SlowMist Blockchain Security and Anti-Money Laundering Report: Key Findings for H1 2025

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Introduction

The first half of 2025 witnessed significant evolution in blockchain security threats and regulatory challenges. Despite technological advancements, the industry faced:

As blockchain security pioneers, SlowMist presents this report analyzing critical incidents, regulatory trends, and AML developments to help industry professionals strengthen risk management.


Blockchain Security Landscape

Incident Overview

H1 2025 recorded 121 security incidents totaling $2.37B in losses:

๐Ÿ‘‰ Explore real-time threat data

Key Attack Vectors:

  1. Compromised accounts (42 cases)
  2. Smart contract exploits (35 cases)
  3. Novel phishing techniques (EIP-7702 abuse)
  4. Deepfake social engineering (AI-synthesized executives/KOLs)

Emerging Fraud Trends

Threat TypeCharacteristicsImpact
Telegram Safeguard scamsClipboard malware via fake airdropsWidespread device compromise
Malicious browser extensionsDisguised as Web3 security toolsCredential theft + remote access
LinkedIn recruitment phishingTailored attacks against engineersCode injection + data exfiltration
AI tool backdoorsCompromised npm packages (e.g., sw-cur)4,200+ developers affected

Anti-Money Laundering Developments

Regulatory Highlights

Notable Case: KiloEX Attack Resolution

Mixer Activity (H1 2025)

ServiceETH DepositedUSD ValueWithdrawals
Tornado Cash254,094$605M248,922 ETH
eXch28,756$82MCeased operations April 30

Conclusion

Key industry shifts include:

  1. Targeted attacks replacing broad exploits
  2. "Compliance-as-gateway" becoming standard
  3. Improved recovery rates through advanced tracking
  4. Regulatory maturity in major jurisdictions (US, EU, Hong Kong)

The report demonstrates blockchain's transition toward institutional-grade security frameworks while highlighting persistent vulnerabilities in hot wallets and social engineering.


FAQ

Q: How does 2025 attack frequency compare to previous years?
A: Fewer incidents (121 vs. 223 in H1 2024) but higher per-event impact (+65.94% losses).

Q: What makes Deepfake scams particularly dangerous?
A: AI-generated videos/audio of trusted figures bypass traditional verification methods with >90% accuracy.

Q: Which chains saw most attacks?
A: Ethereum ($38.59M losses), Solana ($5.8M), and BSC ($5.49M) were top targets.

Q: How effective are current fund recovery efforts?
A: SlowMist-assisted recoveries reached $14.56M, with industry-wide recovery rates improving to 11.38%.

๐Ÿ‘‰ For institutional-grade security solutions