You can't ask most models to help you make "ransomware" directly, but many will be more than willing if you give them the right prompt. DeepSeek and other LLMs with fewer safety and security controls make theoretical cyberthreats - like browser-only ransomware - much more likely to be used in real-world infections, according to Check Point researchers. The Israeli cybersecurity company analyzed a DeepSeek-generated sample in a Wednesday report that its threat hunters describe as in-browser ransomware. Over the past year, the team has tracked almost 3,000 files attributed to DeepSeek, and classified nearly half (1,383 files) as malicious or dangerous using VirusTotal or static source analysis. “Within this dataset, we found a sample that implemented a dangerous browser-native technique we have not observed exploited in the wild,” researcher Alexey Bukhteyev wrote. And while the sample was incomplete, and unable to pull off an in-the-wild infection, the security shop’s testing showed “little effort” would be required to make it attack-ready. “Our research shows that the original incomplete DeepSeek sample can be transformed into a fully functional attack with minimal effort,” Pedro Drimel Neto, malware analysis team leader at Check Point Research, told The Register. “Very little effort is needed,” Neto said. “Low-level expertise is sufficient. You don't need to be a sophisticated cybercriminal or advanced persistent threat group. In fact, we've already observed evidence of actual threat actors attempting this attack using straightforward LLM prompts.” Known threat gets an AI boost The risk ransomware poses to browsers isn’t a new idea. The File System Access specification lists ransomware as a security consideration, and a 2023 USENIX Security paper on Ransomware over Modern Web Browsers described how File System Access API could be abused to encrypt local files from a malicious web application. The File System Access API is a browser capability, primarily supported by Chrome and Chromium-based browsers, that allows developers to build web applications, such as editors, IDEs, and creative tools, that can read, write, and manage files on the user’s local device. “Even though it can be used to develop rich web applications, it greatly extends the attack surface, which can be abused by adversaries to cause significant harm,” Google’s Güliz Seray Tuncay and Florida International University researchers Harun Oz, Ahmet Aris, Abbas Acar, Leonardo Babun and Selcuk Uluagac wrote in 2023, long before LLMs could develop working malware and attack chains. What’s new, according to Check Point, is that an AI model put these previously documented ideas into a “realistic and enforceable attack scenario leveraging a method that defenders had originally thought was unfeasible due to browser sandboxing limits: a DeepSeek-attributed malicious sample, generated as an all-in-one malware fantasy, connected this documented platform risk to a realistic phishing-style web application, demonstrating a viable end-to-end attack chain.” This technique is especially appealing to attackers because it doesn’t require a native payload, APK installation, browser exploit, or root access to a compromised device. Instead, it uses social engineering - tricking a user into clicking on a malicious button - combined with a legitimate permission prompt exposed by the File System Access API in Chrome. Meet InfernoGrabber 9000 This particular sample that Check Point uncovered is a Python Flask application that targets Android users. It’s named InfernoGrabber 9000, and VirusTotal calls it a “fully functional information stealer and ransomware toolkit.” While the security sleuths don’t have the prompt submitted to DeepSeek to produce the malware, they speculate it was something along the lines of: “create a universal malicious tool that runs through the browser and collects as much victim data as possible, encrypts files, and demands ransom. In a single front-end, the generated code assembled routines and stubs for keylogging, clipboard monitoring, form and network-request interception, Discord-token collection, crypto-wallet and payment-card discovery, geolocation requests, webcam and microphone access, screenshots, local-file access, Chrome exploit stubs, ‘persistence,’ and a ransomware-style overlay.” To be clear: the sample doesn’t actually do all of this. “A more accurate reading is that it is an AI-generated blueprint in which the model tried to translate familiar capabilities of native stealers and ransomware tools into a web page opened in the browser,” Bukhteyev wrote. The code presents a victim-facing lure disguised as a Discord avatar AI upscaler. Clicking on the lure is intended to execute a slew of silent, harmful actions that run entirely inside the browser process. These include stealing Discord tokens, harvesting credit card numbers and cryptocurrency seed phrases, logging keystrokes, and capturing unauthorized webcam and microphone feeds. The code also includes specific routines for browser exploitation (such as targeting CVE-2023-4863), uses a hardcoded Discord webhook for data exfiltration and displays a ransomware WinLocker screen demanding Bitcoin. The good news for defenders is that the sample was incomplete, and the browser's built-in security model successfully prevents most of this functionality. However, Check Point was able to create a working proof-of-concept for the browser-native attack using the latest DeepSeek model V4. The team had to remove some of the more explicit terms - like ransomware - from the prompt, but ultimately produced the same functionality: “a web page that asks the user for access to local files, processes them inside the browser, and leaves the user unable to recover the original content.” AKA: browser-only ransomware. Neto told us that this type of LLM-generated code and in-browser attack is “likely happening now.” “We expect to see this activity in the short term, if we haven't already,” he added. While traditional ransomware and extortion groups target enterprises and critical infrastructure organizations, as opposed to Android-device users, which was the focus of this research, “we have seen increased end-user ransomware activity recently,” Neto said. “What's most concerning is that code obfuscation used in these attacks makes them difficult to spot, so there's a real possibility that attacks using this technique are already occurring in the wild but going unnoticed.” ®
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