Api Rate Abuse in Adonisjs with Redis

Rate abuse in AdonisJS applications that use Redis as a shared rate-limit backend can occur when request counting and enforcement are not consistently applied across distributed instances. AdonisJS does not provide built-in rate limiting; developers typically implement custom logic or use community packages that rely on an external store like Redis to coordinate limits across processes and servers. If the counting key is derived from an unverified client identifier (for example, IP address only) or is not incremented atomically, an attacker can bypass limits by rotating identifiers, exploiting inconsistent key scoping, or flooding a single endpoint faster than the sliding window can decrement.

Redis itself is fast and single-threaded for command execution, which is often chosen for rate limiting because operations like INCR and EXPIRE are expected to be atomic. However, the application must use these commands correctly within AdonisJS middleware or route hooks. A common vulnerability pattern is to read the current count, evaluate it in JavaScript, and then set a new value. Between read and write, an attacker can issue many requests that all see the same count and pass the check. Another exposure is missing TTL alignment: if INCR is called without ensuring the key has an expiry, the counter can grow indefinitely or never reset, undermining windowed controls.

Specific attack patterns include token bucket manipulation via crafted headers that cause cache key collisions, and burst exploitation where a single window allows more requests than intended due to race conditions in Lua execution or missing atomic compound operations. Without idempotent and atomic increments combined with key normalization (consistent namespace, granularity, and TTL), the effective protection degrades to a best-effort metric. This is particularly risky for high-value endpoints such as authentication tokens, password resets, and billing actions, where abuse can lead to credential stuffing, enumeration, or resource exhaustion.

In AdonisJS, these risks are realized when middleware logic does not enforce strict per-key atomicity and when Redis data structures are chosen without considering TTL precision and rollback behavior. For example, using a simple string key like rate:ip: without namespacing by route or method can cause cross-endpoint leakage of counters, while omitting Lua scripts for compound checks (read, compare, increment, set-if-absent) introduces timing gaps an attacker can exploit.

To mitigate, you should implement rate limiting in AdonisJS with fully atomic Redis operations, typically via EVAL SHA scripts that execute increment, TTL check, and expiry setting in a single server-side step. Keys must include method and path, TTL must be set on first creation and preserved on increments, and response headers should communicate remaining quota and reset time. This ensures that even under high concurrency, the count is consistent, the window is enforced precisely, and abuse is detected early with minimal overhead.

Remediation centers on atomic increments with TTL initialization using Redis Lua scripts, strict key naming, and header-based feedback. The following patterns assume you have a Redis client available in your AdonisJS application, for example via @adonisjs/redis or a custom provider that exposes a redis use contract.

Atomic rate limiter via Lua script. Use a script that reads the key, checks TTL, increments, and sets expiry only if the key did not exist. This eliminates read–write races.

// resources/scripts/rate_limit.lua
local key = KEYS[1]
local limit = tonumber(ARGV[1])
local window = tonumber(ARGV[2])
local current = redis.call('INCR', key)
if current == 1 then
  redis.call('EXPIRE', key, window)
end
local remaining = math.max(0, limit - current)
local retry = redis.call('TTL', key)
return {current, remaining, retry}

In your AdonisJS middleware or route action, load and invoke the script:

// start/hooks/rateLimit.ts or within a controller method
import { HttpContextContract } from '@ioc:Adonis/Core/HttpContext'
import Redis from 'ioredis'
import fs from 'fs'
import path from 'path'

const redis = new Redis()
const rateScript = fs.readFileSync(path.resolve(__dirname, '../../resources/scripts/rate_limit.lua'), 'utf8')

export default class RateLimitMiddleware {
  protected keyFor(ctx: HttpContextContract) {
    const ns = 'api:rl'
    const routeName = ctx.route?.getRouteName() || 'unknown'
    const ip = ctx.request.ip()
    return `${ns}:${routeName}:${ip}`
  }

  public async handle({ request, response, next }: HttpContextContract) {
    const key = this.keyFor(request)
    const [limit, window] = [100, 60] // 100 requests per 60 seconds
    const [current, remaining, retry] = await redis.eval(rateScript, 1, key, limit, window)
    response.header('X-RateLimit-Limit', String(limit))
    response.header('X-RateLimit-Remaining', String(remaining))
    if (retry && retry > 0) response.header('Retry-After', String(retry))
    if (current > limit) {
      response.status(429).json({ error: 'Too Many Requests' })
      return
    }
    await next()
  }
}

Key normalization and namespacing. Include method and route name in the key to avoid collisions across endpoints and HTTP verbs. This also supports differentiated limits per resource.

const key = `api:rl:${ctx.request.method()}:${ctx.route?.getRouteName() || 'root'}:${ctx.request.ip()}`

TTL discipline. The Lua script ensures EXPIRE is set only when the key is first created, preserving the original window even as INCR updates the counter. Do not call EXPIRE separately after INCR in non-atomic code, as this can reset the window under race conditions.

Burst and header feedback. Returning X-RateLimit-Remaining and Retry-After allows clients to self-throttle. For stricter burst control, add a second tier key with a shorter TTL (e.g., per-second burst) and evaluate it before the main check.

Fail-safe behavior. If Redis is unavailable, decide whether to open the gate or reject traffic. For AdonisJS, prefer safe failure modes such as logging and allowing requests only when you can guarantee auditability, but document the reduced protection. Monitor Lua script execution time and error rates to detect misconfiguration or network issues.

These fixes align the Redis usage with the intended windowed semantics, eliminate race conditions via server-side atomicity, and provide clear remediation guidance specific to an AdonisJS codebase.