The Rug Pull Report

Everything you need to know about crypto and DeFi scams

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The Rug Pull Report

Everything you need to know about crypto and DeFi scams
Solidus Labs Research
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What is wash trading, and why does it happen in crypto?

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Scam tokens deployed per year

Rug pulls and DeFi scams explained

Over the past five years, rug pulls have become endemic in decentralized finance (DeFi), with scam token developers stealing billions of dollars from millions of retail investors. In our inaugural Rug Pull Report, we share original data, research, and case studies to help you understand this emerging money laundering problem.

Download the report now to learn:

  • Why the vast majority of rug pulls have evaded detection under traditional approaches to scam identification
  • The seven types of scam tokens, how they steal from investors, and how many users they've harmed
  • How token smart contract scanners can help regulators, blockchain platforms, and crypto companies combat money laundering

Go to the full analysis
Trusted by compliance teams and regulators globally

Built for Data Complexity

Normalizes non-standard feeds and on/off-ramp data into one real-time schema, while crypto-native models cut through volatility to flag wash trades, spoofing, and insider flow, even amid extreme price swings or cross-venue price gaps.

Future-Proofed for Evolving Crypto-Specific Schemes

Monitors manipulation across both on- and offchain, from insider trading on DEXs, cross-venue schemes spanning spot/derivatives or CeFi/DeFi to native onchain threats throughout the asset life cycle.

Real-Time Intervention Before Risk Escalates

Real-time alerting surfaces risk instantly, enabling timely intervention in a global, 24/7 “always-on” market and instant settlement that legacy batch processing can’t offer

See Risk Across Trades, Transactions & KYC

Unifies trades, transactions, KYC, and behavioral signals in one view—uncovering risks siloed systems miss like account takeovers, new-account scams, and transactions that appear legitimate in isolation but raise suspicion when analyzed against broader trading behavior.

Crowd-Driven Crypto Sentiment Intelligence

Machine-learning sentiment analysis distills signals from messy, unstructured data across Reddit, X, Telegram, and news sources – flagging symbol-level sentiment shifts in real time.

Venue-Agnostic Data Architecture

Venue-agnostic approach to market data delivers high speed and scalability across all digital and traditional asset classes, with fallback mechanisms for uninterrupted surveillance even without full order-book depth.
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