Back to Paper
cs.CRcs.CY
Local ID: 2603.24625v2
AI Summary: gemma4:e4b
From Hype to Collapse: Investigating Rug Pull Scams on Solana
By Jiaxin Chen, Ziwei Li, Zigui Jiang, Ruihong He, Yantong Zhou, Jiajing Wu, Zibin Zheng
Revision History Timeline
v13/25/2026
3/25/2026
No submitter comment provided.
v25/31/2026
5/31/2026
No submitter comment provided.
★ Version indexed in ExplorerComparing v1 vs v2
Green = Added • Red = Removed
Title Comparison
SolRugDetector:From Hype to Collapse: Investigating Rug PullsPull Scams on Solana
Authors Comparison
No author changes.
v1 Comment
No comment for this version.
v2 Comment
No comment for this version.
Abstract Word Diff
Solana has experienced rapid growth due to its high performance and low transaction costs, but the extremely low barrier to token issuance has also led toenabled widespread Rug Pulls. Unlike Ethereum-based Rug PullsPulls, thatwhich often rely on malicious smartsmart-contract contracts,logic, theSolana's unified SPL Token program on Solana shifts fraudulent behaviorsexecution toward on-chain operations such as marketbehavioral manipulation. However, existing research has not yetsystematically conductedexamined athese systematicSolana-specific analysisRug ofPull thesepatterns, specificand no public Solana Rug Pull patternsdataset onis Solana.available Infor empirical research. To bridge this paper,gap, we present a comprehensivelarge-scale empiricalmeasurement study of Rug Pulls on Solana. Based on 68We real-worldmanually incidentverify reports,68 wecommunity-reported constructincidents and releasecurate a manually labeled datasetbenchmark containingof 117 confirmed Rug Pull tokens andtokens, characterizefrom thewhich workflowwe ofdistill Rugthree Pullsrepresentative onon-chain Solana.behavioral Buildingpatterns: onFreeze thisAuthority analysis,Abuse, weLiquidity proposeWithdrawal, SolRugDetector,and aPump-and-Dump. detectionGuided systemby thatthese identifiespatterns, fraudulentwe tokensdesign solelya usingbehavior-guided on-chaincandidate transactionidentification and statehuman-validation data.pipeline. ExperimentalWe resultsapply showthis thatpipeline SolRugDetectorto outperforms100,063 existingtokens toolsnewly issued on theOrca, labeledRaydium, dataset.and WeMeteora furtherduring conductthe afirst large-scalehalf measurementof on2025, 100,063identifying tokens76,469 newlyRug issuedPull intokens. theA firstrandom halfmanual audit of 2025382 andsamples identifyestimates 76,469a Ruglabeling Pullfalse-positive tokens.rate Afterof validating0.26\%, supporting the in-the-wildreliability detectionof results,the wedataset. We release thisthe resulting dataset and analyzeuse it to characterize the Solana Rug Pull ecosystem on Solana.ecosystem. Our analysis revealsshows that Rug Pulls on Solana exhibit extremely short lifecycles, strong price-driven dynamics, severe economic losses, and highly organized group behaviors. These findings provide new insights into the Solana Rug Pull landscape and support the development of effective on-chain defense mechanisms.