Websites taken offline worldwide after critical PHP flaws uncovered
publishers 28articles 28first reported 21 Oct, 17:00 UTCdeveloping since 21 Oct · 9 editions
Government agencies and companies around the world took websites offline Wednesday after cybersecurity firms disclosed two critical vulnerabilities in PHP, the programming language underlying a large share of the web.
The flaws could allow attackers to take control of vulnerable servers without passwords, according to the South China Morning Post. Wiz, one of the security firms involved in the discovery, estimated that PHP runs about 70% of the world’s websites and described the vulnerabilities as among the most severe found in PHP. Because PHP supports widely used web-design platforms, potentially affected sites include online stores, news organizations and government services.
France 24 reported that artificial intelligence was used to uncover the flaws and that PHP developers released a patch Wednesday. The U.S. National Security Agency advised organizations to apply the patch immediately or take vulnerable systems offline, the South China Morning Post reported.
Sky News reported that a major technology company had recorded “millions” of attempts to exploit a critical flaw since it emerged. Government and corporate shutdowns were reported across multiple countries as administrators raced to secure affected systems.
HOW THIS STORY WAS MADE
Written from 3 articles, 1 with the publisher's own text; 3 independent newsrooms once syndicated copies count as one; this version written 12 h after the record first saw the story; the editor kept it as written.
- Publishers
- 28
- Source articles
- 28
- Given to the writer
- 3, 1 with the article's own text
- Independent newsrooms
- 3
- This version written
- 12 h after the record first saw the story
- Second model (editor)
- kept as written
- Publication gate
- passed
- Human review
- none
WHAT THE EDITOR DID
The second model checked the writer's draft against the same sources and published it as written.
Written by a language model from the sources above, then checked by a second model that may only cut, attribute or correct. How it works →