Crypto Hack Attack Victim Creates SIM Swapping Support Project
StopSIMCrime wants to educate crypto holders about SIM Swapping, to help them in filing lawsuits, and to put pressure on telecom operators.
A project for helping victims of SIM swapping has been established in the United States by recent crypto hacking victim Robert Ross. Called StopSIMCrime, the initiative aims to educate holders of digital assets about SIM swapping as well as to provide them with legal support, Vice News reported on Tuesday.
One of the project’s key goals is to advocate for the creation of laws that must oblige telecom operators to stop SIM swapping attempts as carriers are reluctant to prevent those hacks, Ross said. He was a client of AT&T when criminals stole $1 million from his accounts in two crypto exchanges, Coinbase and Gemini. The sum was the savings for his two daughters’ college education. After the accident, Ross decided to establish the StopSIMCrime.
“I really believe this is being enabled by the carriers,” Ross said.
StopSIMCrime also helps victims to connect with REACT - a special task force for tech crimes in California. After investigations by REACT, US authorities have filed 21 felony charges related to cryptocurrency hacks by Nicholas Truglia, a famous SIM swapping hacker. Truglia was the person that hacked Ross phone.
“A portion of your donation may be used to provide funding for lawsuits against the carriers and criminals,” the project explains on its donation page.
Because the carriers’ Terms of Service do not allow for legal class actions, each victim must take action individually, which may benefit the victim individually. For that reason, we are not able to get tax-exempt status.”
SIM swapping has become a major tactic for stealing cryptocurrencies. The hack happens when criminals gain access to a mobile phone carrier and transfer the victim’s phone number to another SIM card. This operation gives the hacker control of the SMS system used by many virtual asset trading operators who try to remain secure via two-factor identification.