Hackers third crack at Boulder uni servers

By
Follow google news

Hackers have hit a Colorado university’s servers for the third time in under two months.

Up to 49,000 people registered at the university may have had their details illegally accessed. The news arrives weeks after SC reported University of Colorado, in Boulder had been hit twice in two weeks.


The university only realized it had been hit for a third time when it started making improvements to its existing system.

"It was through the work we have been doing as a result of the earlier breaches that we discovered this incident," said Dan Jones, information technology security coordinator at the university. "Discovery of this computer access is a direct result of the risk assessment we are in the process of implementing, which ultimately will improve security for campus servers that contain the most sensitive data."

University investigators believe the attack probably occurred at around the same time as the two previous breaches. The data accessed contained names, addresses and Social Security numbers, but the university does not believe that any data was copied.

"The university is very concerned about computer security and we're working hard to ensure our policies and security measures promote a high degree of confidentiality," Bobby Schnabel, vice provost for academic and campus technology told local newspaper Rocky Mountain News.

www.colorado.edu

Add iTnews as your trusted source

Got a news tip for our journalists? Share it with us anonymously here.
Copyright © SC Magazine, US edition
Tags:

Most Read Articles

Poor WA gov M365 security led to $71k theft and children's data breached

Poor WA gov M365 security led to $71k theft and children's data breached

US medical device maker Stryker's Microsoft environment attacked

US medical device maker Stryker's Microsoft environment attacked

Health and Aged Care CISO retires

Health and Aged Care CISO retires

Services Australia describes fraud, debt-related machine learning use cases

Services Australia describes fraud, debt-related machine learning use cases

Log In

  |  Forgot your password?