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.
CISA KEV Adds SharePoint RCE CVE-2026-45659 After Active Exploits
Nepal Unveils First “Hall of Fame” for Ethical Hackers
900+ Oracle E-Business instances Exposed Online
India asks WhatsApp not to roll out ‘username’ feature over fraud concerns
Azure CLI Password Spray Impacts 78 Microsoft Accounts in 81M+ Attempts
Chrome Update Patches 382 Vulnerabilities, Including 15 Critical
Apple fixes more than 30 iOS, macOS, and Safari flaws
Attackers exploit critical flaw in Oracle E-Business
WhatsApp to allow usernames instead of phone numbers
Linux Unveils New Open Source Security Project “Akrites” For (OSS) Ecosystem
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.
