Tether Blacklists $31.4 Million Over Alleged FTX Hack, Musk Responds

On the night of Nov. 11, multiple FTX-linked wallet addresses were found transferring millions of dollars worth of cryptocurrencies without official notice, sparking speculation ranging from FTX’s commencement of bankruptcy proceedings to the involvement of pirates. Within hours, FTX confirmed on Telegram that the money transfers were part of an ongoing hack.

FTX Telegram admin confirms it was hacked. Source: Telegram

Following FTX’s confirmation on Telegram about the hack, as noted above, Tether proactively blacklisted $31.4 million worth of USDT tokens related to the transactions. As underline by blockchain investigator ZachXBT, the blacklisted USDT tokens consisted of $3.9 million USDT on Avalanche (AVAX) and $27.5 million USDT on Solana (SOL).

Billionaire entrepreneur Elon Musk, who recently bought Twitter in hopes of unleashing the platform’s full potential, credited Twitter’s contribution in tracking FTX developments in real time.

Elon Musk is interested in FTX CEO Sam Bankman-Fried. Source: Twitter

By blacklisting the allegedly stolen USDT token, Tether prevented hackers from siphoning assets to another account or exchanging them for other cryptocurrencies. As part of the fix, Tether may burn blacklisted USDT and reissue equal amounts of the asset to the original owner.

However, the hacker also stole many other crypto assets, including Ethereum (ETH) Chainlink (LINK) and USDP, which have yet to see the intervention of the respective ecosystems.

Related: FTX-Binance Standoff Highlights Need for Clear Rules – Sen. Lummis

Over the past few days, major crypto exchanges including Binance, OKX, Kucoin, and Crypto.com have pledged to share their proof of reserve to regain investor confidence.

Leading this campaign, Bitfinex CTO Paolo Ardoino shared 135 cold and hot wallet addresses revealing proof of Bitfinex reserves.

While this move was welcomed by investors, a few community members pointed to Bitfinex’s lack of accountability figures, making the data incomplete for review.