(401).send({ error: 'Unauthorized' })
    }
  }

  private async getUserById(userId: string) {
    // Ensure this query uses indexed fields and does not trigger unbounded joins
    return User.findOrFail(userId)
  }
}

Here, verifyToken is expected to be a bounded operation that checks signature integrity without recursive parsing. The getUserById method performs a single indexed lookup, avoiding N+1 queries or recursive relationship loading that could increase stack depth. middleBrick’s CLI can validate that such patterns are followed by scanning endpoints and flagging excessive data exposure or inefficient authorization logic.

Operational guidance

• Keep token validation stateless and avoid per-request database joins that scale with user or token size.
• Use middleware to resolve user context once per request and reuse it across guards.
• Ensure error handlers do not leak stack traces when JWT verification fails.

By combining efficient JWT handling with middleware-based user resolution, you reduce the conditions that can lead to stack-related failures and maintain a predictable runtime profile.