InfoSecBulletin Cybersecurity for mankind
Security researchers found that datasets used by companies to develop large language models included API keys, passwords, and other sensitive credentials.
Large language models are dominating the online landscape, with companies promoting AI solutions that claim to solve all problems.
Apple Warns 99 Countries iPhone Users of Mercenary Spyware Attacks
Daily Cyber security update for 31.10. 2026
NetamLoader malware to attack Win10: Customized for Gov.t in Asia
‘SMS of Death’ Zero-Click Flaw Disrupting Android Phones With a Single Text
NIST Unveiled Guidance for Post-Quantum Cryptography Transition
Daily Cyber security update for 29.10. 2026
Rapid7 Unveils 17 Zimbra Collaboration Suite Flaws Breaking Identity Boundaries
CISA launched two free tools Identify Security Misconfig in Microsoft 365 and Google Workspace
Apple iOS 27.2 Released Fixes 163 Flaws, Including Critical WebKit RCE
CISA adds PHP2Shell to KEV, PHP Releases New Security Updates to Fix Critical HTTP2Shell flaw
For an AI to be effective, it needs extensive training data, much of which is gathered from the Internet by specialized companies and organizations.
Common Crawl provides datasets for companies to train their AI, gathering information from the internet, which may include sensitive data.
Researchers from Truffle Security discovered that credentials, API keys, and passwords are being exposed. The main issue is that some web developers hardcode sensitive information into websites, which then ends up in LLM training data.
Researchers discovered 11,908 live secrets, such as API keys and passwords, across 2.76 million websites.
“Leaked keys in Common Crawl’s dataset should not reflect poorly on their organization; it’s not their fault developers hardcode keys in front-end HTML and JavaScript on web pages they don’t control. And Common Crawl should not be tasked with redacting secrets; their goal is to provide a free, public dataset based on the public Internet for organizations like Truffle Security to conduct this type of research,” explained the researchers.
Companies that create LLMs have warned against hardcoding sensitive information on websites. They advise avoiding this practice, as users may unintentionally share the code in their work, worsening the issue.
