AI-Generated Browser Ransomware Abuses Chromium API on Windows and Android

Cybersecurity researchers have flagged a new malware artifact generated using DeepSeek that constructed a novel attack path combining ‘unrealistic browser-malware concepts with a real browser capability’ to turn it into a working ransomware technique that runs entirely inside the browser on both Windows and Android devices.

‘This is the first documented case where a frontier AI model independently bridged the gap between a theoretical browser-only ransomware risk and a practical, working attack chain – surfacing a novel attack path that defenders had previously dismissed as unfeasible due to browser sandboxing limits,’ Check Point said in a statement shared with The Hacker News.

The identified sample is a Python Flask application named ‘deepseek_python_20260125_da0631.py’ that was uploaded to VirusTotal on January 25, 2026, with the Google-owned malware scanning service describing it as a ‘fully functional information stealer and ransomware toolkit.’ It has been named InfernoGrabber v9.0 by the malware author.

The application is designed to operate as a malicious web server that lures victims with a fake Discord avatar AI upscaler, while stealthily running a wide array of harmful actions, including stealing Discord tokens, harvesting credit card numbers and cryptocurrency seed phrases, logging keystrokes, and capturing unauthorized webcam and microphone feeds.

‘The code includes specific routines for browser exploitation (targeting CVEs like CVE-2023-4863), data exfiltration via a hard-coded Discord webhook, a ransomware ‘WinLocker’ screen demanding Bitcoin, and an administrative dashboard for the attacker to manage stolen data,’ according to VirusTotal.

The findings come as artificial intelligence and large language models (LLMs) are redefining the cyber threat landscape, enabling threat actors to abuse the technology to develop malware and exploits. The use of DeepSeek is noteworthy as it signals that the Chinese company’s models have lower refusal rates for malicious cyber requests when compared to its Western counterparts from Anthropic, Google, or OpenAI.

The Python malware is an instance of what’s called In-Browser Ransomware that implements a browser-native technique not encountered in real-world campaigns in the past. The attack technique entails using a phishing decoy to trick a user into granting file system access to a web page, which then enumerates local files in the selected folder, reads and exfiltrates their contents, encrypts and overwrites them, and finally displays an extortion note to the victim.

What makes this more unusual is that all of this can be accomplished without installing a native payload, exploiting a browser vulnerability, or requiring root access. The approach is limited to web browsers that expose the picker-based File System Access API, including Google Chrome and other Chromium-based browsers across Windows and Android operating systems.

There is no evidence that the browser-native ransomware pattern has been abused in the wild. Another troubling aspect of AI-assisted development is that it not only lowers the barrier for bad actors to generate offensive code, but also the fact that they do not even need to know such a file system access API exists in the first place, or have the technical expertise to abuse it.