Cybersecurity researchers are cautioning users against installing certain browser extensions that claim to improve ChatGPT functionality, warning that some of these tools are being used to steal sensitive data and gain unauthorized access to user accounts.
These extensions, primarily found on the Chrome Web Store, present themselves as productivity boosters designed to help users work faster with AI tools. However, recent analysis suggests that a group of these extensions was intentionally created to exploit users rather than assist them.
Researchers identified at least 16 extensions that appear to be connected to a single coordinated operation. Although listed under different names, the extensions share nearly identical technical foundations, visual designs, publishing timelines, and backend infrastructure. This consistency indicates a deliberate campaign rather than isolated security oversights.
As AI-powered browser tools become more common, attackers are increasingly leveraging their popularity. Many malicious extensions imitate legitimate services by using professional branding and familiar descriptions to appear trustworthy. Because these tools are designed to interact deeply with web-based AI platforms, they often request extensive permissions, which exponentially increases the potential impact of abuse.
Unlike conventional malware, these extensions do not install harmful software on a user’s device. Instead, they take advantage of how browser-based authentication works. To operate as advertised, the extensions require access to active ChatGPT sessions and advanced browser privileges. Once installed, they inject hidden scripts into the ChatGPT website that quietly monitor network activity.
When a logged-in user interacts with ChatGPT, the platform sends background requests that include session tokens. These tokens serve as temporary proof that a user is authenticated. The malicious extensions intercept these requests, extract the tokens, and transmit them to external servers controlled by the attackers.
Possession of a valid session token allows attackers to impersonate users without needing passwords or multi-factor authentication. This can grant access to private chat histories and any external services connected to the account, potentially exposing sensitive personal or organizational information. Some extensions were also found to collect additional data, including usage patterns and internal access credentials generated by the extension itself.
Investigators also observed synchronized publishing behavior, shared update schedules, and common server infrastructure across the extensions, reinforcing concerns that they are part of a single, organized effort.
While the total number of installations remains relatively low, estimated at fewer than 1,000 downloads, security experts warn that early-stage campaigns can scale rapidly. As AI-related extensions continue to grow in popularity, similar threats are likely to emerge.
Experts advise users to carefully evaluate browser extensions before installation, pay close attention to permission requests, and remove tools that request broad access without clear justification. Staying cautious is increasingly important as browser-based attacks become more subtle and harder to detect.
A newly identified cyberattack campaign is actively exploiting trust in India’s tax system to infect computers with advanced malware designed for long-term surveillance and data theft. The operation relies on carefully crafted phishing emails that impersonate official tax communications and has been assessed as potentially espionage-driven, though no specific hacking group has been confirmed.
The attack begins with emails that appear to originate from the Income Tax Department of India. These messages typically warn recipients about penalties, compliance issues, or document verification, creating urgency and fear. Victims are instructed to open an attached compressed file, believing it to be an official notice.
Once opened, the attachment initiates a hidden infection process. Although the archive contains several components, only one file is visible to the user. This file is disguised as a legitimate inspection or review document. When executed, it quietly loads a concealed malicious system file that operates without the user’s awareness.
This hidden component performs checks to ensure it is not being examined by security analysts and then connects to an external server to download additional malicious code. The next stage exploits a Windows system mechanism to gain administrative privileges without triggering standard security prompts, allowing the attackers deeper control over the system.
To further avoid detection, the malware alters how it identifies itself within the operating system, making it appear as a normal Windows process. This camouflage helps it blend into everyday system activity.
The attackers then deploy another installer that adapts its behavior based on the victim’s security setup. If a widely used antivirus program is detected, the malware does not shut it down. Instead, it simulates user actions, such as mouse movements, to quietly instruct the antivirus to ignore specific malicious files. This allows the attack to proceed while the security software remains active, reducing suspicion.
At the core of the operation is a modified banking-focused malware strain known for targeting organizations across multiple countries. Alongside it, attackers install a legitimate enterprise management tool originally designed for system administration. In this campaign, the software is misused to remotely control infected machines, monitor user behavior, and manage stolen data centrally.
Supporting files are also deployed to strengthen control. These include automated scripts that change folder permissions, adjust user access rights, clean traces of activity, and enable detailed logging. A coordinating program manages these functions to ensure the attackers maintain persistent access.
Researchers note that the campaign combines deception, privilege escalation, stealth execution, and abuse of trusted software, reflecting a high level of technical sophistication and clear intent to maintain prolonged visibility into compromised 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.
Security researchers have identified a weakness in the web-based dashboard used by operators of the StealC information-stealing malware, allowing them to turn the malware infrastructure against its own users. The flaw made it possible to observe attacker activity and gather technical details about the systems being used by cybercriminals.
StealC first surfaced in early 2023 and was heavily promoted across underground cybercrime forums. It gained traction quickly because of its ability to bypass detection tools and extract a wide range of sensitive data from infected devices, including credentials and browser-stored information.
As adoption increased, the malware’s developer continued to expand its capabilities. By April 2024, a major update labeled version 2.0 introduced automated alerting through messaging services and a redesigned malware builder. This allowed customers to generate customized versions of StealC based on predefined templates and specific data theft requirements.
Around the same time, the source code for StealC’s administration panel was leaked online. This leak enabled researchers to study how the control system functioned and identify potential security gaps within the malware’s own ecosystem.
During this analysis, researchers discovered a cross-site scripting vulnerability within the panel. By exploiting this weakness, they were able to view live operator sessions, collect browser-level fingerprints, and extract session cookies. This access allowed them to remotely take control of active sessions from their own systems.
Using this method, the researchers gathered information such as approximate location indicators, device configurations, and hardware details of StealC users. In some cases, they were able to directly access the panel as if they were the attacker themselves.
To prevent rapid remediation by cybercriminals, the researchers chose not to publish technical specifics about the vulnerability.
The investigation also provided insight into how StealC was being actively deployed. One customer, tracked under an alias, had taken control of previously legitimate video-sharing accounts and used them to distribute malicious links. These campaigns remained active throughout 2025.
Data visible within the control panel showed that more than 5,000 victim systems were compromised during this period. The operation resulted in the theft of roughly 390,000 passwords and tens of millions of browser cookies, although most of the cookies did not contain sensitive information.
Panel screenshots further indicated that many infections occurred when users searched online for pirated versions of widely used creative software. This reinforces the continued risk associated with downloading cracked applications from untrusted sources.
The researchers were also able to identify technical details about the attacker’s setup. Evidence suggested the use of an Apple device powered by an M3 processor, with both English and Russian language configurations enabled, and activity aligned with an Eastern European time zone.
The attacker’s real network location was exposed when they accessed the panel without a privacy tool. This mistake revealed an IP address associated with a Ukrainian internet service provider.
Researchers noted that while malware-as-a-service platforms allow criminals to scale attacks efficiently, they also increase the likelihood of operational mistakes that can expose threat actors.
The decision to disclose the existence of the vulnerability was driven by a recent increase in StealC usage. By publicizing the risk, the researchers aim to disrupt ongoing operations and force attackers to reconsider relying on the malware, potentially weakening activity across the broader cybercrime market.
Security researchers have dismantled a substantial portion of the infrastructure powering the Kimwolf and Aisuru botnets, cutting off communication to more than 550 command-and-control servers used to manage infected devices. The action was carried out by Black Lotus Labs, the threat intelligence division of Lumen Technologies, and began in early October 2025.
Kimwolf and Aisuru operate as large-scale botnets, networks of compromised devices that can be remotely controlled by attackers. These botnets have been used to launch distributed denial-of-service attacks and to route internet traffic through infected devices, effectively turning them into unauthorized residential proxy nodes.
Kimwolf primarily targets Android systems, with a heavy concentration on unsanctioned Android TV boxes and streaming devices. Prior technical analysis showed that the malware is delivered through a component known as ByteConnect, which may be installed directly or bundled into applications that come preloaded on certain devices. Once active, the malware establishes persistent access to the device.
Researchers estimate that more than two million Android devices have been compromised. A key factor enabling this spread is the exposure of Android Debug Bridge services to the internet. When left unsecured, this interface allows attackers to install malware remotely without user interaction, enabling rapid and large-scale infection.
Follow-up investigations revealed that operators associated with Kimwolf attempted to monetize the botnet by selling access to the infected devices’ internet connections. Proxy bandwidth linked to compromised systems was offered for sale, allowing buyers to route traffic through residential IP addresses in exchange for payment.
Black Lotus Labs traced parts of the Aisuru backend to residential SSH connections originating from Canadian IP addresses. These connections were used to access additional servers through proxy infrastructure, masking malicious activity behind ordinary household networks. One domain tied to this activity briefly appeared among Cloudflare’s most accessed domains before being removed due to abuse concerns.
In early October, researchers identified another Kimwolf command domain hosted on infrastructure linked to a U.S.-based hosting provider. Shortly after, independent reporting connected multiple proxy services to a now-defunct Discord server used to advertise residential proxy access. Individuals associated with the hosting operation were reportedly active on the server for an extended period.
During the same period, researchers observed a sharp increase in Kimwolf infections. Within days, hundreds of thousands of new devices were added to the botnet, with many of them immediately listed for sale through a single residential proxy service.
Further analysis showed that Kimwolf infrastructure actively scanned proxy services for vulnerable internal devices. By exploiting configuration flaws in these networks, the malware was able to move laterally, infect additional systems, and convert them into proxy nodes that were then resold.
Separate research uncovered a related proxy network built from hundreds of compromised home routers operating across Russian internet service providers. Identical configurations and access patterns indicated automated exploitation at scale. Because these devices appear as legitimate residential endpoints, malicious traffic routed through them is difficult to distinguish from normal consumer activity.
Researchers warn that the abuse of everyday consumer devices continues to provide attackers with resilient, low-visibility infrastructure that complicates detection and response efforts across the internet.