RektRadar

RektRadar

Real-time scam detection on Ethereum, from contract deployment to the day the dev disappears.

RektRadar is an on-chain analytics platform that continuously monitors every new token deployed on Ethereum mainnet and tells you within seconds whether it is a scam, a honeypot, or a high-risk project. Unlike competing detectors that score each token once at deployment time, RektRadar keeps re-scoring it at J0, J7 and J30, which lets it catch the "late rugs": tokens that look clean at launch and have their liquidity quietly pulled days later. On 3 600 production tokens, our multi-temporal pipeline flagged 47 percent of them as late rugs that a static-only scan would have missed.

Key features

Multi-temporal detection. Every new token is analyzed within seconds at deployment (J0): bytecode, ownership, taxes, liquidity, source verification, signatures of known scams. A nightly cron re-snapshots every analyzed token at J7 and J30 and surfaces dynamic flags a static scan cannot see: volume collapse, late LP burn, dev inactivity, late-onset honeypot. The drift score (max post-J0 vs J0) quantifies how wrong the initial verdict was.

Wallet Audit. For any externally owned account, RektRadar produces a dual report in seconds: scammer side (cluster membership, scam tokens deployed, on-chain relations, 0-100 score) and victim side (scam tokens received, estimated ETH net loss, top scams). Methodology is fully on-chain and auditable.

3D Explorer. Interactive WebGL visualization of the deployer / funder / cluster graph across three renderers. Trace a rug back to its origin wallet, spot serial deployers and bot networks, map wash-trading rings.

Forensics. Consolidated per-token report: mempool history, swaps with price impact, LP events, deployer profile, holder distribution, similarity to known scam templates, links to Etherscan and GoPlus.

Late Rugs dashboard. Public view of the scams the T0 detector missed, with drift distribution and a live "late rug rate" metric as a quality indicator for the detector itself.

Use cases

• Traders vet a token in two seconds before buying, get alerted when liquidity is pulled while holding.
• Analysts and journalists trace fraud back to its origin cluster, identify serial deployers, export forensic reports.
• Compliance and law enforcement follow flows between rug-involved wallets, deliver graph-based evidence.
• Wallet and aggregator builders integrate the scoring API to score every token before a swap (sub-100 ms).
• Researchers access a dataset of scams scored across time horizons, cross-checked against GoPlus Security.

What sets it apart

• J0 / J7 / J30 multi-snapshot by default. Most competitors (Token Sniffer, GoPlus, Honeypot.is) score once. RektRadar surfaces the half of scams a static scan lets through.
• Transparent precision and recall. The platform runs a stratified cross-check against GoPlus Security, computes precision / recall / F1 and ships the metrics publicly. We measure our own errors rather than hide them.
• Native on-chain stack. Three dedicated Ethereum nodes (Nethermind + Lighthouse), no Etherscan dependency on the critical path, sub-second analysis latency.
• Wallet audit covers both sides. Victim angle is rare in the space and lets users assess counterparty risk in OTC trades and treasuries.
• Open hexagonal architecture. 17 microservices, ports and adapters, room to add Solana or L2s without rewriting business logic.

72 000+ tokens analyzed and tracked, growing by ~1 700 per day. 3 600+ already snapshotted at J7. Live at app.rektradar.io.