How anti-bot systems work, and where a proxy tier fits

Originally published on proxymint.

How anti-bot systems work, from vendor docs: IP reputation, TLS fingerprints, browser checks and behaviour feed one score, then a policy picks the challenge.

TL;DR

• Anti-bot systems turn many signals into one score. Cloudflare scores requests from 1 (automated) to 99 (human); Akamai scores from 0 (human) to 100 (bot). The site owner's policy decides what each score triggers.
• The inputs are IP, network fingerprints, browser checks and behaviour. Vendors combine heuristics, machine learning and in-page JavaScript checks, then track the same client over time through cookies and fingerprint statistics.
• A challenge is the policy acting on the score. Most real visitors pass invisible checks without seeing anything; a visible challenge or a block means earlier inputs already pushed the request into the risky range.
• The proxy tier changes the IP, the ASN and the TCP stack. The TLS handshake runs end to end through the tunnel, and the browser and the pacing come from the client on every tier.
• Automation a site accepts has a declared route. Verified bots identify themselves through published IP ranges, reverse DNS or signed requests, and they obey robots.txt.

Key takeaways

• Run a no-proxy baseline first. If the same client is challenged from a home connection, the client is the problem, and no proxy tier changes that.
• Do not claim to be a browser you are not. A script handshake behind a Chrome User-Agent contradicts itself on every tier; name the tool, or run a real browser where the page needs one.
• Keep cookies across a session. Vendors smooth scores with session history, and a client that drops cookies starts from nothing on every request.
• Treat a refusal as an answer. If a site does not permit automated access, a block means stop; use its API or its verified-bot route instead.

Read the full guide on proxymint, with the commands, tables and sources.