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ShieldLabs

ShieldLabs is fraud detection and prevention with traffic quality scoring. It detects users running multiple accounts, account sharing and takeovers under any masking, so you prevent losses from free-trial, bonus, promo and API abuse.

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listed
llms.txt
https://shieldlabs.ai/llms.txt
llms-full.txt
https://shieldlabs.ai/llms-full.txt
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17703 bytes
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Documentation: developer or product docs worth reading, nothing to call
First seen
2026-10-25T18:50:21.655Z
Last checked
2026-10-25T18:50:23.228Z
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Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-25T18:50:21.659Z. Cached copy · Source on shieldlabs.ai

# ShieldLabs

> ShieldLabs is fraud detection and prevention with traffic quality scoring. It detects users running multiple accounts, account sharing and takeovers under any masking, so you prevent losses from free-trial, bonus, promo and API abuse.

ShieldLabs stops fraud and abuse on websites and web apps. It identifies visitors and users, detects masking with 99.9% risk signal detection accuracy and 99.9% identification accuracy, detects bots and AI agents, and returns a risk score with the named signals behind it. Multi-accounting, account sharing, account takeover and impossible travel are detected directly, out of the box. ShieldLabs helps block fraudulent and abusive traffic, with results delivered through the analytics dashboard, API and webhooks.

Founded in 2025. ShieldLabs Inc., Sheridan, Wyoming, USA.

It helps you detect and prevent:
- multi-accounting
- account sharing
- account takeover
- impossible travel
- new-account fraud and fake signups
- free trial and freemium abuse
- subscription abuse and paid-access sharing
- promo and bonus abuse
- referral fraud
- coupon and discount abuse
- ban and restriction evasion
- giveaway and contest fraud
- sybil attacks and airdrop farming
- voting and survey fraud
- ad fraud and affiliate fraud
- bad bots and abusive AI agents

It gives you enterprise-level functionality without enterprise pricing: a self-serve product that connects in about five minutes, with no sales calls and no annual contracts.

Core topics:
- fraud detection and prevention
- traffic quality scoring by source, channel, referrer and UTM campaign
- visitor and user identification with a persistent visitor ID
- device and network intelligence (300+ signals, cross-checked)
- risk signal detection (VPN, proxy, Tor, anti-detect browsers, automation)
- bot and AI traffic detection
- risk scoring (0-100) for the visitor, user, device and IP address
- High-Risk Events: multi-accounting, account sharing, account takeover, impossible travel
- API and webhooks

Risk bands:
- Trusted: 0-29
- Suspicious: 30-59
- Dangerous: 60-100

Risk signals detected (each returned as an individual named signal):
- VPN detection: traffic routed through a VPN to mask the real IP address and location
- Proxy detection: traffic routed through proxy servers of various types, including residential proxies
- Tor detection: connections through the Tor network
- Apple Private Relay detection: relayed connections such as iCloud Private Relay
- Datacenter IP detection: connections from datacenter and hosting infrastructure
- IP reputation: an IP address reported for abuse elsewhere
- Anti-detect browser detection: a browser that spoofs or rotates its fingerprint
- Browser automation detection: headless and automated browsers
- Operating system tampering: a mismatch between the reported and actual operating system
- Incognito mode detection: private browsing used to mask identity
- Geolocation spoofing detection: a spoofed or inconsistent geographic location
…

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