Rate Limiting Bypass in Adonisjs with Cockroachdb

Rate limiting in Adonisjs typically relies on in-memory counters or external stores to enforce request caps per identifier. When Cockroachdb is used as the backing store without careful schema design and isolation controls, certain patterns can weaken the effectiveness of those limits.

One common bypass scenario involves the interaction between distributed transactions and the timing of counter updates. If an application checks a request count in Cockroachdb and then increments it in a separate, non-atomic operation, an attacker can interleave multiple requests so that the check passes before the increments consolidate. Cockroachdb’s serializable isolation provides strong guarantees, but application-level read-modify-write sequences that span multiple transactions do not automatically become atomic.

Another bypass vector specific to Adonisjs arises from how identifiers are selected for rate limiting. Using only a user-supplied value (e.g., an API key or IP address) without normalizing or scoping it to a tenant or service can allow attackers to share a single limiting bucket. In a multi-tenant setup where Cockroachdb hosts shared tables, an attacker may intentionally target sparse keys that map to under-utilized rows, reducing contention and avoiding lock-driven backpressure that would otherwise slow bursts.

Adonisjs middleware stacks may also misorder operations. If authentication or tenant resolution occurs after the rate limit check, an unauthenticated attacker can probe endpoints with arbitrary identifiers, causing the check to evaluate against a cold or empty Cockroachdb row. The initial read returns zero, the write increments to one, and repeated identifiers are not consolidated quickly enough under high concurrency, permitting bursts that exceed intended thresholds.

Schema design in Cockroachdb can inadvertently support bypasses if indexes are missing or poorly aligned with the rate limiting query pattern. Without a properly indexed composite key on (identifier, window), queries that filter by time ranges may perform full table scans in tests but exhibit higher latency under load, causing clients to retry and amplifying traffic. This can manifest as inconsistent enforcement where some requests are blocked and others proceed, a behavior that middleBrick flags as BFLA/Privilege Escalation in its security checks.

Finally, the visibility into runtime behavior provided by OpenAPI/Swagger spec analysis (with full $ref resolution) helps correlate design-time expectations with observed findings. middleBrick scans the unauthenticated attack surface of an Adonisjs service backed by Cockroachdb and can detect whether rate limiting is inconsistently applied across operations, producing findings mapped to OWASP API Top 10 and compliance frameworks.

To harden rate limiting with Cockroachdb in Adonisjs, ensure that all counter updates occur within a single serializable transaction and that identifiers are scoped deterministically. The following patterns demonstrate robust implementations.

Atomic increment with deterministic keys

Use a composite key that includes a time bucket (e.g., per-minute window) and a normalized identifier. Perform the read and write in one transaction so that concurrent requests serialize correctly:

import { BaseQueryClient } from '@ioc:Adonis/Addons/Database'

export default class RateLimiter {
  constructor(
    protected db: BaseQueryClient
  ) {}

  async allow(key: string, limit: number, windowMs: number): Promise {
    const bucket = this.getBucket(windowMs)
    const tenantKey = `tenant:123:rate:${key}:${bucket}`

    return this.db.transaction(async (trx) => {
      const row = await trx
        .from('rate_limits')
        .where('tenant_key', tenantKey)
        .forUpdate()
        .first()

      if (!row) {
        await trx
          .from('rate_limits')
          .insert({
            tenant_key: tenantKey,
            count: 1,
            expires_at: new Date(Date.now() + windowMs)
          })
        return true
      }

      if (row.count >= limit) {
        return false
      }

      await trx
        .from('rate_limits')
        .where('tenant_key', tenantKey)
        .update({ count: row.count + 1 })

      return true
    })
  }

  protected getBucket(windowMs: number): string {
    const epoch = Math.floor(Date.now() / windowMs)
    return `${epoch}`
  }
}

Schema and indexes for Cockroachdb

Create a table aligned with the query patterns and add indexes that support efficient seeks. This reduces lock contention and prevents full scans that can amplify traffic under concurrency:

CREATE TABLE IF NOT EXISTS rate_limits (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  tenant_key TEXT NOT NULL,
  count INT NOT NULL DEFAULT 1,
  expires_at TIMESTAMPTZ NOT NULL,
  CONSTRAINT unique_tenant UNIQUE (tenant_key)
);

CREATE INDEX IF NOT EXISTS idx_rate_limits_expires ON rate_limits (expires_at);
CREATE INDEX IF NOT EXISTS idx_rate_limits_tenant_expires ON rate_limits (tenant_key, expires_at);

Middleware ordering and normalization

Ensure middleware execution order places rate limiting after stable identifier resolution. Normalize identifiers to avoid shared buckets across unrelated consumers:

import { HttpContextContract } from '@ioc:Adonis/Core/HttpContext'
import RateLimiter from 'App/Services/RateLimiter'

export default class RateLimitMiddleware {
  constructor(
    protected limiter: RateLimiter
  ) {}

  public async handle(
    { request, response }: HttpContextContract,
    next: () => Promise
  ) {
    const key = request.url() // simplified; include tenant and method in production
    const allowed = await this.limiter.allow(key, 100, 60_000)
    if (!allowed) {
      return response.status(429).send({ error: 'Too Many Requests' })
    }
    await next()
  }
}

By combining atomic transactions, precise indexes, and deterministic key construction, Adonisjs applications using Cockroachdb can enforce rate limits consistently. middleBrick’s scans highlight gaps in these controls and map findings to remediation guidance, helping teams align implementation with expected security posture.