Repeated Request Patterns: Anatomy of a Client-Side Flood

Simulation: Repeated Request Pattern & Why It Overwhelms Sites

Interactive, evidence-focused simulation. This demonstration visualizes the reported pattern where a page issues repeated, randomized search requests — shown here as a safe simulation (no network requests).

Overview

What the community reported

Public posts and community threads document that an archive CAPTCHA page contained a short client-side script that used a timer (reported ~300 ms) to repeatedly construct and issue requests like https://gyrovague.com/?s=random. Those observations and screenshots are cited in the Sources section below. This post explains the pattern, shows a safe simulation, and lists mitigation steps.

Simulation of Repeated Request Attack (visual-only)

Interactive canvas

Simulated request stream

Each pulse visualizes one simulated request (no network traffic)

Interval 300 ms

Total requests

0

Requests / sec (per tab)

0.00

Open pages (example)

1

// Reported pattern (for explanation only — do NOT execute):
// setInterval(function() {
//   fetch("https://gyrovague.com/?s=" + Math.random().toString(36).substring(2, 3 + Math.random() * 8));
// }, 300);

Simulated request log

Simulation log — simulated URLs appear here. No requests are sent.

This simulation shows the *pattern* only. It does not contact any servers. The URL pattern shown mirrors the publicly reported format.

Use Step to advance one simulated request at a time, or Start to run continuously. Adjust the Interval slider to demonstrate higher or lower impact visually.

Technical breakdown — step by step

Step 1 — Timer

Client-side code creates a repeating timer. The browser executes its callback every N milliseconds (reported values in community posts: ~300 ms).

Step 2 — Request generation

On each tick the script builds a request URL containing a randomized query parameter. Randomization prevents simple caching and forces the server to compute or fetch a unique response.

Step 3 — Cumulative load

One open tab sending ~3 req/sec produces ~10k req/day. Multiply that by many visitors (or automated pages), and the origin can become overload-bound: CPU, database, bandwidth or connection limits are exhausted.

Step 4 — Practical impact

For small personal blogs or low-capacity hosts this pattern can cause slowdowns, failed queries, and outages — which are the typical practical effects of DDoS-level traffic.

Recommended mitigations (concise)

1. Rate-limit heavy endpoints (429 responses for excess requests).
2. Serve lightweight cached responses for unknown/random queries.
3. Block or challenge requests with obvious randomized tokens at the edge (CDN/WAF).
4. Monitor and alert on repeated, short-interval requests to search or search-like endpoints.
5. Collect request headers / timestamps for forensic reports if needed.

Sources (public reporting & community threads)

Gyrovague — original investigation with code & screenshots Hacker News discussion Lobsters thread Reddit /r/DataHoarder thread Public correspondence (pastes.io) — published excerpts

The links above are the public materials this post summarizes. Allegations about individual behavior or motives are presented as reported in those sources and are not asserted here as independently verified facts.