How LinkedIn's bot detection works (and how Kavex stays under the radar)
LinkedIn has the most aggressive anti-bot stack of any major platform. Understanding the three layers helps you scrape safely.
Layer 1: IP reputation
LinkedIn maintains scoring on every IP that hits them. Datacenter IPs get high anti-bot scores instantly. Residential IPs with low traffic + organic-looking patterns score well.
Layer 2: browser fingerprinting
Beyond cookies + IP, they fingerprint your browser: canvas hash, WebGL, fonts list, audio context, plugin list. Headless browsers leak signals here. Kavex uses puppeteer-extra-plugin-stealth to patch the most common leaks.
Layer 3: behavioral signals
No mouse movements, identical request timing, no scroll events = bot. We add randomised delays (2.5-5.5s per request) and per-context fingerprint variation to look human.
Account ban risk
Even with all of the above, scraping with one cookie too aggressively gets the cookie owner's LinkedIn account flagged or banned. Mitigation: rotate cookies (sub-accounts), stay under ~200 profiles/day/cookie.
Tools mentioned in this guide
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