This past June, a scam artist attempted to trick a group of potential bidders out of 30,000 bitcoins during an auction by the U.S. Marshals Service. It all started when the Marshals Service accidentally sent out an email to a group of possible bidders, part of the Silk Road bust. After that accidental email was leaked to the press, a scammer got a hold of the list of people the email was sent to, and sent all of them a phishing scheme in the form of a fake email.
The email consisted of a letter from someone going by the name, Linda Johnson, who claimed to be working for Bitfilm Production. The email asked the recipients to participate in an interview regarding the Silk Road coin auction. Anyone who fell for the scam and responded to the email would then be sent a second email with a Google Doc claiming to be a list of interview questions. However, when the link was clicked-on, the hacker was able access that person’s personal email accounts and passwords.
Unfortunately one of the recipients, Bitcoin Reserve, a bitcoin arbitrage fund from Australia, fell for the scam. Co-founder Sam Lee clicked on the “Google Doc” link, allowing the scammer to access his email contacts. The scammer used that list to send a fake email to employees from Lee’s account asking them to send 100 bitcoins to an address. It was too late by the time they realized it was a fake email from a hacker.
Neither Marshals Service nor Bitfilm Production had any involvement or knowledge of the scam until they were contacted after the scam had been discovered. They urge that anyone who thinks they are a victim of the phishing scam to contact the FBI.
Phishing scams like this one take place all the time and are an unfortunate consequence of our society’s growing reliance on technology. This is why cybercrime insurance is extremely important for businesses today. Cybercrime insurance works to protect your business from hackers and data breaches. Let’s hope the victim of this scam, Bitcoin Reserve, has a good cybercrime policy to adequately mitigate their losses.
