Skip to content

How LinkedIn's bot detection works (and how Kavex stays under the radar)

Concept explainers·1 min read·Updated 2026-05-13·4 sections

LinkedIn has the most aggressive anti-bot stack of any major platform. Understanding the three layers helps you scrape safely.

01

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.

Rate
3 credits / profile
LinkedIn Profiles
1 credit
$0.001
credits never expire
Per 1,000
$3.00
3,000 credits
RUN SIZECREDITSCOST
30 profiles90$0.09
100 profiles300$0.30
500 profiles1,500$1.50
1,000 profiles3,000$3.00
3 credits a profile · 1 credit = $0.001 · a run that finds fewer pays less
02

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.

03

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.

04

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.

Written after running it
Last run
plumbers · London · Google Maps
Rows
30
Time
23s
Cost
€0.06
30 rows × 2 credits = 60 credits · 1 credit = $0.001

Every number on this page comes from that run, not from a price list.

Point it at something. See what comes back.Your first 500 Google Maps leads are on the house. No card needed.
Start scraping free