New Token Launch Risk Score

WHAT IT DOES

New Token Launch Risk Score provides AI-powered risk analysis for newly launched or recently discovered token contracts. Scan token contract bytecode to detect ownership controls that could drain funds, minting functions that could inflate supply without limit, and liquidity locking status that indicates whether the deployer can withdraw liquidity. Detect honeypot transaction patterns that allow transfers only for specific addresses, proxy patterns that could point to malicious implementations, upgradeable contract risks, admin key exposure, and suspicious ownership configurations. Analyze transaction simulation results to see how the contract behaves under different conditions. View token holder distribution to detect concentration risks and whale dominance. Review recent transaction activity to identify suspicious patterns. The AI then translates these technical findings into a plain-language risk summary with specific flags, code quotes where relevant, and a final overall risk rating (Low/Medium/High/Critical).

HOW TO USE

1. Connect your Web3 wallet (Ethereum, Polygon, Arbitrum, Optimism, BSC, or any EVM-compatible chain).

2. Select the network that matches your target chain from the dropdown.

3. Paste the token contract address you want to analyze.

4. Select analysis depth (standard or deep).

5. Click "analyze risk" to read on-chain data and generate an AI explanation.

6. Review the risk summary, specific flags, and overall risk rating.

7. Make an informed decision about whether to interact with the token.

8. Remember: the AI explanation informs your decision, but your wallet's signature prompt is what actually protects you — always review it before signing.

TECHNICAL MECHANISM

New Token Launch Risk Score reads real on-chain data including bytecode, ownership configuration, and recent transaction history. The tool uses a combination of pattern recognition and AI analysis to identify scam indicators. It scans bytecode for known opcode sequences associated with honeypots, blacklist functions, and unrestricted minting. It checks ownership patterns for single admin keys, multi-sig configurations, and renounced ownership. It verifies liquidity locking status by checking for lock contract interactions. It detects proxy patterns by examining storage slots and delegatecall usage. The parsed on-chain data is then passed to the AI bridge (BGAI.generate) which produces a structured risk summary in plain language. The AI explanation is capped at 1,024 tokens and focuses on actionable flags rather than general commentary. The tool presents the risk rating and specific findings before any action is taken, enabling the user to make an informed decision.

WHAT IT CANNOT SEE

This tool cannot guarantee that contract analysis catches all malicious patterns — sophisticated scams may use novel techniques not yet recognized. It cannot detect social engineering or off-chain fraud that occurs outside the contract itself. It cannot verify that contract behavior matches its bytecode after deployment, as contracts can be self-destructing or upgradeable. It cannot ensure that token distribution data reflects current ownership, as transfers happen constantly. The AI analysis is based solely on the data provided — it cannot detect off-chain intent, future contract upgrades, or social engineering attacks. Always verify critical information independently and never rely solely on automated analysis for financial decisions. The AI explanation is generated and does not replace reviewing what you are actually signing in your wallet.

PLEASE NOTE

New Token Launch Risk Score supports EVM-compatible blockchains only (Ethereum Mainnet, Goerli, Polygon, Arbitrum One, Optimism, BNB Smart Chain, and others via network selection). The tool requires a free API key for the AI bridge. The tool never submits transactions or signs messages; it is strictly read-only. The AI explanation is generated after reading on-chain data — always review your wallet's signature prompt before approving any transaction. Analysis depth affects the amount of data examined; deeper analysis may take slightly longer.