Api Rate Abuse in Restify with Bearer Tokens

Rate abuse occurs when an attacker issues a high volume of requests to consume resources, degrade service, or bypass usage limits. In Restify, combining per-endpoint rate limits with Bearer token identification can create or expose subtle vulnerabilities if token scope and rate enforcement are not aligned.

Bearer tokens are commonly passed via the Authorization header as Authorization: Bearer <token>. If rate limiting is applied globally or per IP rather than per token, a single compromised or shared token can be used to exhaust quotas for many users. Attackers can script token reuse across requests, especially when tokens have long lifetimes or broad scopes, effectively bypassing intended per-user limits.

In a black-box scan, middleBrick tests this combination by submitting authenticated requests with distinct Bearer tokens while monitoring whether rate limits are enforced at the token level. Without token-aware throttling, repeated abusive requests can succeed until global thresholds are reached. This exposes an unauthenticated attack surface because an endpoint may accept anonymous traffic but still expose token-based routes; middleBrick flags such inconsistencies under Rate Limiting and Authentication checks.

Additional risk arises when tokens are issued with elevated scopes or when token binding is weak. For example, a token intended for read-only access might still be usable to trigger costly operations if rate limits are not differentiated by scope. Attack patterns like token replay or credential stuffing can exploit weak segregation between token identity and enforcement windows, leading to privilege escalation or denial of service.

middleBrick includes checks for Rate Limiting and Authentication in parallel, cross-referencing OpenAPI specifications with runtime behavior. If your spec defines securitySchemes as type: http with scheme bearer but does not require distinct rate limits per token scope, the scan will highlight gaps. Findings include guidance to scope limits to token identity and to validate enforcement across authenticated paths.

Remediation focuses on tying rate limits to the Bearer token identity and ensuring enforcement is consistent across all authenticated routes. Below are concrete Restify examples using a token-aware rate limiter.

First, define a map to track requests per token and enforce limits within a sliding window. This example uses an in-memory store for simplicity; in production, use a shared store like Redis when scaling across instances.

const restify = require('restify');
const server = restify.createServer();

// Simple token bucket store keyed by Bearer token
const tokenBucket = new Map();
const RATE_LIMIT = 100; // requests
const WINDOW_MS = 60_000; // 1 minute

function allowRequest(token) {
  const now = Date.now();
  const record = tokenBucket.get(token);
  if (!record) {
    tokenBucket.set(token, { count: 1, start: now });
    return true;
  }
  if (now - record.start > WINDOW_MS) {
    record.count = 1;
    record.start = now;
    return true;
  }
  if (record.count < RATE_LIMIT) {
    record.count += 1;
    return true;
  }
  return false;
}

server.pre((req, res, next) => {
  const auth = req.headers.authorization || '';
  const match = auth.match(/^Bearer\s+(\S+)$/);
  const token = match ? match[1] : null;
  if (!token || !allowRequest(token)) {
    return next(new restify.UnauthorizedError('Rate limit exceeded or missing token'));
  }
  return next();
});

server.get('/secure', (req, res, next) => {
  res.send({ ok: true });
  return next();
});

server.listen(8080, () => console.log('Listening on port 8080'));

This approach scopes enforcement to the token extracted from the Authorization header. Ensure tokens are issued with minimal scopes and short lifetimes to reduce impact of reuse. Rotate secrets regularly and avoid sharing tokens across services.

For distributed environments, replace the in-memory map with a rate limiter backed by Redis, using atomic operations to maintain consistency. The principle remains: identify the request by token and enforce limits independently of IP or global counters.

middleBrick’s CLI can validate these patterns by scanning your endpoints; run middlebrick scan <url> to see whether rate limiting is enforced per token in practice. The dashboard helps track scores over time, and the GitHub Action can fail builds if risk scores degrade after changes to authentication or rate-limiting logic.