> For the complete documentation index, see [llms.txt](https://whitepaper.gopluslabs.io/goplus-network/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://whitepaper.gopluslabs.io/goplus-network/web3-security/deepscan.md). # DeepScan ## Overview DeepScan is GoPlus' AI-powered token security audit product. It helps projects, developers, launchpads, exchanges, wallets, and communities evaluate token contracts before users are exposed to risk. By combining static analysis, AI-driven semantic review, financial risk modeling, and GoPlus' accumulated security rule patterns, DeepScan produces professional token security reports with fast delivery. DeepScan extends GoPlus Web3 Security from transaction protection into token-level pre-deployment and pre-listing security. It helps projects identify contract vulnerabilities, scam patterns, access-control issues, rug pull risks, and honeypot behavior before those risks reach users or liquidity markets. ## Why Token Audit Matters Tokens are one of the most common entry points for Web3 users. A token contract can look simple from the outside while hiding privileged functions, transfer restrictions, minting risks, proxy upgrade risks, or mechanisms that can drain liquidity or prevent users from selling. Traditional audits are often expensive, slow, and hard to access for early-stage projects. Simple automated scanners can catch basic issues but often miss deeper logic flaws and financial attack patterns. DeepScan is designed to make token security audit faster, more accessible, and more intelligence-driven. ## Core Capabilities ### Smart Contract Vulnerability Detection DeepScan detects common smart contract vulnerabilities, including reentrancy risks, integer overflow or underflow, unchecked external calls, unsafe logic, and other structural flaws that may harm users or projects. ### Rug Pull Risk Detection DeepScan identifies mechanisms that can be used to drain funds or undermine user trust, including hidden mint functions, ownership risks, proxy upgrade dangers, privileged liquidity controls, and unsafe administrative permissions. ### Access Control Analysis DeepScan audits privileged functions, admin roles, multisig requirements, ownership status, and role-based access patterns to uncover centralization and abuse risks. ### Honeypot and Scam Detection DeepScan detects hidden transfer restrictions, sell blockers, trading traps, malicious token behavior, and known scam patterns that may prevent users from exiting positions. ### AI-Powered Semantic Audit DeepScan uses large language models to improve semantic understanding of contract logic, helping identify complex vulnerabilities and intent-level risks that basic static rules may miss. ### Financial Semantic Modeling DeepScan applies financial scenario modeling for DeFi and tokenomics-specific risks, including fund flow risk, economic attack vectors, liquidity behavior, and token control design. ### Graph-IR Static Analysis DeepScan performs deep static analysis using graph-based representations of smart contract control flow and data flow, enabling structural detection of vulnerabilities and risky logic paths. ## DeepScan in the GoPlus Security Layer DeepScan is part of GoPlus Web3 Security and complements the rest of the GoPlus security layer: * **GoPlus Intelligence** provides security data, rule patterns, AI analysis, and token risk intelligence. * **SafeToken Protocol** helps projects create and manage tokens with safer templates and liquidity controls. * **Security RPC, infrastructure integrations, and on-chain firewall** protect users when they interact with tokens and contracts. * **AgentGuard** can protect AI agents that review, deploy, or interact with token contracts before they execute risky actions. Together, these components form a lifecycle approach to token security: audit before deployment, safer issuance and liquidity management, real-time risk intelligence, and transaction protection during user interaction. ## Use Cases **Token projects** Audit token contracts before launch, identify high-risk design issues, and provide security transparency to users and partners. **Launchpads and listing platforms** Screen token projects before listing or launch to reduce rug pull, scam, and honeypot exposure. **Wallets and dApps** Use DeepScan reports and GoPlus Intelligence to provide clearer token risk information to users. **Exchanges and market data platforms** Evaluate token contract risks before listing, indexing, or surfacing token data. **AI-assisted Web3 development** Use DeepScan alongside AgentGuard when AI agents help write, review, deploy, or modify token contracts. ## Conclusion DeepScan brings AI-powered audit capability into the GoPlus Web3 Security product line. It helps projects detect token risks before launch, gives platforms a faster way to screen assets, and strengthens the broader GoPlus mission of protecting high-risk digital actions before they execute. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://whitepaper.gopluslabs.io/goplus-network/web3-security/deepscan.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. 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