Anonymous Crypto Trader Loses $50M USDT to Scam

Anonymous Crypto Trader Loses $50M USDT to Scam

Anonymous trader loses $50 million USDT in address poisoning scam. Funds laundered via Tornado Cash.
Key Points:
  • $50 million USDT lost to address poisoning.
  • Funds quickly laundered through Tornado Cash.
  • No broader market impact noted.

A crypto trader suffered a $50 million USDT loss due to an address poisoning scam on December 20, 2025, following withdrawals from Binance.

The incident highlights vulnerabilities within crypto transactions, emphasizing ongoing risks as $3.4 billion has been lost in such scams throughout 2025.

Article

An anonymous crypto trader lost $50 million USDT to an address poisoning scam that occurred after a 50 USDT test transaction, highlighting the vulnerability of using predictable address characters in blockchain transactions.

The trader’s wallet, active for two years, primarily handled USDT transfers and had just withdrawn funds from Binance. Following the scam, an on-chain message offered a $1 million bounty for returns, underscoring the severity of the situation.

The scam affected only the individual trader, with the stolen funds converted from USDT to DAI, then ETH, and laundered via Tornado Cash. “The similarity between both addresses, which shared the same first 3 characters and last 4 characters,” notes Cos, Founder of Slowmist, showing the pitfalls of predictable transaction patterns (source).

Crypto observers noted that this trend is not isolated; address poisoning scams have caused significant financial losses in 2025, exceeding $3.4 billion across tens of thousands of wallets and victims.

No statements from prominent crypto leaders or organizations addressed the event, leaving the community with limited commentary and official input.

Insights suggest continued vigilance against address poisoning scams is crucial, with past events showing substantial financial repercussions. Future technological solutions could reduce such vulnerabilities, but historical data indicates scammers exploit predictable transaction patterns.