Memory Leak in Adonisjs with Api Keys

A memory leak in an AdonisJS application that exposes API keys typically arises when key material is retained in long-lived objects or closures and is never released. Unlike a crash, a leak gradually increases memory consumption, which can lead to higher latency, process restarts, and in extreme cases, denial of service. The issue is not the key itself, but how the application stores, caches, and references key values across requests.

Consider an AdonisJS service that caches external API keys in an in-memory map keyed by route or user identifier without eviction logic. If each incoming request creates a new cache entry and nothing removes stale entries, the heap grows continuously. This pattern is common when developers store keys as plain properties on request-scoped objects or attach them to global state for convenience. Because AdonisJS runs on Node.js, the garbage collector will not reclaim objects that remain reachable; a lingering map or closure holding key strings keeps those objects alive indefinitely.

Furthermore, middleware that reads API keys from headers or environment variables and attaches them to the request context can inadvertently create references that persist beyond the request lifecycle. If the context is shared across requests (for example, via a singleton service that accumulates key metadata), each request adds more data that the GC cannot collect. Over time, this leads to a steady increase in RSS memory, slower event-loop processing, and potential timeouts. From a security perspective, leaked keys in memory increase the attack surface: a memory dump could expose credentials, and prolonged runtime may amplify the impact of any other vulnerability that enables code execution.

In the context of middleBrick’s checks, a memory leak related to API keys would surface under the Data Exposure and Unsafe Consumption categories. The scanner does not measure heap size directly, but it tests how keys are handled across unauthenticated endpoints and flags patterns where sensitive values appear in logs, error messages, or uncontrolled caches. Because the scan runs black-box and analyzes OpenAPI specs alongside runtime behavior, it can detect indicators such as missing rate limiting on key-introspection endpoints or inconsistent authorization that might allow an attacker to probe key storage indirectly.

Real-world analogies include CVE-class patterns where unbounded caches or missing cleanup in server-side SDKs lead to resource exhaustion. Remediation focuses on limiting the lifetime of key references, using scoped storage, and ensuring that sensitive values are not retained in mutable global structures. Proper design keeps key usage short-lived and avoids attaching credentials to long-lived objects, thereby reducing both security and stability risk.

To remediate memory leaks involving API keys in AdonisJS, refactor how keys are stored and accessed so that references are short-lived and scoped to the minimal necessary context. Avoid attaching key material to global singletons or request contexts that survive beyond the request lifecycle. Instead, use transient variables within request handlers and rely on framework-managed configuration for static values.

Example 1: Safe runtime key usage without caching

import Env from '@ioc:Adonis/Core/Env'
import { HttpContextContract } from '@ioc:Adonis/Core/HttpContext'

export default class ApiController {
  public async callExternal(ctx: HttpContextContract) {
    // Retrieve the key from environment at call time; no persistent reference is kept
    const apiKey = Env.get('EXTERNAL_API_KEY')
    const response = await fetch('https://api.example.com/data', {
      headers: { Authorization: `Bearer ${apiKey}` }
    })
    const data = await response.json()
    ctx.response.send(data)
  }
}

This pattern ensures the key exists only as a local variable for the duration of the request and is not stored beyond the function scope.

Example 2: Scoped cache with TTL instead of unbounded map

import { LRUCache } from 'lru-cache'
import Env from '@ioc:Adonis/Core/Env'

// Configure a bounded cache with size and TTL limits
const keyCache = new LRUCache({
  max: 50,
  ttl: 1000 * 60 * 5 // 5 minutes
})

export async function getOrCreateKey(identifier: string): Promise {
  const cached = keyCache.get(identifier)
  if (cached) return cached
  // Simulate key derivation or retrieval; in practice, fetch securely
  const freshKey = Env.get(`KEY_${identifier}`)
  keyCache.set(identifier, freshKey)
  return freshKey
}

Using an LRU cache with size and TTL constraints prevents unbounded growth. The cache holds only a fixed number of recent entries and automatically evicts older ones, reducing the risk of accumulation.

Example 3: Avoid attaching keys to request context singletons

// services/key_service.ts
import { singleton } from '@ioc:Adonis/Core/Services'

@singleton()
export class KeyService {
  // Do NOT store per-request key values here
  public getKeyForRequest(requestId: string): string | null {
    return null // placeholder; keys should be fetched per call, not stored
  }
}

// In a controller
import KeyService from 'App/Services/KeyService'
import Env from '@ioc:Adonis/Core/Env'

export default class ReportController {
  public async show(ctx: HttpContextContract) {
    const key = Env.get('REPORT_API_KEY')
    const service = new KeyService()
    // Use local key variable; do not persist on service or context
    ctx.response.send({ ok: true })
  }
}

By keeping key material out of singletons and request-scoped accumulators, you eliminate references that could keep objects alive. Combine this with environment-based configuration and bounded caches to achieve both security and stability.