Nvidia is cementing its presence in the autonomous vehicle space by introducing a new artificial intelligence platform designed to help cars make decisions in complex, real-world conditions. The move reflects the company’s broader strategy to take AI beyond digital tools and embed it into physical systems that operate in public environments.
The platform, named Alpamayo, was introduced by Nvidia chief executive Jensen Huang during a keynote address at the Consumer Electronics Show in Las Vegas. According to the company, the system is built to help self-driving vehicles reason through situations rather than simply respond to sensor inputs. This approach is intended to improve safety, particularly in unpredictable traffic conditions where human judgment is often required.
Nvidia says Alpamayo enables vehicles to manage rare driving scenarios, operate smoothly in dense urban settings, and provide explanations for their actions. By allowing a car to communicate what it intends to do and why, the company aims to address long-standing concerns around transparency and trust in autonomous driving technology.
As part of this effort, Nvidia confirmed a collaboration with Mercedes-Benz to develop a fully driverless vehicle powered by the new platform. The company stated that the vehicle is expected to launch first in the United States within the next few months, followed by expansion into European and Asian markets.
Although Nvidia is widely known for the chips that support today’s AI boom, much of the public focus has remained on software applications such as generative AI systems. Industry attention is now shifting toward physical uses of AI, including vehicles and robotics, where decision-making errors can have serious consequences.
Huang noted that Nvidia’s work on autonomous systems has provided valuable insight into building large-scale robotic platforms. He suggested that physical AI is approaching a turning point similar to the rapid rise of conversational AI tools in recent years.
A demonstration shown at the event featured a Mercedes-Benz vehicle navigating the streets of San Francisco without driver input, while a passenger remained seated behind the wheel with their hands off. Nvidia explained that the system was trained using human driving behavior and continuously evaluates each situation before acting, while also explaining its decisions in real time.
Nvidia also made the Alpamayo model openly available, releasing its core code on the machine learning platform Hugging Face. The company said this would allow researchers and developers to freely access and retrain the system, potentially accelerating progress across the autonomous vehicle industry.
The announcement places Nvidia in closer competition with companies already offering advanced driver-assistance and autonomous driving systems. Industry observers note that while achieving high levels of accuracy is possible, addressing rare and unusual driving scenarios remains a major technical hurdle.
Nvidia further revealed plans to introduce a robotaxi service next year in partnership with another company, although it declined to disclose the partner’s identity or the locations where the service will operate.
The company currently holds the position of the world’s most valuable publicly listed firm, with a market capitalization exceeding 4.5 trillion dollars, or roughly £3.3 trillion. It briefly became the first company to reach a valuation of 5 trillion dollars in October, before losing some value amid investor concerns that expectations around AI demand may be inflated.
Separately, Nvidia confirmed that its next-generation Rubin AI chips are already being manufactured and are scheduled for release later this year. The company said these chips are designed to deliver strong computing performance while using less energy, which could help reduce the cost of developing and deploying AI systems.
Security researchers have identified a new category of Android malware that uses artificial intelligence to carry out advertising fraud without the user’s knowledge. The malicious software belongs to a recently observed group of click-fraud trojans that rely on machine learning rather than traditional scripted techniques.
Instead of using hard-coded JavaScript instructions to interact with web pages, this malware analyzes advertisements visually. By examining what appears on the screen, it can decide where to tap, closely imitating normal user behavior. This approach allows the malware to function even when ads frequently change layout, include video content, or are embedded inside iframes, which often disrupt older click-fraud methods.
The threat actors behind the operation are using TensorFlow.js, an open-source machine learning library developed by Google. The framework allows trained AI models to run inside web browsers or server environments through JavaScript. In this case, the models are loaded remotely and used to process screenshots taken from an embedded browser.
Researchers from mobile security firm Dr.Web reported that the malware has been distributed through GetApps, Xiaomi’s official application store. The infected apps are mainly games. In several cases, the applications were initially uploaded without harmful functionality and later received malicious components through software updates.
Once active, the malware can run in what researchers describe as a “phantom” mode. In this mode, it opens a hidden browser based on Android’s WebView component. This browser loads a webpage containing advertisements and a JavaScript file designed to automate interactions. The browser operates on a virtual screen that is not visible to the device owner. Screenshots of this screen are repeatedly captured and sent to the AI model, which identifies relevant ad elements and triggers taps that appear legitimate.
A second operational mode, referred to as “signalling,” gives attackers direct control. Using WebRTC technology, the malware streams a live video feed of the hidden browser to the threat actor. This allows them to perform actions such as tapping, scrolling, or entering text in real time.
Dr.Web identified multiple infected games hosted on Xiaomi’s platform, including titles with tens of thousands of downloads. Beyond official app stores, the malware has also been found in modified versions of popular streaming applications distributed through third-party APK websites, Telegram channels, and a Discord server with a large subscriber base. Many of these apps function as expected, which reduces user suspicion.
Although this activity does not directly target personal data, it still affects users through increased battery drain, higher mobile data usage, and faster device wear. For cybercriminals, however, covert ad fraud remains a profitable operation.
Security experts advise Android users to avoid downloading apps from unofficial sources and to be cautious of altered versions of well-known apps that promise free access to paid features.
Cybersecurity analysts are raising concerns about a growing trend in which corporate cloud-based file-sharing platforms are being leveraged to extract sensitive organizational data. A cybercrime actor known online as “Zestix” has recently been observed advertising stolen corporate information that allegedly originates from enterprise deployments of widely used cloud file-sharing solutions.
Findings shared by cyber threat intelligence firm Hudson Rock suggest that the initial compromise may not stem from vulnerabilities in the platforms themselves, but rather from infected employee devices. In several cases examined by researchers, login credentials linked to corporate cloud accounts were traced back to information-stealing malware operating on users’ systems.
These malware strains are typically delivered through deceptive online tactics, including malicious advertising and fake system prompts designed to trick users into interacting with harmful content. Once active, such malware can silently harvest stored browser data, saved passwords, personal details, and financial information, creating long-term access risks.
When attackers obtain valid credentials and the associated cloud service account does not enforce multi-factor authentication, unauthorized access becomes significantly easier. Without this added layer of verification, threat actors can enter corporate environments using legitimate login details without immediately triggering security alarms.
Hudson Rock also reported that some of the compromised credentials identified during its investigation had been present in criminal repositories for extended periods. This suggests lapses in routine password management practices, such as timely credential rotation or session invalidation after suspected exposure.
Researchers describe Zestix as operating in the role of an initial access broker, meaning the actor focuses on selling entry points into corporate systems rather than directly exploiting them. The access being offered reportedly involves cloud file-sharing environments used across a range of industries, including transportation, healthcare, utilities, telecommunications, legal services, and public-sector operations.
To validate its findings, Hudson Rock analyzed malware-derived credential logs and correlated them with publicly accessible metadata and open-source intelligence. Through this process, the firm identified multiple instances where employee credentials associated with cloud file-sharing platforms appeared in confirmed malware records. However, the researchers emphasized that these findings do not constitute public confirmation of data breaches, as affected organizations have not formally disclosed incidents linked to the activity.
The data allegedly being marketed spans a wide spectrum of corporate and operational material, including technical documentation, internal business files, customer information, infrastructure layouts, and contractual records. Exposure of such data could lead to regulatory consequences, reputational harm, and increased risks related to privacy, security, and competitive intelligence.
Beyond the specific cases examined, researchers warn that this activity reflects a broader structural issue. Threat intelligence data indicates that credential-stealing infections remain widespread across corporate environments, reinforcing the need for stronger endpoint security, consistent use of multi-factor authentication, and proactive credential hygiene.
Hudson Rock stated that relevant cloud service providers have been informed of the verified exposures to enable appropriate mitigation measures.