DeFiTrust: A transformer-based framework for scam DeFi token detection . . . However, none of the existing work has employed sentiment analysis for DeFi scam token detection In this work, we utilize a pre-trained BERT model to analyze the public’s sentiment about DeFi tokens and incorporate that sentiment to identify scam tokens
Blockchain Scam Detection: State-of-the-Art, Challenges, and Future . . . Therefore, many researchers have studied the detection methods for blockchain scams On the basis of introducing the mainstream types of scams, including Ponzi scheme, Phishing scam, Honeypot, and Pump and dump, this paper provides a thorough survey on the detection methods for these scams, in which 48 studies are investigated
Scam Token Detection Based on Static Analysis Before . . . - Springer Therefore, detecting scam token contracts before deployment is desirable In order to overcome the above issues, we propose a scam token contract detection system based on static analysis Unlike transaction and liquidity data, code-based data, such as bytecodes and opcodes, can be obtained before the deployment of token contracts to blockchain
Detecting Scam Tokens and Backdoor Functions in EVM Based Networks Scam tokens often leverage weaknesses in smart contracts, decentralized exchanges, or token issuance platforms to exploit user trust This research focuses on the early detection and classification of these fraudulent smart contracts before they can affect any ecosystem user
Trade or Trick? Detecting and Characterizing Scam Tokens on Uniswap . . . Then, we propose a hybrid approach for flagging scam tokens and scam liquidity pools on Uniswap accurately (see Section 4) We manually label a scam token benchmark dataset, and identify features that can be used to distinguish them Our detection approach is powered by a guilt-by-association based expanding
Scam Token Detection Based on Static Analysis Before Contract . . . In this paper, we propose a scam token detection system based on static analysis In order to detect scam tokens before deployment, we utilize code-based data, such as bytecodes and opcodes, because they can be obtained before contract deployment
DeFi Wallet Scams In 2026: Key Risks And Real Cases In 2026, the average delay between compromise and detection was 16 8 hours, by which point tokens were already swapped, bridged, and laundered While some victims report their crypto losses in DeFi wallet scam threads across forums and community platforms, recovery success remains extremely low
The Detection of Scams on the Ethereum Blockchain Machine learning-based detection helps to identify more scammers and scam tokens based on transactions on Uniswap Interestingly, they found thousands of collusion addresses to help carry out the scams in league with the scam token pool creators Four kinds of collusion addresses can be seen in [Fig 14]
Token Scan - CertiK Instant token security checks to detect scams and make better decisions
5 Tools to Help You Detect and Stay Safe From Crypto Scams - MUO Token Sniffer uses various techniques to identify fraudulent tokens and scams, including smart contract code analysis, checks for known scams, scanning for red flags about the project, etc By using Token Sniffer, you can protect yourself from scams, make informed investment decisions and contribute to the security of the blockchain ecosystem
Detecting Scam Tokens and Backdoor Functions in EVM Based Networks Scam tokens often leverage weaknesses in smart contracts, decentralized exchanges, or token issuance platforms to exploit user trust This research focuses on the early detection and classification of these fraud-ulent smart contracts before they can affect any ecosystem user
Token Sniffer – AI-Powered Scam Detection Across 15 Blockchains Token Sniffer is a cutting-edge scam detection tool, enabling users to identify fraudulent tokens, prevent rug pulls, and enhance DeFi security By integrating multi-chain fraud monitoring, automated smart contract scanning, and community-driven security intelligence, Token Sniffer sets the standard for next-generation blockchain protection