Sybil Attack Prevention
Sybil attack prevention.
Protect fair participation.
Repeated web accounts and bots can claim benefits intended for different participants. ShieldLabs detects multi-accounting at enrollment and claims, helping keep Web3 rewards for eligible members.
Start FreeClaim your allocation
Connect to participate.
Browser automationProxy
Protect program fairness
Help stop multi-accounting from taking extra participation slots.
Protect airdrop allocations
Help stop repeated participation from consuming rewards meant for eligible members.
Stop automated enrollment
Help stop bots from farming program access and rewards.
Preserve legitimate access
Keep eligible participants moving through enrollment and claims.
Sybil resistance at web enrollment
Keep one claim per participant.
Protect program rewards.
ShieldLabs detects multi-accounting at Web3 program signup to stop repeated enrollment and airdrop farming. Preserve allocations for eligible participants.
Automated enrollment
Stop scripted claims.
Keep program access fair.
Bots can farm allowlist enrollment, airdrop and mint claims at scale. ShieldLabs detects browser automation and masking to help keep limited program access available to eligible participants.
Protect airdrop allocations
Help keep distributions available to eligible participants.
Preserve allowlist access
Keep registration capacity from being consumed by automated requests.
Protect mint and claim access
Help stop scripted requests from crowding out legitimate participation.
The ShieldLabs platform
More than a wallet address.
The web activity behind participation.
Use visitor identity, account links, device and network evidence, bot detection and risk assessments around your Web3 interface. Read ready-made account abuse detections and traffic quality together.
Enterprise-level functionality. Without enterprise pricing.
Accurate identification
Recognize returning visitors even when they clear cookies, use incognito mode or change their IP address. Detect linked users with different email addresses.
Identification accuracy
High-Risk Event detection
Detect account abuse out of the box by automatically identifying linked users. No fraud model training is required.
Risk signals
Get ready-made detections of automation, masking and spoofing, with named risk signals behind each result.
Bot & AI traffic detection
Detect bot activity from browser automation signals. Recognize useful crawler and AI traffic alongside abuse detections.
Risk scoring
Score every visit from 0 to 100 and classify it as trusted, suspicious or dangerous. See the signals behind each score.
Device fingerprinting
Recognize devices by their fingerprints. Identify the browser, operating system and device type. Detect headless browsers and tampering.
IP & Network Intelligence
Reveal the real IP address and geolocation behind masking. Detect VPNs, proxies and private relays.
203.0.113.42198.51.100.23Traffic quality scoring and risk analytics
Get one score for all your traffic and ready-made quality results by source, channel, referrer and UTM campaign.
Analytics dashboard
Investigate participant accounts.
Trace the web activity behind claims.
See multi-accounting, bots and risk assessments around program participation. Find the users, visitors and devices associated with detected abuse.
How it works
Start protecting your product.
Connect once and start receiving ready-made fraud and abuse detections.
- 01
Add the snippet or SDK
Connect your product and start identifying visitors and users.
- 02
Receive ready-made detections
Get detected fraud and account abuse in the dashboard or through API integrations.
- 03
Prevent fraud and abuse
Protect customer accounts, paid access and signup incentives in your product.
Developers
Integrate in
5 minutes.
A single API to identify, detect and score. Receive risk data through API responses and webhooks. Integrate selected data points into your business logic to prevent fraud and abuse.
Protect your users and product
Make abuse visible.
Keep genuine users moving.
Start Free FAQ
Understand the evidence.
Put it to work.
What is a Sybil attack?
A Sybil attack uses multiple accounts or identities controlled by one participant to gain disproportionate access, influence or rewards.
How does ShieldLabs help prevent Sybil attacks?
ShieldLabs detects multi-accounting and bots at browser-based enrollment and claims. Help protect airdrops, allowlists and other Web3 programs from repeated and automated participation.
Does a wallet address prove a unique person?
No. A wallet address is an application identifier. ShieldLabs provides web identity and account evidence; it does not certify a physical person or guarantee one wallet per person.
Does this include on-chain transaction analysis?
This page covers web visits, browser and device evidence, account relationships and risk. Wallet labels and participation history in the examples come from your application.
Where should I add detection in a Web3 program?
Add ShieldLabs at browser-based enrollment, allowlist signup and mint or airdrop claims. Multi-accounting and bot detections help protect limited allocations and participation.