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[ on-chain  ·  solana + evm ]

Token Risk Check

Paste any contract address for an instant on-chain risk assessment -- honeypot detection, liquidity analysis, holder concentration, and contract permissions.

Read the contract before the contract reads you. Honeypot, rug, and scam detection from on-chain state — not market data.

⚠️ Token Risk Check
✓ On-Chain Analysis
🔒 No Signup
⚡ Results in Seconds
🔍 Honeypot detection
💧 LP lock status
👥 Holder concentration
⚡ Solana + EVM
4.6 / 5 from 2,174 users Direct on-chain reads 🔐 Non-custodial — no wallet connect required Sub-5-second scan 🔗 Solana · Ethereum · Base · Arbitrum · BNB · Polygon · Avalanche 📊 54,628 risk checks run
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Unlimited Token Risk Checks

Verify every contract before buying. Honeypot detection, LP lock analysis, and holder concentration reviews across Solana and EVM.
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Live Detections
127 scans today
49K+Scans Run
6Chains
15+Risk Signals
FreeFirst Check
What the checker detects
Example signals · run a scan to see live results
⚠️Sell TaxDETECTED
💧LP LockUNLOCKED
🔑Mint AuthorityACTIVE
OwnershipRENOUNCED
🐋Whale Wallet42%
📅Token Age3 DAYS
🚨Approval RiskHIGH
CooldownACTIVE
🔄Last Update48H AGO
📉Liquidity 24h-12%
🚫Transfer LockENCODED
Freeze AuthENABLED
📋ContractVERIFIED
💰LP Depth$48K
🔗Blacklist FnPRESENT
🔍
Honeypot Detection
Simulates sell transactions to detect transfer locks, fee traps, and whitelist-only exit conditions before you buy in. Reads the contract directly — not market data. Works across Solana SPL tokens and all major EVM chains.
💧
Liquidity & Holders
Reviews pool depth, LP lock status, and top wallet percentages. Surfaces unlocked pools and concentrated wallets before the price collapses.
Results in Seconds
On-chain read — no API delays, no market data lag. Raw contract analysis returned in under 5 seconds.
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Token Risk Analysis -- Contract, Liquidity & Holders

🔗 TL;DR

A token's risk lives in three places: contract permissions (can the dev mint, freeze, or block sells?), liquidity structure (is the LP locked and deep enough to exit?), and holder distribution (can a handful of wallets dump the entire float?). The checker above reads all three directly on-chain in under five seconds.

Scan time< 5 sec
Signals checked15+
Cost (first check)Free

Coordinated buy detection focuses on uncovering clusters of transactions that occur in close temporal proximity and exhibit patterns consistent with collective action. On the surface, multiple buys occurring almost simultaneously from different wallet addresses might suggest genuine, organic interest in a token or a surge in momentum driven by market hype. However, these transaction clusters can sometimes obscure a more complex underlying reality. In many cases, what appears as decentralized demand may in fact be orchestrated by a single controlling entity employing various technical mechanisms to simulate widespread participation. This fundamental disconnect between apparent decentralization and actual control complicates the interpretation of such activity, as coordinated buys can be used strategically to influence price dynamics or market perception.

A critical analytical dimension in detecting coordinated buys is understanding the relationship between wallet addresses and the private keys that control them. Since a private key grants full authority over an address’s transactions, a single actor can control multiple addresses simultaneously, coordinating buys across them to create an illusion of broad market interest. This technique can involve distributing funds across dozens or even hundreds of addresses and executing buys in rapid succession or near-simultaneously. Detecting this requires moving beyond mere timing analysis to examine nuanced transaction metadata and blockchain heuristics. Patterns such as repeated use of addresses in tandem, shared nonce values that suggest sequential transaction submission by the same entity, or consistent interactions between a set of wallets can all hint at underlying control relationships. Without this deeper inspection, surface signals of synchronized buys remain potentially misleading or incomplete.

Another layer of complexity emerges when considering transaction fee structures and wallet management configurations that influence how coordinated buys are executed. On networks with low or negligible fees, executing large volumes of small transactions across many addresses is economically feasible. This can facilitate spam-like coordinated buys that inflate apparent volume and create false impressions of demand. In such environments, the barrier to orchestrating elaborate multi-wallet purchase strategies is low, enabling actors to artificially boost liquidity metrics or manipulate price momentum. Conversely, on blockchains with higher transaction fees, actors are incentivized to consolidate control into fewer addresses or implement multisignature (multisig) wallet setups to optimize operational costs and manage risk. Multisig wallets, which require multiple private keys to authorize a transaction, can complicate detection efforts by distributing control authority and obscuring straightforward private key ownership. While multisigs reduce single points of failure and enhance security, they also introduce challenges in attributing coordinated activity, as multiple signatories can mask the true origin of transaction sequences. The interaction between network fee economics and wallet architecture thus shapes the scale, frequency, and subtlety of coordinated buy patterns observed on-chain.

It is important to emphasize that the presence of coordinated buy patterns alone does not inherently confirm malicious intent or manipulation. In some cases, coordinated buys can represent legitimate collective action by investment groups, decentralized autonomous organizations (DAOs), or community-led initiatives pursuing shared strategies. These entities may intentionally synchronize purchases to signal confidence in a project or to meet governance or staking thresholds. Additionally, certain tokens or projects implement incentive structures—such as airdrops, liquidity mining rewards, or timed community buys—that encourage collective purchasing behavior, which can mimic the appearance of coordination but stem from valid, transparent mechanisms. Therefore, interpreting coordinated buy signals requires careful contextualization alongside other data points, including wallet relationship mappings, contract design analysis, and broader market conditions. Without such triangulation, there is a risk of false positives or mischaracterizing benign activity as manipulative.

Further complicating the analytical landscape is the role of automated bots and algorithmic trading strategies in executing coordinated buys. Bots can be programmed to distribute buy orders across multiple addresses with precise timing and sizing to simulate organic market interest while pursuing profit or manipulation goals. These automated agents can respond rapidly to price changes, social media signals, or on-chain events, amplifying coordinated buying effects. Detecting bot-driven coordination demands analyzing not only transaction timing but also patterns in order book interactions, gas usage anomalies, and repetitive behavioral signatures that deviate from typical human trading patterns. Even so, the mere presence of automation does not automatically imply bad faith; bots can facilitate legitimate trading and liquidity provision. Consequently, bot activity must be interpreted within a broader analytical framework.

In sum, coordinated buy detection is a sophisticated analytical endeavor that hinges on unraveling the complex interplay of technical controls, economic incentives, and behavioral patterns. It requires moving beyond simplistic clustering of transaction times toward a holistic examination of private key control, wallet interrelationships, fee structures, and contract mechanics. While coordinated buy patterns can sometimes indicate attempts to manipulate price or market perception, these signals are not definitive proof of nefarious intent. They must be integrated with additional on-chain and off-chain intelligence to produce meaningful insights into token risk profiles and market dynamics. This nuanced approach enables a richer understanding of how coordination manifests in decentralized markets and helps differentiate between authentic collective action and orchestrated deception.

Pre-buy on-chain checklist

  • Mint authority renouncedConfirms supply is capped — no new tokens can be issued post-launch.
  • LP locked or burnedLiquidity cannot be removed in a single transaction. Lock duration and locker contract are both verifiable on-chain.
  • !Top 10 holders under 40%Lower concentration means coordinated dumps are mechanically harder. Above 40% is a structural caution.
  • !No active freeze authorityActive freeze means wallets can be paused at the contract level — no exit possible during a freeze.
  • ×No transfer restrictionsThe transfer function should accept any holder selling. Encoded sell blocks, whitelist exits, and hidden tax functions are honeypot signatures.

Frequently asked questions

Verify the contract address before you buy in. Paste it into the scanner above for the full on-chain breakdown.

Why on-chain signals matter

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Solana + EVM Checks SPL tokens and EVM contracts across Ethereum, Base, Arbitrum, BNB Chain, Polygon, and Avalanche.
⚙ Methodology
Every risk verdict is generated from three on-chain reads run in parallel: (1) direct contract bytecode analysis for honeypot patterns, mint/freeze authority, and blacklist functions; (2) liquidity pool inspection for LP lock status, depth, and removable percentage; (3) holder distribution from token-account snapshots. No editorial opinion is layered on the output. Read the full methodology →