nomaannxx01-cloud[.]github[.]io

nomaannxx01-cloud.github.io is flagged as a generic phishing domain targeting cloud service credentials, specifically leveraging GitHub Pages for hosting. This domain is part of a campaign designed to mimic legitimate cloud platforms, likely aiming to harvest Microsoft, Google, or AWS login details from unsuspecting users. The threat actor employs a drainer kit tailored for cloud credential theft, often redirecting victims to fake login portals that closely resemble official cloud service interfaces. The infrastructure suggests a low-cost, high-reach approach, exploiting GitHub’s free hosting to evade immediate takedowns while maintaining operational flexibility.

This domain exhibits several concrete technical indicators that solidify its malicious classification. PhishDestroy identifies that nomaannxx01-cloud.github.io resolves to IP 185.199.108.153 and is flagged by 8 out of 95 VirusTotal security vendors. The domain is registered through GitHub, Inc., utilizing a Let’s Encrypt SSL certificate to appear legitimate. It appears on 1 security blocklist and is specifically blocked by OpenPhish, a leading phishing intelligence feed. While the exact creation date is not provided, the active status and recent flagging suggest a recently deployed campaign tailored for rapid exploitation before potential takedowns.

Currently, nomaannxx01-cloud.github.io remains active, posing an elevated risk to users who may encounter it through phishing emails, social media, or spoofed cloud service notifications. The domain’s reliance on GitHub Pages complicates immediate mitigation, as GitHub’s abuse response processes can introduce latency. Users are advised to avoid interacting with this domain and verify any cloud service login prompts through official channels. Organizations should ensure their security stacks include real-time phishing intelligence feeds like OpenPhish to preemptively block such domains. The remaining risk is elevated due to the domain’s active status, reliance on trusted hosting providers, and the potential for rapid iteration to evade detection.