Personal data of 210,000 Gangnam Unni users leaked, including procedure and hospital details
Translated from Korean and summarized by DistantNews. Read the original for the full story.
At a glance
- Healing Paper said an attempted unauthorized access to an API linked to consultation records exposed personal information belonging to more than 210,000 Gangnam Unni users.
- The company said it blocked the access routes, conducted a full system inspection and strengthened authentication and detection measures after repeated attempts.
- The incident has drawn criticism because users reportedly could not access the companyโs page for checking what information had been leaked.
The personal information of more than 210,000 users of Gangnam Unni, a plastic-surgery and beauty information platform, was reportedly exposed in a hacking attack. The leaked details included procedure names, hospital information and registered photographs.
Healing Paper, the platformโs operator, said on the 7th that it detected abnormal access attempts on the API connected to consultation records on the 4th. The company said it immediately blocked the route and took emergency security measures across the system.
An incident occurred in which some customer personal information was leaked after abnormal access was attempted on the API linked to consultation-record inquiries.
The same attacker tried to gain access again through another route on the following day, the company said. Healing Paper said it blocked that route as well and completed a full inspection of its systems. It also said it had strengthened authentication, improved detection of abnormal access and revised its authorization checks.
The company voluntarily reported the leak to the Korea Internet and Security Agency. However, its response has come under criticism because the page intended to let users check the details of the breach could not be accessed.
We immediately blocked the route and conducted a full inspection of the entire system.
Originally published by Dong-A Ilbo in Korean. Translated, summarized, and contextualized automatically by DistantNews, with a note on how the source frames the story. Not individually reviewed before publishing. How this works.