Detect and Block Onchain Fraud Before Funds Leave
Real-time withdrawal fraud detection that outperforms legacy providers by 4X, stopping phishing, scams, and other schemes before transactions execute.
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Real-Time Withdrawal Fraud Detection and Automated Pre-Transaction Decisioning
Scans all covered blockchains and Web2 sources continuously to detect phishing domains, cloned frontends, drainer contracts, and scam addresses often within minutes of deployment.
ML models and graph analysis identify fraud clusters and hidden relationships across all onchain activity, catching evolving scam typologies that address-level checks miss.
Returns real-time APPROVE or DENY recommendations for withdrawals and transfers, enabling organizations to block fraudulent transactions before funds leave.
Identifies spoofed addresses planted in transaction histories and flags transfers to poisoned destinations before funds are sent.
Tracks addresses for reputation changes, monitors fund flows across bridges, and provides direct and indirect (multi-hop) exposure coverage of illicit activity.
Proven in Public. Verified Onchain
$13M Saved
Phishing attack detected and neutralized before funds could be further drained. Hypernative alerted the team within 2 minutes, enabling the protocol to pause, execute a liquidation strategy, and recover nearly $13M in user assets.
$3.5M Attack Detected
Hypernative detected the drainer address used to steal $3.5M from Curve Finance's hijacked frontend 2 hours before it reached its first victim. Curve was not a customer at the time. Every dollar lost was entirely avoidable.
$10M Saved
An active exploit detected in real time and Solv alerted immediately, despite not being a customer. The rapid warning enabled the team to secure additional contracts and prevent a further $10M from being drained following a $2.7M double-minting attack.
How Fraud Prevention Works
Hypernative continuously monitors all transactions, events, and contract deployments, automatically identifying fraud patterns and mapping them to scam clusters.
ML models and graph analysis identify fraud clusters and hidden relationships, combining onchain activity with offchain intelligence sources to confidently flag bad actors.
When a user initiates a withdrawal or transfer, the API evaluates the destination against real-time threat intelligence and returns an APPROVE or DENY recommendation with evidence.
Addresses are continuously re-evaluated as new intelligence emerges. Previously clean addresses that become linked to fraud clusters are flagged in real time.
Securing Every Stage of Digital Asset Activity
Fraud Prevention is one piece of a comprehensive product suite, purpose-built to protect every layer of on-chain operations.
Frequently Asked Questions
A real-time withdrawal fraud detection and decisioning product that identifies phishing, scams, pig-butchering, address poisoning, drainer activity and other schemes, blocking fraudulent transactions before funds leave via API-first integration.
Legacy tools detect fraud after transactions settle, often weeks later. Hypernative detects in real time before the transaction executes. In benchmarking, we identified 96% of scam addresses vs. 28% by competing providers.
Proprietary machine learning models and graph analysis that identify fraud clusters and hidden wallet relationships across all onchain activity. These models are able to catch coordinated scams that individual address checks miss.
When a withdrawal or transfer is initiated, the API evaluates the destination in real time and returns an APPROVE or DENY recommendation with supporting evidence, enabling automated fraud blocking.
Pig-butchering scams, investment fraud, phishing domains, drainer contracts, address poisoning, honeypot tokens, impersonation tokens, DNS hijacking, fake airdrops, coordinated scam networks - and more.
A single API endpoint that integrates into any withdrawal, transfer, or payment flow. Supports exchanges, custodians, payment providers, and wallets with real-time response at payment-speed latency.
Yes. Fraud Prevention can be enhanced with your own historical data for improved detection accuracy specific to your user base and transaction patterns.
