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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
4.9 / 5 from 2,186 users Direct on-chain reads 🔐 Non-custodial — no wallet connect required Sub-5-second scan 🔗 Solana · Ethereum · Base · Arbitrum · BNB · Polygon · Avalanche 📊 63,813 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.
$5.6BFBI crypto losses 2023
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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

Smart contract trust scores often hinge critically on the contract’s mutability, with particular attention paid to whether the contract employs a proxy upgrade pattern. At first glance, a contract that appears immutable can create a strong impression of security, as immutability is widely perceived as a safeguard against tampering. However, the presence of an upgradeable proxy complicates this narrative significantly, since the logic governing the contract’s behavior can be altered post-deployment by authorized parties. This introduces a structural risk pattern where the contract’s outward appearance—its initial bytecode—may not fully represent its operational reality over time. Consequently, a smart contract that seems immutable might in fact be mutable in practice, challenging the assumption that immutability alone is a sufficient marker of trust. The potential for logic changes after launch means that vulnerability or malicious features can be introduced well after the initial deployment, and this temporal aspect of risk is difficult to gauge from static code inspection alone.

The importance of mutability patterns in trust scoring is magnified by the role of control over private keys linked to critical functions within the contract, especially those with upgrade authority. Holding these keys effectively grants governance over the contract’s future state and asset flows, since private keys authorize all interactions from a given address. This dynamic places private key custody at the center of trust analysis because the well-audited code of a contract can be rendered moot if the key-holder acts with malicious intent or if the keys are lost, stolen, or compromised. Additionally, many contracts lack recovery mechanisms for lost or stolen keys, which can lead to permanent asset loss or contract paralysis. Therefore, the governance structure around key management can sometimes serve as a more telling indicator of risk than code characteristics alone. Contracts with upgrade authority concentrated in a single key-holder generally carry higher operational risk compared to those with distributed control, though even distributed control is not without its pitfalls due to potential collusion or failure among signers.

Transaction fee structures and multisig wallet configurations further contribute to the nuanced landscape of smart contract operational security. Chains with high transaction fees can disincentivize spam or low-value exploit attempts, effectively raising the cost of attack and potentially reducing the frequency of probing transactions that seek vulnerabilities. Conversely, low-fee chains often enable frequent, low-cost transactions, which can sometimes allow attackers to test exploit vectors repeatedly with minimal financial consequence. This environmental context is integral to interpreting contract risk since the external economic incentives shape attacker behavior. Meanwhile, multisig wallets introduce a mechanism for shared control by requiring multiple parties to approve key actions such as upgrades or fund transfers. This can reduce the risk of a single point of failure but also introduces complexity in coordination and potential delays in response times. Multisig arrangements are not infallible; they do not exclude the risk of collusion among signers, insider threats, or operational mistakes such as lost keys or slow decision-making. When these factors interact, a low-fee chain combined with single-key control typically elevates risk, whereas multisig wallets operating on moderate-fee platforms may offer a more balanced security posture, though still subject to governance and operational challenges.

In constructing a meaningful smart contract trust score, it is essential to evaluate structural capabilities alongside governance and operational realities. Proxy upgrade patterns should not be automatically equated with malign intent; they often exist to enable necessary bug fixes, feature improvements, or adaptations to changing ecosystem standards. Similarly, centralized private key control does not inherently imply abuse, though it remains a significant vector of risk if the key-holder is compromised or acts maliciously. Multisig wallets provide an improved security layer but require careful analysis of signer distribution, operational procedures, and the potential for human error or collusion. A robust trust scoring model therefore requires a layered approach that accounts for the coexistence of these factors, recognizing that they operate within legitimate and necessary frameworks in many cases.

Moreover, the trustworthiness of a smart contract is not static but evolves alongside the practices of the governing parties and the broader ecosystem context. For instance, a contract with an upgradeable proxy governed by a highly transparent and reputable multisig group may carry a different risk profile than one governed by a single anonymous key-holder without visible safeguards. Community governance, timelocks on upgrades, and public communication around contract changes can mitigate some risks associated with mutability. Conversely, opaque upgrade processes or frequent changes without clear rationale can indicate elevated uncertainty. Therefore, smart contract trust scores should integrate not only static code features and access control patterns but also dynamic governance signals and behavioral data to reflect risk as a continuum rather than a binary state.

The interplay between contract architecture, key management, fee environments, and multisignature governance creates a complex mosaic of risk factors that demand careful, context-aware analysis. Structural patterns like proxy upgrades and private key control highlight potential vectors for contract manipulation, but these alone do not confirm malicious intent. Instead, they serve as foundational indicators that must be assessed alongside operational practices and ecosystem dynamics. Only through a nuanced, multi-dimensional approach can trust scores meaningfully inform assessments of smart contract risk, aiding stakeholders in navigating the evolving and sometimes opaque world of decentralized finance.

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 →