LLM Scraper Bots Overloading HTTPS Servers: A Growing Infrastructure Crisis
LLM Scraper Bots Are Overloading HTTPS Servers Across the Internet
A system administrator at acme.com has revealed that LLM-powered scraper bots are causing significant infrastructure problems for small websites, highlighting a growing crisis as AI companies aggressively crawl the web for training data.
What Happened
Starting February 25, 2026, acme.com suffered intermittent outages lasting over a month — high ping times, packet drops, and complete unavailability. After extensive troubleshooting with ISP Sonic, the admin discovered the root cause at 1 AM: nearly all incoming traffic was from LLM scraper bots.
Key observations:
- Almost all incoming packets were web requests
- Nearly all targeted non-existent pages
- Almost exclusively on port 443 (HTTPS)
- User-agent strings proudly identified them as LLM scraper bots
The Technical Problem
The site runs two servers: a fast HTTP server and a slower HTTPS server. The LLM bots overwhelmed the HTTPS handler, causing congestion that spread to the NAT daemon, leading to cascading packet loss across all traffic.
The fix was simple but drastic: closing port 443 eliminated all problems immediately.
Why This Matters
This isn't isolated. The admin knows of two other hobbyist sites experiencing identical issues. As more AI companies scale their web scraping operations, small operators are bearing disproportionate infrastructure costs they can't afford.
The broader implications:
- Legitimate traffic displaced — bots consuming bandwidth meant for real users
- HTTPS overhead amplified — TLS handshakes make bot traffic more expensive than HTTP scraping
- No opt-out mechanism — robots.txt is often ignored by scraping operations
- Asymmetric cost burden — small sites pay for AI companies' data acquisition
The Industry Failure
LLM companies are "pounding every site on the net" without adequate rate limiting or respect for server capacity. Unlike traditional search engine crawlers that throttle themselves and respect robots.txt, many LLM scrapers operate with minimal consideration for target server load.
This represents a fundamental market failure: the costs of AI training data acquisition are externalized onto millions of website operators who receive no compensation and have no recourse.
Potential Solutions
- Mandatory rate limiting for AI crawlers at the protocol level
- Economic mechanisms — payment or credit for data usage
- Technical standards — standardized AI-crawler identification and throttling
- Legal frameworks — extending unauthorized access laws to excessive scraping