Rate Limiting Bypass in Fiber with Dynamodb

Rate limiting is a control that restricts how many requests a client can make to an endpoint within a defined time window. When an API built with Fiber uses DynamoDB as a backing store for tracking request counts or tokens without carefully designing access patterns and conditional writes, the control can be bypassed or weakened. This often occurs because DynamoDB conditional writes and atomic counters must be used correctly to enforce per-client or per-key limits; if checks are performed outside of the conditional update, or if the key design does not isolate clients properly, the rate limit can be evaded.

Consider a typical design where a client identifier maps to a DynamoDB item that stores a timestamp and a counter. If the application first reads the item, computes a new count in application logic, and then writes back without a condition that enforces atomicity, an attacker can issue concurrent requests that all see the same pre-update count and each pass the limit check. This is a classic time-of-check-to-time-of-use (TOCTOU) issue. In Fiber, if route handlers perform such non-atomic read-modify-write cycles against DynamoDB, an attacker can send many parallel requests and increment the counter only after all reads, effectively bypassing the intended cap.

Key DynamoDB characteristics that influence this vulnerability include conditional writes, atomic counters (ADD with increment/decrement), and the use of unique keys per client or per bucket. If conditional expressions are omitted, updates are not atomic, and if keys are not scoped narrowly (for example, using a global key instead of a composite of client and time window), different clients can collide or a single client can spread requests across multiple items to avoid detection. Another subtle bypass arises from clock skew or improper window boundary logic; if timestamps are used to define windows and the server or client clocks drift, requests may be attributed to the wrong window, allowing excess requests to slip through. DynamoDB’s strong consistency for reads can mitigate some races, but it does not replace the need for conditional, atomic updates at write time.

In the context of an unauthenticated scan, middleBrick tests whether rate limiting is enforced even when no credentials are presented. It checks whether the API applies limits uniformly, whether responses differ when limits are approached or exceeded, and whether timing or concurrency tests reveal inconsistent enforcement. For a Fiber service using DynamoDB, a bypass might be inferred if concurrent requests all succeed with identical rate-limit headers or if a client can reset counts simply by changing a lightweight query parameter that does not map to a distinct DynamoDB key. These patterns indicate that the safeguard is not reliably preventing abuse.

To reduce risk, design DynamoDB interactions so that limit checks and updates are expressed as a single conditional write. Use client-specific partition keys and time-bucketed sort keys, and employ atomic increments with conditionals that reject updates when the limit would be exceeded. In Fiber, this means moving the check-and-update logic into the DynamoDB operation rather than performing it in application code before the write. Coupled with sensible key design and short, well-defined time windows, this approach makes it significantly harder for an attacker to circumvent rate limits through concurrency or key separation tactics.

Remediation focuses on using DynamoDB conditional writes and atomic counters so that limit enforcement happens atomically on the server side, removing race conditions that a Fiber route handler might introduce. Below is a concise, realistic example for a Fiber application that tracks requests per API key within a fixed time window using atomic increments and a conditional write.

const { DynamoDBClient, UpdateItemCommand } = require("@aws-sdk/client-dynamodb");
const { marshall, unmarshall } = require("@aws-sdk/util-dynamodb");

const client = new DynamoDBClient({ region: "us-east-1" });

async function checkAndIncrement(apiKey, windowKey, limit) {
  const cmd = new UpdateItemCommand({
    TableName: process.env.RATE_LIMIT_TABLE,
    Key: marshall({ pk: { S: `APIKEY#${apiKey}` }, sk: { S: windowKey } }),
    UpdateExpression: "ADD requestCount :inc SET lastUpdated = :now",
    ConditionExpression: "attribute_not_exists(pk) OR requestCount < :limit",
    ExpressionAttributeValues: marshall({
      ":inc": { N: "1" },
      ":limit": { N: limit.toString() },
      ":now": { S: new Date().toISOString() },
    }),
    ReturnValues: "UPDATED_NEW",
  });

  try {
    const res = await client.send(cmd);
    const updated = res.Attributes ? unmarshall(res.Attributes) : {};
    return { allowed: true, count: Number(updated.requestCount?.N) };
  } catch (err) {
    if (err.name === "ConditionalCheckFailedException") {
      return { allowed: false, count: limit };
    }
    throw err;
  }
}

// In a Fiber route handler:
app.all("/api/resource", async (c) => {
  const apiKey = c.get("X-API-Key") || "anonymous";
  const windowKey = `window#${Math.floor(Date.now() / 60000)}`; // 1-minute bucket
  const result = await checkAndIncrement(apiKey, windowKey, 100);
  if (!result.allowed) {
    c.status(429);
    return { error: "Rate limit exceeded" };
  }
  c.set("X-RateLimit-Remaining", String(100 - result.count));
  // proceed with request handling
});

This pattern ensures the increment and the limit check happen in a single DynamoDB update, with the conditional expression preventing updates once the limit is reached. The key includes both entity identity and a time bucket so that counts roll over correctly without manual cleanup. For a more robust implementation, consider using DynamoDB streams to feed a lightweight aggregation or alerting pipeline, but the core protection must reside in the conditional write itself.

When integrating with the middleBrick CLI, you can run middlebrick scan <url> to validate whether your endpoints expose rate-limit bypass risks in unauthenticated scans. Teams on the Pro plan can enable continuous monitoring to detect regressions as code changes, and the GitHub Action can fail builds if a scan’s risk score drops below a chosen threshold, helping keep rate-limiting safeguards reliable across deployments.