Have you ever tried to open an “encrypted” email or an urgent document, only to realize with horror that your usual DOCX attachment is actually a file with the extension .docx.exe? Or maybe you received an email supposedly with an invoice, contract, or internal memo attached — only for the file to prove something entirely different from what it claimed to be? If so, you were likely targeted in a malware infection attempt.

Cybercriminals routinely disguise malicious files as harmless documents and archives, banking on recipients blindly clicking without checking the extension. Kaspersky experts analyzed the file formats most frequently deployed in malicious email blasts to reveal what really lurks behind these extensions — and how attackers weaponize them in their campaigns.

Important note: the extensions we’ll be discussing here are routinely used for completely legitimate files. For example, Windows executables normally end in .exe. However, we’re focusing specifically on scenarios where attackers disguise or spoof a file’s extension to pass it off as a totally different type of file.

What extensions are most commonly found in malware?

Our experts analyzed malicious email blasts from the beginning of 2026 to pinpoint the 15 most common dangerous file extensions.

.exe

.js

.html

.dll

.bat

.vbs

.xls

.pdf

.jse

.au3

.docx

.htm

.wsf

.scr

.lnk

Let’s take a look at what files with these extensions actually do under the hood.

Executable files

An executable file is a compiled computer program that’s ready to run. Once launched, a malicious executable can download additional payloads, alter system settings, steal user data, connect your device to external attacker-controlled servers, and much more. These are the most common executable extensions found in malicious email campaigns:

• .exe
• .dll
• .com
• .scr

.exe

The classic Windows executable extension. It powers every program you use daily, from web browsers and games to office suites and software installers.

By the way, attackers often wrap EXE malware in double extensions: invoice.docx.exe, report.pdf.exe, or even photo.jpg.exe. They take advantage of a default Windows setting: hiding extensions for known file types. Because this setting is turned on by default, users only see the first part of the file name — invoice.docx, report.pdf, or photo.jpg — and assume it’s just a normal document or image. But the second you open this trap file, the malware fires up.

.dll

Another common extension abused in malicious campaigns is .dll (dynamic-link library). These libraries hold functions that Windows programs frequently require while running, such as printing a document. This modular architecture prevents redundant code by letting multiple applications call on the exact same library for specific tasks. However, if an attacker replaces a legitimate library with an infected one, running any normal program that calls on it can trigger malicious code.

.com

While files with the .com extension have absolutely nothing to do with the web domain of the same name, cybercriminals likely count on victims mistaking these files for links in an unusual format. In reality, it’s a legacy Windows executable format.

While modern versions of Windows no longer rely on this file type, the operating system can still run and execute it, which makes opening one a very bad idea.

.scr

SCR files are screensavers — those idle Windows screen animations featuring abstract patterns, the iconic bouncing logo, or whatever else. Despite their harmless reputation, screensavers are essentially executables just like EXE files: once opened, they can install extra components or alter system settings all the same. In malicious campaigns, these files routinely masquerade as images, screenshots, or documents.

Scripts

Scripts are text files containing a sequence of commands that a computer runs automatically in order. They can download files, install and launch programs, modify security settings, and plenty more. Email attackers rely on the following script types most often:

• .js and .jse
• .bat
• .cmd
• .vbs
• .wsf
• .vbe
• .au3

.js and .jse

The .js and .jse extensions are used by JavaScript files. Most users associate JavaScript strictly with websites and don’t realize these files can run locally on their computer as standalone programs.

We recently wrote about CrystalX RAT, a remote access Trojan that logs keystrokes, steals clipboard contents, and injects malicious components into web browsers upon installation. Its JS scripts monitored cryptocurrency wallet addresses and silently swapped them with the attacker’s own to hijack transactions in real time.

.cmd and .bat

BAT and CMD files are designed to automate tasks in Windows and can execute virtually any command in the operating system. Launching a file like this can infect your computer with malware.

.vbs

Visual Basic Script files with the .vbs extension are a long-time favorite among cybercriminals. Attackers frequently use double extensions to disguise .vbs files as plain text documents, such as invoices, app installation guides, or emails. For example, in the early 2000s, the iconic ILOVEYOU worm spread via a VBS script, infecting millions of computers worldwide. Victims received an email with the subject line “ILOVEYOU”, but instead of a sweet confession, the attachment contained a malicious script. At its peak, this “romantic” worm had infected up to 10% of all internet-connected computers.

Web files

This category mainly includes .html, .htm, .hta and .svg files. Often these are phishing pages in disguise, designed to mimic login forms for email, cloud services, banking apps, and other platforms. Naturally, any credentials entered on these fake pages go straight to the attackers hunting for your accounts and money. Web files can also trigger malicious scripts and download additional payloads onto your system.

Archives

Cybercriminals routinely package malicious files inside archives (most commonly having .zip, .rar, and .7z extensions). These archives are often password-protected or encrypted to hinder content scanning. Additionally, attackers sometimes create intentionally corrupted archives with damaged data structures: basic security filters often stumble over broken files and skip scanning them altogether, whereas archive managers can automatically repair and unpack the contents anyway.

That’s why we recommend using advanced security solutions that can spot phishing emails and prevent you from launching dangerous files hidden inside archives.

PDF files (.pdf)

To many, PDF feels like a completely harmless file format — after all, what could go wrong with flight tickets, documents, and slide decks? But beyond static text and images, PDFs can host clickable links, forms, and even scripts.

Malicious code in a PDF is usually tucked away behind visual elements or embedded as hidden text. What’s more, the risk comes not just from the file itself, but also from the software you use to view it: for instance, attackers spent months exploiting a vulnerability in Adobe Acrobat that allowed them to execute malicious PDF files remotely. If you regularly work on PDF documents, make sure the JavaScript feature is disabled in your Adobe Acrobat. To do this, go to Preferences → JavaScript in the menu.

By the way, heavy users of AI agents and chatbots should keep in mind that AI frequently falls for prompt injections: hidden commands buried inside images, text, and web pages that the AI interprets as legitimate instructions, and executes without your knowledge.

LNK shortcuts (.lnk)

Files with the .lnk extension are standard Windows shortcuts that we use every day for quick access to apps and folders. By themselves, shortcuts don’t contain any software — they simply tell Windows what file to open or what command to run.

However, an attacker can give a shortcut any icon and any name they want, completely hiding its real destination. The moment a victim clicks the shortcut, the infection chain kicks off. The malicious code usually executes silently in the background, leaving virtually no visual clues that anything went wrong.

Cybercriminals also like to hide LNK files behind double extensions — betting that users won’t look close enough to see what kind of file they’re actually opening. Here’s a classic example:

Your best bet is to avoid clicking incoming LNK files altogether: no one in their right mind has a reason to email you a desktop shortcut.

By the way, if you haven’t updated Windows in a while, we’ve bad news for you: malicious code can automatically execute without you ever clicking the LNK — simply opening the folder where it lives is enough to trigger it. Microsoft finally released a patch for this long-abused vulnerability in December 2025. So don’t put off system updates — install them regularly to patch up the security holes attackers rely on.

Microsoft Office documents

.xls and .xlsx

The file extension .xls is used by older versions of Microsoft Excel. Millions of people use Excel spreadsheets every day, and many are still running outdated software, which makes XLS files a favorite bait for cybercriminals. Budgets, employee rosters, financial records, urgent reports, and other spreadsheets stop being harmless the moment a user clicks Enable Content, allowing the program to run macros — custom Microsoft Office scripts and commands built into the files. Once allowed, the malicious macro gets free rein to download files, launch programs, harvest system data, and alter settings.

Unlike XLS, XLSX is the modern Excel format, which doesn’t support embedded macros by default. Unfortunately, that doesn’t guarantee complete safety: the spreadsheet can still pull live data from external sources and automatically connect to web destinations in the background.

.docx

Although modern DOCX files don’t support macros by default, attackers have long figured out other ways to weaponize these documents. Typically, malicious DOCX files contain embedded links or instructions that force Word to contact external servers and pull down malicious payloads. Back in 2023, for instance, researchers uncovered a Microsoft Office vulnerability that allowed attackers to trigger built-in Windows system tools and download malware straight from the web.

To reduce your risk when working with Microsoft Office apps, we recommend turning on Protected View, blocking external connections, and disabling all macros without notification.

Macro support in modern Microsoft Office

Macros are sequences of commands that automate complex, repetitive tasks in Excel, Outlook, Word, and PowerPoint. In modern versions of Microsoft Office, documents containing embedded macros are saved in specialized file formats with extensions like .xlsm, .xltm, .xlsb, .docm, and .pptm. While Microsoft Office includes built-in security controls — such as blocking files downloaded from the web and warning you about potentially dangerous macros — if you don’t actually use macros and have no specific reason to run them, treat files with these extensions with caution.

How to check if a file is safe

• Use a comprehensive security suite that automatically scans files for malware, prevents infections, blocks visits to suspicious sites, and completely removes malicious files from your devices. You can take advantage of a 30-day trial of Kaspersky Premium if you urgently need to disinfect your computer or scan a file for viruses.
• Never open attachments from unknown senders. Curiosity can cost you dearly, since many malware strains specifically hunt for credit card and crypto wallet data. Let the Mail Anti-Virus feature included in our security solutions do the heavy lifting: it automatically filters out malicious or suspicious emails and protects you from phishing attacks.
• Examine incoming emails carefully: cybercriminals have become adept at disguising malicious messages. Always verify email addresses against official ones and make sure you know the signs of phishing and spam. Attackers might try to scare you with claims of a hacked account, lure you to shady sites with “unbeatable deals”, or urge you to review a work document attached to the email. Any attempt to manipulate your emotions is a major red flag that someone is trying to trick you.
• Never open files with double extensions. It’s almost always a trap.
• If you run into a file with an unfamiliar extension, take a moment to look it up online. Remember: a photo or plain text document will never have an .lnk or .vbs extension.

Check out our other posts on email and file security:

• Spam 101: what is spam, and how to defeat it
• Phishing 101: what to do if you get a phishing email
• Attackers disguising phishing as Google AppSheet notifications
• Checking QR codes in emails
• How to safely convert files

For security researchers studying unconventional side-channel attacks, acoustic keylogging is something of a Hello World: a foundational problem that’s been tackled many times. A recent paper authored by researchers across three Japanese universities cites six previous studies on the topic that date as far back as 2004. While earlier experiments showed theoretical promise, they came with real-world caveats so severe that it made them all but impractical for actual espionage. The authors of this latest study, however, claim to have overcome most of those limitations. Today, we look at how they pulled it off, and assess whether their method holds up in real-world scenarios.

What makes this new approach different?

Previous acoustic keylogging techniques were fundamentally flawed. Best-case scenarios required prior training on the target’s specific keyboard model. Worst-case scenarios required a complex microphone array to isolate the subtle acoustic differences between keystrokes. Crucially, almost all prior models failed outside silent environments, which rendered the attack vector virtually useless.

The Japanese research team demonstrated reliable keystroke interception even if the target was sitting nearby in a public space, sound was being recorded in an online meeting, or the researchers were using a contact microphone to eavesdrop through a wall. All this with strong model accuracy and a minimal training dataset. Their process needs a sample of just 150 to 200 keystrokes to reach a 99% accuracy rate for subsequent typing.

How to crack 200 keystrokes in under 50 iterations

To understand how the researchers achieved such high accuracy and adaptability, we have to look at their audio processing pipeline. Their analysis begins by automatically segmenting a raw recording into discrete keystrokes. This data is then passed through a specialized algorithm that simplifies the subsequent audio analysis. Next, the system clusters together acoustically similar signals. The assumption is that the members of one cluster map to the exact same key. One particularly intriguing takeaway was isolating the spacebar sound from all the rest. Because the spacebar produces a distinctly unique sound profile compared to other keys, identifying it provides reliable word boundaries. This streamlines the next phase: feeding the preprocessed acoustic data into specialized language models for inference.

Yes, the method relies on not one but two language models. The first model performs multiple passes over the audio stream to map acoustic signatures to potential keyboard characters. During each pass, the model leverages dictionaries to hypothesize character mapping, and check whether the resulting text aligns with standard words. The second model handles the final refinement pass: it ingests thoroughly pre-processed data rather than raw inputs. The method doesn’t stop there: unrecognized keystrokes undergo manual analysis, with analysts injecting educated guesses before re-running the recognition pipeline once again. The goal of looping through these multiple iterations is to achieve complete recognition across the keyboard from an ultra-compact dataset of ideally no more than 200 captured keystrokes. This marks a major shift from legacy methods, which relied on massive training datasets.

Research results

To validate their theoretical model, the researchers created an experimental testing setup:

The team tested four distinct laptop models, each producing unique acoustic keyboard signatures. Participants typed 2400 characters per experiment, with analysts extracting audio samples ranging from 50 to 400 keystrokes. Across all devices, the system reliably reconstructed typed text from a baseline sample of just 150 keystrokes or more. Recognition accuracy exceeded 80% at 150 keystrokes, and approached 100% once the sample reached 200 keystrokes.

The researchers achieved nearly identical results in field-like conditions with the microphone placed three meters away from the target device. Going a step further, the team successfully tested an even higher-friction scenario: using a specialized contact microphone to eavesdrop through a physical wall.

Under these conditions, accuracy dipped slightly for certain laptop models. Dell and Lenovo devices yielded roughly 80% accuracy over a 200-keystroke sample, while Apple and HP ones maintained nearly 100% recognition rates.

Remote interception during videoconferencing presented an additional variable: results depended on both the target laptop model and the specific web conferencing software used. Even so, most test scenarios yielded reliable character recognition, though a few edge cases required expanding the sample size from 200 to at least 250 keystrokes.

Reasonable critique

Despite these impressive results, the technique has clear limitations. First, all experiments were conducted strictly on lowercase English text. The target dataset was capped at just 29 characters: the standard alphabet, spacebar, period, and comma — even number keys were excluded. As a result, capturing randomized character strings like complex passwords remains a major hurdle. Yet these are precisely the targets threat actors care about most.

However, the Japanese research team didn’t ignore passwords. While recognition accuracy was predictably low, the authors proposed assessing success through a more realistic lens. For starters, attackers can almost always capture audio of other typing activity alongside password entry. This provides a stream of natural language with minimal special characters. Factoring this broader acoustic context into the password analysis significantly improves the odds of a successful guess.

Next, the researchers rightly noted that even a list of several candidate passwords increases the chances of compromising a target account. As shown in the graph above, when ample data (426 keystrokes) is captured, a short five-character password can be successfully cracked within 100 attempts with a 90% success rate. Naturally, longer passwords proved far more resistant to eavesdropping.

Despite its limitations, this study represents a major breakthrough in acoustic side-channel attacks. It demonstrates a highly practical threat scenario: an attacker captures a brief audio recording of typing activity, then uses iterative analysis and targeted manual adjustments to process and refine the data offsite.

While eavesdropping in a noisy restaurant or through a wall is likely difficult to scale, capturing keystrokes during virtual meetings for offline decoding represents a realistic attack scenario. Ultimately, this research offers a compelling proof-of-concept: modern algorithms can drastically improve the accuracy of acoustic reconnaissance.

The news coverage surrounding the breach of the AI platform Hugging Face by OpenAI’s autonomous agents reads like a scene straight out of a Terminator movie (apparently, a prequel). Yet for enterprise security teams — even those that don’t develop or deploy artificial intelligence — the detailed incident report published by Hugging Face provides a critical operational roadmap. The incident needs to be evaluated on the basis of a single, fundamental question: “Which organizational security flaws, missing controls, configurations, or technologies allowed this attack to succeed?”

What happened?

During cyber-capability testing of OpenAI’s advanced AI models against the ExploitGym benchmark, an agent found a way to break out of its confinement, reached the internet, and breached the infrastructure of several companies — one of them being Hugging Face. The model reasoned that it could find solutions to the benchmark challenges on these external systems. Dozens of articles have since covered these incidents in detail, so no need to go over that here. What truly matters is what transpired within Hugging Face’s systems between July 9 and 13 while the AI agent operated unchecked inside. The Hugging Face incident report includes an interactive attack timeline, so we’ll just list the key stages. A malicious configuration in a dataset uploaded by the agent allowed it to exfiltrate credentials for a worker pod, and execute code within it. The agent then exploited cloud environment metadata and escaped the privileged pod to obtain root-level access on the host. It retrieved a substantial number of secrets from storage and used a stolen VPN key and shared cluster administrator credentials to pivot deeper into the internal network and source code repository before the Hugging Face security team blocked its access.

How significant is the fact that this was next-generation AI?

Regardless of the specific area of ​​activity of a company, tomorrow it may become a target of human attackers, fully autonomous systems, or AI agents manually controlled by human hackers.

Many AI practitioners have rushed to dismiss this incident based on assumptions that OpenAI will never allow its tech to get into serious hacking. However, security researchers have argued for months that even currently available open-source models can be used to launch both offensive research and real-world attacks. Human oversight, while still a requirement, can be minimal. Even mid-tier models can easily iterate through a dozen vulnerabilities, inventory a compromised infrastructure, and quickly figure out how to expand their reach across a network. Defenders must analyze incidents like this in detail, map the key findings to their own environments, and adapt security controls to counter both stealthy human actors and rapid, noisy AI agents.

Three distinguishing features of AI-powered attacks

The techniques detailed in the Hugging Face report are not novel — the agent didn’t invent anything new. However, three factors regarding the attack’s speed and scale fundamentally shift the economics for both attackers and defenders:

• Trial and error becomes significantly cheaper. Where a human hacker might test five attack vectors, an agent can iterate through five hundred. Scenarios that security teams previously dismissed as “theoretically possible, but impractical and unlikely” and placed them on the backlog can now become active threats.
• Attack execution and incident response windows shrink. High-speed attacks are not unheard of, while some ransomware operators achieve compromise within hours even without AI. However, this accelerated pace will likely become the new baseline in cybersecurity, as demonstrated by the agent obtaining admin privileges at Hugging Face within 13 hours.
• Numerous alerts and log entries generated by the agent’s trial-and-error attempts can both help and hinder attackers as well as defenders. This noise can serve both as an indicator for threat detection, and as camouflage to hide meaningful actions. It also acts as a natural bottleneck for incident response when the workflows are fully manual.

How to protect corporate infrastructure

Don’t ignore alerts from triggered defensive controls. This classic mistake — often highlighted by our incident response experts — was made at Hugging Face as well. Malware execution, port scanning, WAF triggers, requests to non-allowlisted resources, and failed authentication attempts are likely some of the event types that are blocked automatically. However, all these events are often categorized by SIEM systems as purely informational and routinely ignored. In the Hugging Face incident, numerous server-side request forgery (SSRF) attack attempts were successfully blocked over several days, yet no one batted an eyelash. Much like persistent human adversaries, the AI agent continuously retried until it succeeded. To detect such attacks, establish clear rules and triage workflows for defensive alert telemetry to ensure that priority automatically escalates when event volume spikes.

Investigate rapidly escalating error counts. Legitimate operations rarely generate hundreds of authentication failures within 10 minutes across four distinct systems. Analyze historical baselines, and generate medium or high-severity alerts depending on the deviation from normal error rates. As part of automated response protocols, you can temporarily block the user or system that is generating the anomalous activity, or enforce rate-limiting on them.

Configure escalation paths and conduct security team drills. Hugging Face’s correlation engine successfully detected the attack, yet it failed to grab the human operators’ attention. During security drills, organization must evaluate how quickly key personnel respond to critical incidents — even when these happen during off-hours such as at night on weekends.

Isolate services — internal ones included — that process untrusted input by design. The primary vulnerability at the start of the attack was a node configured to process machine learning datasets, which are widely known for potentially containing executable code. This node, capable of executing untrusted code, operated without a sandbox or adequate isolation controls. The risk extends beyond machine learning — to document conversion engines, image processing pipelines, user file upload handlers, and any other server-side processes that perform deserialization or template rendering. Any server-side execution of this nature must run under maximum feasible isolation, restricted from accessing external or internal network hosts beyond an absolute minimum, and ideally enclosed within a sandbox.

Restrict egress. Most organizational servers don’t require unrestricted connectivity to arbitrary external hosts. If implementing strict default-deny egress policies presents technical challenges, consider a streamlined approach: enforce DNS traffic resolution exclusively through your enterprise DNS server, and configure it to block known public services that are commonly abused as command-and-control infrastructure or exfiltration channels such as Pastebin or OneDrive. Similar outbound restrictions can also be enforced via an NGFW.

Avoid issuing large batches of secrets, or storing them as a single object. Even with secret managers in place, an attacker can gain broad access if a compromised account holds excessive permissions. In the Hugging Face breach, a single Kubernetes Secret object contained 136 keys. The fewer secrets issued, the lower the impact of a compromise — and the easier key-rotation becomes.

Avoid long-lived secrets and don’t store them in environment variables that are inherited by all child processes.

Isolate applications from metadata servers. Most cloud environments run services — such as the EC2 Instance Metadata Service — that issue credentials and system details upon request. Isolating these from arbitrary applications — via firewall rules or by enforcing IMDSv2 — eliminates the most direct path from a single application compromise to a full account takeover.

Audit server and application identities. Service accounts with broad access, OAuth tokens, cross-environment keys, and other artifacts granting excessive privileges are most frequently found in server and app identities rather than human user accounts.

Track usage of credentials at anomalous entry points. In the Hugging Face attack, credentials assigned to an internal host were leveraged from an external one. Detection rules that flag these anomalies are straightforward to implement and deliver high-fidelity, low-noise signals of an attack that’s already progressed beyond its initial stages.

Use short-lived, single-use keys when provisioning new devices and services. During the incident, a single key was used to register 181 separate devices without triggering a single alert.

Mac users have historically trusted their operating system to keep them safe. That peace of mind mostly comes from Apple’s strict control over its ecosystem, and the fact that macOS has historically faced fewer mass attacks than Windows. However, that doesn’t mean Macs are invulnerable: threats do exist, and new ones emerge all the time. Over just the past few weeks, security researchers have published reports on at least two new campaigns that target Apple devices.

The malware used in one of the campaigns has been dubbed CrashStealer, while the other is known as ClickLock. Both rely on different tricks to force users into entering their Mac password, which attackers then use to steal account credentials, crypto assets, documents, and much more. In today’s post, we take a close look at how CrashStealer operates — and how to avoid falling victim to it.

A videoconferencing app with CrashStealer inside

It was back in May 2026 that researchers spotted the first signs this malware was being developed, and by early July, they caught it operating in the wild. The malware earned its name because of its core mechanism: it disguises itself as the macOS built-in crash reporting tool (CrashReporter) while functioning as an infostealer designed to hijack sensitive data.

Researchers managed to trace one of the websites users visited to download the malware. The site poses as a legitimate platform for distributing the video conferencing tool Werkbit.

However, you can’t just visit the site and download the software. Before downloading, visitors are asked to enter a special meeting PIN. This setup likely allows the attackers to limit the distribution scope by targeting only specific, pre-selected victims. Exactly how the cybercriminals choose their targets and deliver the PIN remains unknown.

The “lucky” users with a code end up installing the initial malicious payload — named Werkbit Setup. Interestingly, it carries a valid Apple developer certificate and has successfully passed Apple’s notarization process — meaning it cleared the automated prescan for malicious code. As a result, the attackers manage to bypass the operating system’s built-in Gatekeeper defense. This allows the payload to launch without triggering the usual untrusted software warnings.

Once launched, Werkbit Setup first reaches out to GitHub. Researchers believe using this popular platform helps attackers blend in by making these initial network requests look far less suspicious to security tools. After retrieving instructions from a GitHub repository, the program connects directly to the attackers’ server to fetch CrashStealer itself.

The loader then saves the malware to a temporary macOS folder, launches it, and wipes most of the intermediate setup files. As a result, a fully functional infostealer is up and running within seconds of Werkbit Setup starting. By the way, the user never gets any videoconferencing app.

How CrashStealer works

Unlike the Werkbit Setup loader, the CrashStealer malware itself isn’t signed with an Apple developer certificate. To keep users from suspecting anything, the malware disguises itself as the built-in macOS crash reporting tool, CrashReporter, by using the exact same name, app identifier, and a similar icon.

Once launched, CrashStealer completes a sequence of steps to gain access to sensitive data, establish persistence in the system, and cover its tracks:

1. Remove metadata — including the attribute that flags the app as an internet download.
2. Display a fake system prompt asking for the user’s macOS password.
3. Use the previously captured credentials to gain access to Keychain, the built-in macOS password manager.
4. Check the computer for installed security tools and malware analysis software.
5. Collect saved browser passwords, cookies, Keychain contents, and data from other password managers and crypto wallets.
6. Encrypt the data it stole and prepare it for forwarding to the attackers’ server.
7. Create a copy of itself and establish persistence to launch automatically every time macOS boots.
8. Delete temporary files and other installation traces to make detection much harder.

Step two deserves a closer look. The password prompt that the user sees looks extremely convincing. What’s more, the malware immediately verifies whether the credentials are correct: if you make a typo and enter an invalid password, CrashStealer will pop the window right back up to ask you again.

What data is CrashStealer after?

CrashStealer’s hit list is massive. First and foremost, its operators target Keychain: the built-in macOS password manager where the system stores account credentials, cryptographic keys, certificates, tokens, and more.

Users of third-party password managers aren’t safe either: the malware steals data from 14 of these services, including 1Password, Bitwarden, LastPass, Dashlane, Keeper, KeePassXC, NordPass, Enpass, and RoboForm.

In addition, the malware collects all credentials and cookies stored in Chromium-based browsers — Chrome, Brave, Edge, Opera, Opera GX, Vivaldi, Chromium, and NAVER Whale — as well as Firefox. The attackers clearly have a strong interest in crypto assets: CrashStealer specifically targets data from 80 different crypto wallet extensions, including MetaMask, Phantom, Coinbase Wallet, Trust Wallet, Rabby, Exodus, Keplr, and Solflare.

Finally, the malware scans the Documents and Downloads folders to pick files that might be of interest to the cybercriminals. CrashStealer encrypts all the stolen data with the AES-256-GCM algorithm, ZIP’s it up, and sends it to the attackers’ server.

How to protect your device

The spike in attacks on macOS is a clear wake-up call: Apple users need to get proactive about their security. We recommend:

• Researching apps online before installing them
• Sticking to utilities from official app stores whenever possible
• Using a reliable security solution that blocks malicious websites and stops malware activity on your device
• Keeping all your credentials and banking details in a secure password manager. One option is Kaspersky Password Manager— which, notably, wasn’t listed among the apps targeted by CrashStealer

Kaspersky security solutions detect the malware described in this post and assigns to it the verdicts HEUR:Trojan-Downloader.OSX.Agent.gen and HEUR:Trojan-PSW.OSX.Agent.gen.

Sooner or later, every company faces the need to upgrade its infrastructure — whether to accommodate expansion, optimize processes, or keep up with the latest trends in IT. When such a decision is made, company management typically thinks first and foremost about broad changes at the platform level — assuming that applications can simply be reinstalled and everything will work as before. And to a certain extent, that’s actually true. In an era when corporate security could be ensured solely by antivirus software on workstations, that would have worked; however, modern cyberthreats require protection at a fundamentally different level.

In this post, we explore why platform migration should be discussed with the security team as well, and why simply “installing a program” isn’t enough to ensure modern-day protection.

Security is no longer just a single program

In the past, securing endpoints was enough to prevent most attacks. In today’s environment, endpoint protection is just one layer of cybersecurity. A modern extended detection and response (XDR) solution isn’t a single application installed on a computer; it’s a set of components that work in conjunction. They provide protection for workstations and servers, event logging and storing, infrastructure-level threat detection mechanisms, investigation tools, automated response, reporting, and asset management. Each component serves a specific purpose and places different demands on the hardware and platform.

For the event storage system, disk speed is the most critical factor. In a large company, the security system generates millions of records per day, and all of them must be written quickly and then retrieved just as quickly when needed. The bottleneck here usually isn’t processor capabilities, but rather how fast the system can read and write data.

Threat detection requires computing power. The system compares events in real time and determines what can be a sign of an attack, and what is normal operational activity.

Response time is critical for investigation tools. When an analyst investigates an incident and pieces together what happened, they work in real time, and every second of waiting adds to the overall response time to the attack.

What does this mean? When a company switches platforms, it’s not enough to simply verify that the company’s existing cybersecurity solutions support the new architecture. The answer “yes, it’s supported” is too vague. It’s essential to clearly understand exactly which parts of the cybersecurity architecture are supported — starting from which version, and what is still under development.

What to ask before it’s too late

To avoid unexpected complications during implementation and operation, we recommend asking the right questions in advance — during the planning phase. Here are those questions:

Which specific components have been tested on the new platform?

The question shouldn’t be phrased as “Is the platform supported?”; the compatibility of each part of the system should be verified. Different components of a modern information-security solution operate under different rules, which is why their compatibility is tested separately. A one-word “yes” answer should raise a red flag: a reputable vendor will always provide a detailed response — including a list of components and any caveats (where applicable).

Will there be sufficient resources to handle the company’s workload?

“It will run” and “it will handle needed data volume” are two fundamentally different statements. The former is verified in a lab on a test bench; the latter depends on how many events specific infrastructure generates per day, and how long the company is required to retain them under regulatory or internal policies. It makes sense to ask the vendor for recommendations on calculating resources based on specific data volumes rather than relying on general minimum resource requirements when planning.

Is the security solution up to date?

It’s always important to clearly understand which version of the cybersecurity solution supports the new platform. It’s not uncommon for different parts of the infrastructure to be protected by different builds of the same solution. Sometimes this is because new features didn’t seem particularly critical; other times it’s because the IT security team was waiting for a convenient time to update so as not to disrupt business processes. If the security team won’t verify right away that all builds already support the new platform, the IT department may suddenly find itself having to make urgent changes to the migration schedule because updating the security solutions will require its own maintenance window and approvals. It’s better to find this out at the start of the project rather than deep into the process.

Are there any licensing restrictions?

Sometimes the availability of certain features depends not only on the product version but also on the specific licenses the company has purchased. It’s more cost-effective to clarify this during the design phase.

None of these questions require in-depth technical knowledge; a project manager can easily ask them. But when asked at the right time, they eliminate most of the unpleasant surprises that usually surface during the implementation of a new platform.

How do we help?

We’re constantly expanding the list of platforms supported by our solutions, and strive to describe their compatibility in as much detail as possible — like versions, component composition, and honest disclaimers where applicable. For example, the latest versions of Kaspersky NEXT XDR Expert and Kaspersky NEXT EDR Expert now support the Nutanix 7.3 hypervisor.

For companies planning to build their infrastructure on this platform (or migrate over to it) this means one thing: the compatibility issue goes from being a roadblock to being resolved. There’s no need to postpone securing the infrastructure until the migration is complete, or to deploy cybersecurity solutions in an untested configuration — cybersecurity is now integrated into the migration project on a par with other systems.

The full technical requirements for our security solutions are always available in the documentation; it’s always worth reviewing them before deployment schedules are finalized.

Your phone rings, you pick up and say hello. On the other end: total silence. No one answers, and the call abruptly disconnects. If you don’t already use spam call blockers, you’ve almost certainly run into this situation before.

In most cases, these are scam calls. Today, we explain why these calls happen, what the callers want from you, and how to protect yourself. Most importantly, we’ll look at whether you even need to bother protecting yourself against them in the first place.

Who’s calling?

It’s not just scammers on the line — robots, legitimate call center operators, and ordinary folks make these calls too. Let’s break down each type of caller — ordered from best-case to worst-case scenario for your security.

Actual person

The most harmless scenario is that an actual person called you, but their microphone is acting up. Maybe they accidentally muted themselves with their ear, or their smartphone connected to a Bluetooth headset, speaker, or car system that isn’t capturing their voice. Carrier glitches can also mute one side of a call. The caller might have no idea there’s a problem — as far as they know, they are speaking, but no one can hear them. In cases like this, you usually recognize the incoming phone number.

If the call comes from an unknown number, there’s still no need to panic — though the list of those who might be calling gets much longer.

One legitimate possibility is a call center agent who simply didn’t pick up or connect their headset in time. Call center systems are designed to dial numbers faster than agents can wrap up their calls. The system tried to route the call to a human, but no reps were available. That’s why you sometimes have to wait a few seconds before hearing a single word, or why you might hear ringing tones as if you were the one making the call.

Robot or AI

Silence on the line is a common sign of robocalls. Robots test whether a phone number is active and, if it is, pass it along to a human — meaning a real sales rep (or scammer) will call you back in the next few days. It’s worth noting that scammers aren’t the only ones making these pinging calls. Legitimate call centers use the exact same tools to reduce the workload on their live agents.

An AI agent could also be behind the silent call. To the person answering, there’s no practical difference: the call looks identical to one made by a standard bot. However, AI can do more than just auto-dial numbers — it can analyze your response and use that data to decide whether your number is active and ready to be handed off to a live person for follow-up.

Unwanted caller

Now we get to the real threat. Perhaps one of the most dangerous and unpleasant sources of silent phone calls is a scammer. A quick, silent call like this can actually be the groundwork for a long, elaborate attack with cover stories about loans, government agencies, other fraudsters, even law enforcement.

Debt collectors might also be calling and staying quiet. Your number could end up on their radar if you, your family, or close contacts have outstanding debts. In these cases, a silent call is often used as a tactic for psychological pressure.

A similar technique is used in stalking. While silent calls cause no direct harm on their own, they can be leveraged to induce anxiety, create a feeling of being constantly watched, and cause ongoing emotional distress.

Why do they call and stay silent?

When you pick up, you likely respond out of habit with a quick “Hello?” or “Hi there.” That’s all it takes for the other party to gather a wealth of data. While this information used to be difficult to process, the rise of artificial intelligence has made the task significantly easier. Let’s look at what someone can learn about you from just one spoken word:

• Region, accent, and location. Scammers are sophisticated and cunning. Their tactics are often tailored by region — targeting residents of specific countries or even regions within them. This is especially relevant in places like India or South Africa, which have 22 and 11 official languages, respectively.
• Approximate age and gender. While a human listener might easily confuse a teenager’s voice with a young woman’s or misjudge someone’s age entirely, AI is far better at picking up on subtle vocal nuances. Knowing your age and gender helps scammers refine their playbook for future social engineering attacks.
• Times you’re available. If you answer the phone in the morning, afternoon, or late at night, attackers can schedule their follow-up call during the exact time window when you’re most likely to pick up.
• Likelihood of a successful attack. AI can automatically assess the potential value of a target. For instance, if someone answers quickly, speaks calmly, and doesn’t immediately hang up on unknown numbers, they’ll likely be assigned a higher priority for follow-up calls by live scam operators.

Back to the “why do they call and stay silent”, the main reason is to harvest biometric data. Just a few seconds of recorded audio can help cybercriminals create a voice deepfake. While one or two words might not yield a convincing clone on their own, attackers can stitch together recordings from multiple silent calls to build a believable replica.

This technology is already being used in real-world scams. Impersonating a relative, colleague, or boss, fraudsters can urgently ask you to send them money, to share a two-factor authentication code for government services, or to complete some other seemingly innocuous request. The more realistic the deepfake sounds, the harder it is to spot the scam — especially when backed by a convincing backstory.

What to do if you get a silent call?

If you answer a call, say a few words, and hang up, there’s no need to panic. However, that brief interaction can confirm to attackers that your number is active and that you’ll answer calls from unknown numbers. As a result, your phone number could end up on target lists for future spam or scam campaigns. That said, it’s important to remember that a single silent call poses no immediate security threat.

Here are a few tips to help you stay calm and avoid falling for scam tactics if those silent calls are becoming a problem:

• Don’t answer calls from unknown or hidden numbers. Here’s a helpful tip: if someone genuinely needs to reach you, they’ll find another way to do so, or keep calling from the exact same number at various times. Scammers almost always dial from different numbers, while automated bots operate on a rigid schedule — like calling every day at precisely 8:05 AM.
• Don’t rush to call back. Scammers often count on proactive victims who are curious enough to return calls from unfamiliar numbers. On top of that, calling back could end up costing you money if it’s a premium-rate number.
• Don’t speak first. Wait for the caller to greet you before starting a conversation. If you hear muffled noise or complete silence on the line, hang up and save yourself the hassle — it’s likely a scam.
• Block unknown numbers — even after the call. If you picked up and realized the call could be risky, it’s best to block the number right away. You can use the built-in features on most modern smartphones to do this.
• Don’t share your number everywhere. Phishing sites, fly-by-night web pages, and sketchy giveaways often exist solely to collect your personal data. When filling out forms online, it doesn’t hurt to use a burner or secondary number.
• Get a second phone number. Separate your daily life between two numbers. Use your main line strictly for family, friends, and work contacts, and reserve the secondary line for deliveries, online marketplaces, and general web sign-ups.

Further reading on scammers and deepfakes:

• They’re reading you like a book: scammers’ favorite tricks
• How to recognize a deepfake: attack of the clones
• The great messaging heist targeting your wallet
• You’re in for a big payout again
• Scareware: fake threats, real risks

Leveraging legitimate software is one of cybercriminals’ tactics of choice, with remote management tools ranking among their top tools. A recent example involves the remote administration utility ScreenConnect. It’s designed for IT support teams to troubleshoot systems and configure software seamlessly in the background. However, when weaponized by threat actors, ScreenConnect becomes a versatile attack vehicle used to harvest data, deploy malware, and move laterally across corporate networks.

During a recent incident detected by Kaspersky Managed Detection and Response, our experts identified an attempt to use ScreenConnect in an attack. This allowed a detailed study of how attackers used this application in a large-scale malware distribution campaign. The following breakdown illustrates the mechanics of ScreenConnect-assisted attacks, and outlines key strategies to defend your organization against them.

How ScreenConnect reaches target computers

In the campaign analyzed by our experts, the attackers bundled ScreenConnect with legitimate free business software. They established a network of phishing websites to spoof popular tools, including OBS Studio, DS4Windows, DNS Jumper, Glary Utilities, Bandizip, Process Hacker, and others.

These rogue websites featured high-quality designs that could be taken for the official pages, making them highly convincing to unsuspecting users. Once the victim clicks the download button for the software, an archive is downloaded to their computer that contains additional files alongside the requested application:

• A legitimately signed Microsoft executable (exe), renamed to match the expected application installer (for example, OBS-Studio-Installer.exe)
• A malicious library named res.1033.dll
• An Assets directory containing installers for both ScreenConnect and the intended application

Launching the renamed file disguised as the app installer triggers DLL sideloading of a malicious library. This library silently runs the ScreenConnect installation without restarting the system, while using the standard Windows installer to set up the software the user originally tried to install.

The attackers used search engine optimization techniques to drive traffic to their fake websites. As a result, these malicious pages appeared at the top of search results for certain free software utilities on major search engines.

Our experts discovered over 90 domain names translated into more than 10 different languages. While most of these websites targeted English, Russian, and Chinese speakers, several domains catered to German, French, Spanish, Arabic, and other regional audiences.

A detailed analysis of the IP addresses and associated spoofed domains is available in our technical research article on Securelist, along with full indicators of compromise.

Why the attackers exploited ScreenConnect

In this campaign, attackers leveraged ScreenConnect to generate and execute malicious scripts on victim machines. These scripts served several key functions: they created exclusions for specific drives, directories, and processes within Windows Defender, disabled the User Account Control (UAC) security mechanism, and delivered and deployed AsyncRAT – a remote access Trojan.

To maintain persistence, the scripts configured a Windows scheduled task to run the malicious code at preset intervals. AsyncRAT then established a connection with the attackers’ command-and-control server to receive further instructions.

The primary objective of this campaign appears to be gaining unauthorized access to enterprise systems, likely to then resell it on cybercrime marketplaces.

How to secure corporate infrastructure

Although ScreenConnect in and of itself is a legitimate tool, its presence poses a security risk to corporate environments. Consequently, Kaspersky security solutions detect this application as not-a-virus:HEUR:RemoteAdmin.MSIL.ConnectWise.gen.

Security teams should implement the following controls:

• Enforce strict application control policies, including software allowlisting and restrictions on MSI package installations from unverified sources
• Monitor for newly installed remote management utilities and scheduled tasks
• Filter outbound network traffic from workstations to unknown IP addresses and domains

As noted previously, this campaign was originally detected through the Kaspersky Managed Detection and Response service, which can be employed to protect against such threats.

AI is getting better and better at solving CAPTCHAs. This makes website creators — who still need to verify that they’re dealing with humans rather than bots — come up with increasingly sophisticated proof-of-humanity checks. It seems an average user would no longer be surprised if asked to recite a poem — or, say, dance Gangnam Style — just to access certain websites. The latter could double as an age check, but I digress.

Attackers have learned to exploit this variety of sometimes bizarre checks to their advantage. In our blog, we’ve talked more than once about the ClickFix attack technique, which is all about slipping a malicious instruction to the victim under the guise of yet another CAPTCHA. Historically, this technique has been used to attack users of Windows-powered devices. However, researchers recently discovered a campaign that targets macOS users. In this post, we talk in more detail about the ClickFix attack technique, the new campaign aimed at Mac users, and the criminals’ goals.

What is the ClickFix attack technique?

Many common attacks and scams rely on social engineering, or more simply put — manipulation. Instead of looking for software vulnerabilities, attackers try to trick the user into doing the dirty work themselves: opening a malicious file, clicking a link to a fake website, sharing sensitive data, or running a harmful command.

The simplest example of social engineering is scam calls, where criminals pose as a boss, neighbor, bank security specialist, police officer… the list goes on almost indefinitely. And if you think that only, let’s say, not-so-smart users fall for these tricks, you’re wrong: social engineering has been used to hack the CIA chief’s email, hijack the X (Twitter) accounts of Elon Musk and Joe Biden, and steal half a billion dollars. In our post, They’re reading you like a book: scammers’ favorite tricks, we take a close look at their favorite techniques — give it a read so you don’t end up sharing company with those famous victims.

The ClickFix attack also relies on social engineering, except the scammers aren’t betting on the victim’s fear of an authority figure, but rather on their trust in technology and their habit of following instructions — even when they don’t quite make sense.

In a classic ClickFix scenario, a user visits a compromised or fake website and, instead of the expected page, sees a notification that there’s an issue. The site might claim that to continue, you need to complete an extra verification check, update your browser, or fix an error.

Next, the victim is prompted to follow a few simple steps that look harmless enough at first glance. Usually, this involves copying some text from the attackers’ website, opening the Run window, pasting the text there, and hitting Enter.

In reality, the text is a malicious command that downloads and runs malware on the device. Since the user carries out all the steps themselves, the operating system’s security mechanisms and built-in warnings are often dismissed as just another part of the instructions. In our blog, we’ve covered the typical pretexts used to trick victims into taking dangerous actions; you can read more about them here.

The ClickFix attack scheme quickly began spawning various spin-offs, such as FileFix and ConsentFix. Until recently, the victims of ClickFix and its variants were mostly Windows users, while Mac fans encountered it much more rarely. But lately, the number of ClickFix attacks targeting Apple device owners has been on the rise.

How does the ClickFix attack on macOS work?

The ClickFix attack on Macs, as documented by cybersecurity researchers, begins with the classic fake CAPTCHA scenario. To pass the proof-of-humanity check, attackers prompt the user to copy a command, open Terminal, and paste the text into it.

The more interesting part is what happens after the malicious command is run. First, it downloads a malicious DMG — the standard format for disk images containing installation files in macOS — from a remote server, and saves it to the /tmp temporary folder under a random name. Then the script mounts the disk image without showing it in Finder or creating a desktop icon — keeping it hidden from the user. After that, the script searches the disk image for an app or installation package, and automatically launches it once found.

One case documented in the researchers’ findings involved the widespread AMOS (Atomic macOS Stealer) infostealer being distributed this way. To gain extra privileges on the system, the stealer Trojan displays a fake macOS system authentication window tricking the user into willingly typing in their password.

What are the attackers’ goals?

After the malware is installed, the attackers steal literally everything they can get their hands on from the victim’s device. Atomic macOS Stealer extracts sensitive information stored in Chromium-based browsers (Google Chrome, Microsoft Edge, Brave, Opera, Arc, Vivaldi, CocCoc, and Yandex), as well as Firefox-based ones (LibreWolf, SeaMonkey, Tor Browser, Waterfox, and Zen Browser), such as the following:

• Cookies
• Saved logins and passwords
• Auto-fill data
• Saved credit and debit cards
• Browser profile data

On top of all that, the attackers also steal PDF, TXT, and RTF files from the victim’s device. The criminals are especially interested in crypto apps; specifically: desktop crypto wallets like Exodus, Electrum, Atomic Wallet, Wasabi Wallet, Bitcoin Core, Litecoin Core, DashCore, Guarda, Binance Wallet, Dogecoin Wallet, and Tonkeeper. The malware also gathers data from more than 200 cryptocurrency-related browser extensions.

But that’s still not everything…

It also targets the Telegram and Discord desktop apps. The malware doesn’t spare Apple’s ecosystem either — stealing Safari cookies, notes from Apple Notes, and passwords stored in the built-in Apple Keychain.

All of the collected information is added to a ZIP archive and uploaded to the attackers’ server. In addition, the malware replaces legitimate versions of hardware crypto wallet apps — specifically Ledger Wallet and Trezor Suite — with malicious fakes.

Together, this trove of data gives attackers broad opportunities to further compromise accounts, steal digital assets, and launch new attacks on behalf of the victim.

How do I avoid becoming a victim of ClickFix?

As social engineering attacks continue to grow, users need to be more vigilant than ever. That goes for everything from chatting with strangers on the phone or in messaging apps to everyday online activity.

• Never paste commands into your Terminal just because a website asks you to — whether it’s to pass a verification check, confirm your identity, or view hidden content. No legitimate website will ever ask you to enable its features by manually running commands Terminal.
• Never enter your macOS administrator password unless you completely understand what app is asking for it.
• Regularly install macOS security updates, or better yet, set them to install automatically by going to System Settings → General → Software Update and clicking the i icon next to Automatic Updates. While recent versions of macOS may warn you when you try to paste suspicious or malicious commands into Terminal, this isn’t a reliable feature. For example, the malicious code shown in the screenshot above didn’t trigger any warnings at all on macOS Tahoe 26.5.2.
• Trust the warnings from your operating system and security software rather than the instructions — or even the demands — of websites and apps.
• Install a reliable security suite for macOS. It’ll warn you and block malicious activity if you land on a suspicious website.
• Use secure password manager to keep your login credentials and crypto assets safe from cybercriminals.

Read our posts to learn about other threats facing Apple device owners:

• Crypto thieves ramping up attacks on Apple users
• The iPhone — invincible no more: a look at DarkSword and Coruna
• Predator vs. iPhone: the art of invisible surveillance
• AirBorne: Attacks on Apple devices through vulnerabilities in AirPlay
• SparkCat trojan stealer infiltrates App Store and Google Play, steals data from photos

Virtually every website selling products or services features a live chat widget. It usually appears as a small window in the corner of your screen, complete with an agent’s name and picture.

Sometimes these chats simply collect tickets for tech support or sales to process. But often there’s a real person on the other end of the screen. And here’s where many users get an unwelcome surprise: agents can see absolutely everything you type into the chat box, even if you have no intention of sending it. In this article, we break down why this happens and what you can do about it.

All in the name of customer experience

It’s all thanks to a feature known as live typing preview, or real-time typing view, which exists under one name or another in almost every popular customer chat platform. Here’s how the developer of LiveAgent, a customer support system used by more than 40 000 companies, describes it in its blog: “This live chat typing preview allows agents to anticipate questions and deliver faster responses. Additionally, real-time chat monitoring enables supervisors to oversee chat interactions, ensuring quality and timeliness.”

While that might sound helpful and harmless to some, having their unsent messages watched without their permission can trigger outright panic for others. To make matters worse, it’s almost impossible to tell whether the website uses the feature. That is, unless the chat agent admits it to you directly… or replies to a message you decided not to send.

As an alternative, you can run a quick test: type out a message in the chat box, wait 15 seconds, and see if the agent starts typing a response. If they do, you’re almost certainly dealing with real-time typing view. Whether to continue that conversation is up to you. Most importantly, never type personal information into a chat box — even if you are doing so from a Kaspersky Premium device.

Real-time typing view is just the tip of the iceberg. In reality, websites collect far more information than you might think. You’re likely already familiar with cookies, and have probably asked yourself at least once, “Should I accept these or not?” We answer that and other questions in our post Taking the biscuit: why hackers like cookies so much.

Tracking techniques that use web beacons and tracking pixels are not as widely known. To learn why they’re a concern and how to turn them off, check out our post Who is tracking you on the web and how.

Another technology that remains largely unknown to the general public is session replay scripts. These tools allow website owners to watch a recording of your visit: how fast you moved your cursor, where you clicked, which pages you browsed, and what you added to your shopping cart.

Of course, the primary goal of session replay is the same as real-time typing view: improving customer and user experience. It helps companies analyze your behavior, identify friction points, and make improvements. If a customer fails to complete a checkout or encounters a technical issue, developers can review the recording of their session to pinpoint what went wrong.

In theory, it sounds helpful enough: “We will collect a small amount of data about you to make the site better for you.” In practice, however, this approach comes with significant risks for users.

What’s the catch?

The primary risk here lies in whether websites collect this data legally, how they store it, and whether they share it with anyone. While you can usually gauge legality by reading their privacy policy, the terms of data storage and sharing are often described vaguely.

On top of that, users rarely realize an agent can read their message before it’s actually sent. If you change your mind about asking a question, edit a sentence, or delete your text entirely, that doesn’t mean no one saw the draft. Unlike cookies, opting out of this technology is practically impossible. Most chat widgets simply don’t offer a setting to disable typing previews.

As for tracking pixels, web beacons, and session replays, they’re essentially a form of digital surveillance. Beyond collecting vast amounts of data, they can also slow down your device’s performance. Even worse, if attackers compromise a website’s analytics system, they can gain access to this data, reconstruct your path through the site, and uncover details that can be leveraged in future attacks. To learn more about how scammers pull off these tricks and how social engineering works, check out our post They’re reading you like a book: scammers’ favorite tricks.

While you can’t prevent a potential breach on a third-party website, you can — and should — take steps to secure your own browsing session.

What you can do about it

First and foremost, pay close attention to what you type in a chat box. It’s critical never to share personal data, credit card numbers, logins, passwords, or any other sensitive information with live chat agents.

Beyond that, there’s always a risk of landing on a phishing site and entering your account credentials there. To prevent this, we recommend using our security solutions, which block visits to malicious and suspicious websites. We also suggest storing your credentials in Kaspersky Password Manager — our password manager won’t let you auto-fill saved logins and passwords on a fake site.

Users of Kaspersky Standard, Kaspersky Plus, and Kaspersky Premium have access to our Private Browsing feature (on Windows and macOS). It prevents third-party services from tracking your online activity and collecting real-time information about what you do on websites.

To minimize the risk of data leaks, keep these tips in mind:

• Never type anything into a chat box that you aren’t prepared to send. On almost every website, the agent can see your text while you type, so deleting a message is no guarantee it stays private.
• Keep in mind that your activity on a website may be recorded. These recordings can capture your clicks, page navigation, and form inputs. Treat every website as if your actions are being watched.
• Pay attention to privacy policies. Reputable companies typically state whether they use analytics tools, session recording, or third-party support services.
• Approach chat interactions with caution. Real-life rules apply here as well: never share verification codes, passwords, phone numbers, or other sensitive details in a chat.
• Be smart about accepting cookies. Taking a moment to check what information a website collects is well worth the effort — it helps you make an informed choice about whether you’re comfortable sharing that data.

Tired of endless digital tracking? Here’s how you can protect yourself and your data:

• How to shrink your digital footprint
• Why data brokers build dossiers on you, and how to stop them doing so
• Turning purple: how visited links threaten your privacy
• How smartphones build a dossier on you
• The incognito myth: how private browsing really works
• Advertisers sharing data about you with… intelligence agencies

Cybersecurity news increasingly focuses on attackers using AI for reconnaissance, vulnerability hunting, and writing scripts or malware. Against this backdrop, a less conventional category of attacks remains largely overlooked: threat actors leveraging AI tools that are already deployed within the target infrastructure.

AI agents designed for coding, as well as command-line tools — such as Claude Code CLI, Gemini CLI, Codex CLI, Amazon Q CLI, and the like — are particularly attractive targets. These agents can read and modify files, execute shell commands, install packages, and connect to external services. Developers frequently allow them to run in autonomous mode to avoid manually confirming every action. For an attacker, such an agent becomes a ready-made multitool that has the right privileges, access to critical data, and security clearances from the get-go. That said, other types of AI agents, such as automated financial workflow assistants, are equally vulnerable to exploitation.

Why trusted agents pose a threat

An AI agent differs from standard software in its broad access privileges, the wide array of ways it can be prompted to execute actions, and the difficulty of detecting its anomalous behavior. Enterprise security controls (EPP, EDR, XDR, SIEM) scan for malicious files, known command patterns, and suspicious action sequences. Even if a specific strain of malware hasn’t been seen before, its behavior often gives it away: bulk searches for documents, spawning PowerShell processes, accessing credential stores, or exfiltrating data to an external server from an unknown application will trigger a deluge of alerts in the SOC.

It’s much harder to apply that same detection logic to a legitimate, pre-installed AI agent. By design, these tools are capable of executing an extremely broad range of operations. Running scripts, reading configuration files (including secrets), moving documents, and installing dependencies can all be a part of their normal operation — especially on a developer’s machine.

When an attacker attempts to exploit this kind of AI system, they use natural language to convey their instructions to the agent. A request to hunt for passwords, API keys, or cryptocurrency wallets can be framed in thousands of ways, expressed in a variety of languages, or phrased indirectly. Furthermore, a malicious command can be hidden in any data stream the agent reads — including text fields in documents, file names, or even error logs, as shown in one of the examples below.

Integration between agents and third-party software further expands the attack surface. Agents are frequently connected to email, instant messaging apps, task management systems, knowledge bases, and MCP servers. As a result, a malicious instruction can originate from virtually any source whose data the agent processes automatically.

Below are several examples of attacks where threat actors leveraged AI agents to achieve their goals.

The Nx attack: a coding agent hunting for passwords

The most prominent and widespread example is the compromise of Nx npm packages in August 2025, dubbed s1ngularity. Attackers exploited a vulnerable GitHub Actions workflow, stole a package-publishing token, and released trojanized versions of several @nx packages that harvested developer secrets.

The malicious post-install script checked whether Claude Code, Gemini CLI, or Amazon Q CLI were installed on the victim’s machine. If any of these were detected, the agent was issued an instruction to scan for cryptocurrency wallets, ENV files, API keys, and other sensitive data. The agents were executed with auto-approval flags enabled, such as --dangerously-skip-permissions, --yolo, and --trust-all-tools.

The search results were then exfiltrated to public GitHub repositories created by the attackers. Over several waves of the attack, thousands of secrets belonging to hundreds of organizations were exposed.

Instead of developing a complex, custom harvesting tool, the attackers repurposed the victim’s own legitimate agent. It already could navigate the file system, understand file context, and identify high-value target data. However, post-incident analysis by security experts revealed drawbacks to this approach: the AI occasionally refused to carry out unethical tasks. Furthermore, the malware authors had to adjust their code so that it “waited” for the AI agent, which could take a considerable amount of time to process the task while generating significant intermediate output.

Communication via… error logs

The AgentJacking study was not a malicious attack, but it differed from one only in intent: its authors had no interest in stealing secrets. After developing the attack technique, the researchers tested it on a global scale and received callback responses from AI agents belonging to more than a hundred real-world companies — some of them major enterprises.

The attack targeted Sentry, a widely used telemetry service. Websites and mobile applications automatically transmit error reports to Sentry so developers can monitor issues without requiring active user reporting. These reports are accepted without authentication because errors often occur for anonymous visitors that the site or application knows nothing about.

The researchers configured their own MCP server within Sentry, allowing AI agents to analyze reports automatically. Next, they submitted a fake error message containing a data block formatted to match Sentry’s MCP server data structure. Embedded inside was an indirect prompt injection — an instruction to run “additional diagnostics” via a command like: npx @controlled-validation-package --diagnose.

If error handling were delegated to an agent, it could take the instruction as legitimate and execute a command resulting in the automatic installation of a third-party package. In this experiment, the package merely contacted the researchers’ command-and-control server without exfiltrating any sensitive data. A genuine malicious package, however, could steal credentials, tamper with code, or establish persistence in the system. Identifying targets for such an attack is equally straightforward: an organization’s use of Sentry is easily detectable by analyzing website code or checking the strings in mobile application binaries.

Notably, according to the study’s authors, Sentry acknowledged the issue but stopped short of implementing comprehensive fixes, opting instead to block only the specific variant of the malicious query routed through the MCP. Screenshots included in the study reveal that a Sentry employee described the system as “technically not defensible” against the attack.

Stealing financial data via MCP

Abusing unsecured MCP servers — as seen in the Sentry case — is just one way to attack this protocol. Introduced just a year ago, and already dubbed “USB for AI agents”, the Model Context Protocol standard was adopted rapidly without robust built-in security controls. As a result, attacks targeting MCP are bound to proliferate — a trend Microsoft recently warned about.

Threats can lie hidden even within the description of an MCP tool before the agent ever uses it. Agents parse tool descriptions to determine when to call the tool and what data to supply. If an MCP server provider stealthily alters a description — adding an instruction that demands collection of additional data — the agent may execute it as part of its standard workflow.

Microsoft highlights a scenario involving a financial agent that verifies vendor banking details via an external MCP service. Following a malicious update to the tool’s description, the agent begins appending unpaid-invoice data to its responses. The user sees a normal response, while confidential financial data is quietly exfiltrated to the server owner. Individually, each operation appears authorized: the agent operates under the employee’s permissions, queries an approved service, and uses a standard interface. Yet a data breach still occurs.

It remains unclear from Microsoft’s ambiguous phrasing whether this example is purely hypothetical or based on an investigation into a live incident. However, the fact that the post originated from Microsoft Incident Response points to the latter.

Prompt injections have already gone mainstream

In 2026, Unit 42 researchers identified widespread attempts to covertly embed instructions designed for AI systems across numerous public websites. Many of these instructions aim to force models to reveal system prompts, approve sites and posts during ad placement reviews, or boost phishing pages through SEO manipulation.

Similarly, an analysis of Common Crawl data by Google recorded a surge in such material: between November 2025 and February 2026, the share of malicious injections increased by 32%.

That said, confirmed cases of successful compromise using these techniques remain scarce. The presence of an injection on a webpage does not guarantee that an agent ever actually executed the command. Nevertheless, threat actors have clearly begun optimizing web content for an era when it will be read not just — and perhaps not primarily — by humans.

How to defend against agent-based attacks

The key takeaway for cybersecurity teams and all AI users alike is simple: an AI agent should never be considered trusted merely because it has been officially approved for enterprise use.

Organizations must:

• Maintain an inventory of agents, MCP servers, and the various tools available for them. Use allowlists to restrict agent tools, and package installations to components explicitly vetted and approved by information security. Allowlists must specify exact package versions.
• Monitor version changes to MCP tools and other dependencies. Re-evaluate updates before adding them to the approved inventory.
• Apply the principle of least privilege. Grant agents only the specific permissions needed for a given task, and only for the duration of that task.
• Enforce human-in-the-loop approval for package installations, script executions, file sends, and other high-risk actions.
• Run agents in isolated environments with limited access to developer workstations.
• Disable dangerous auto-approval modes.
• Restrict outbound network connections, and permit data transmission only for approved services.
• Store secrets in centralized, secure vaults, using short-lived tokens and rotating them regularly.
• Forward prompt logs, tool calls, shell commands, and agent network traffic into SIEM and XDR systems. For these tools to be effective, they require deep operational context regarding agent activity alongside specialized rule sets tailored to AI data processing pipelines.

Starting last year, Android users have been seeing the “number is now verified” system notification more frequently. And in some cases people even find mysterious outgoing text messages in their history that they never sent.

These messages often cause confusion and even anxiety: has a virus infected the phone? Are tech giants spying on our phone numbers? Let’s break down how this feature works, and what potential risks it poses to your privacy.

Why all these phone number verifications?

The notification pops up whenever Google’s phone number verification feature is triggered on your device.

Its main job is to make sure that the SIM card tied to a specific phone number is physically inside the phone. Once verified, that phone number automatically links to all active Google accounts on the device.

There are several key services that rely on the verification. Most importantly, it drives Rich Communication Services (RCS) — the modern standard for “rich” messaging right inside your standard text messaging app. It feels like a popular chat app, but without the need to install anything extra. Unlike iMessage, which is locked to Apple’s ecosystem, RCS works across compatible smartphones on various platforms since it’s an industry standard set by carriers rather than tech giants. Since recently, both Apple and Android users have been able to exchange RCS messages. For this feature to work in the Google Messages app, Google needs ongoing confirmation that your SIM card is active. If you pull out your SIM card, RCS chats will keep working for about eight days before turning off automatically.

According to Google, phone number verification serves several other purposes as well:

• Account security and recovery. A verified phone number enables quick sign-ins to your Google account, two-factor authentication, and easy password recovery.
• Emergency services and device location. This includes Find My Device, remote phone lock, and sharing your location with emergency responders — including via satellite messaging on the Pixel 9 and certain other modern smartphones.
• Better sharing on Google. This feature helps other people find you faster on Google Meet or Duo, use Quick Share to send you files, and see that the phone number is linked to your profile.

Google also recently confirmed that this data is used to counter scams. Verification helps block calls or text messages from spoofed numbers if both the real owner of the number and the recipient are using Android devices with verified phone numbers.

How does Google verify phone numbers?

Phone number verification technology has been around on Android for quite some time — Google was sending test SMS messages as far back as in 2019. However, it became widely visible to most users following a Google system update in September 2025. The process was baked deeper into the operating system, with number verification now running by default during initial phone setup, and re-running periodically in the background.

There are two main technical methods for the verification process. Which one your device uses depends on your mobile carrier and your version of Android.

The older method relies on hidden text messages. In the background, your smartphone sends a specialized technical text message to Google’s servers. The operating system intercepts this message before you ever see it, which is why it rarely appears in your standard text messaging app. However, due to software glitches or Android customization quirks, these texts occasionally surface in sent messages, startling users. They typically look like this: “(string of letters and numbers) Google is re-verifying the phone# of this device.” Google explicitly notes in its help documentation that standard messaging rates from your carrier may apply.

In recent years, direct carrier verification (via carrier APIs) has become the primary verification method. This approach is more modern and secure than previous ones. The smartphone sends an encrypted token containing device and SIM card identifiers to the mobile carrier, and the carrier responds with the confirmed phone number. The whole process takes just a couple of seconds and runs completely unnoticed by the user. Both global telecom giants and smaller providers have connected to this verification network. Notably, third-party apps can also tap into the results of this check through Firebase Phone Number Verification, getting confirmation from your mobile carrier about which phone number is active on the device.

“Other device data” and privacy concerns

In its official documentation, Google notes that device identifiers and SIM card data may be collected during verification. In practice, this refers to unique identifiers for the SIM card and its subscriber profile (ICCID and IMSI), as well as technical device identifiers needed to run mobile networks. Google also explicitly states that it does not sell your personal information, including your phone number, to anyone.

Naturally, sending additional unique identifiers to Google — especially given the scale of its advertising business — always raises concern among privacy-conscious users. Here’s what you should keep in mind:

Metadata collection. To run RCS, Google exchanges data with your mobile carrier. Even when the content of RCS messages is encrypted, metadata — such as who is messaging whom and when — can still be stored on your carrier’s servers, and in some cases, on Google’s servers. As cybersecurity experts at the Electronic Frontier Foundation point out, if privacy is your top priority, you’re better off sticking to dedicated encrypted messaging apps.

Linked accounts. If you have both a personal and a work Google account (or a personal and a family account) set up on the same phone, the verified number will automatically link to both profiles. The operating system does not offer built-in tools to separate numbers for different accounts on a single device.

Phone number leaks. Apps on your device can already access various user identifiers, including your phone number. However, this verification system makes it easier to link multiple phone numbers to a user who has multiple Google accounts. And while Google states that it never sells phone numbers, you cannot say the same with confidence about obscure third-party Android developers.

On by default. The feature is enabled out of the box, and most users have no idea their device is silently querying their carrier in the background. While you can opt out in your Google settings once you notice it, there’s no guarantee that any data already collected will actually be deleted.

Turning off verification — why and how

For most users, verification is genuinely helpful. It simplifies account recovery, makes finding a lost phone easier, powers modern text messaging features, and assists emergency services when every second counts.

However, if you want to minimize the amount of metadata sent to Google, mobile carriers, and other data brokers, you can manually disable the feature. Here’s how:

1. Open your Android smartphone’s settings.
2. Tap Google, select the account at the top, and switch to the All services tab.
3. Under Privacy and security, tap Phone number verification.
4. Turn off automatic phone number verification.

If you also want to disable Better sharing on Google, go to Settings → Google → Manage your Google Account → Personal info → Contact info → Phone, select your number, and turn off the setting.

If you have multiple Google accounts on your phone, you’ll need to repeat these steps for each one. Unfortunately, verification sometimes turns itself back on automatically, and there’s no reliable way to prevent this on standard consumer phones with stock software.

Keep in mind that disabling verification means losing access to RCS chats in Google Messages — forcing you to fall back on basic SMS, or switch to alternative secure messaging apps. You also won’t be able to use that number for quick account recovery should you forget your password.

To thoughtfully customize your privacy settings across all your devices — regardless of the operating system, browser, or app — check out our free online tool, Privacy Checker.

Curious about other privacy risks you might not even know exist? Check out our other deep dives, here:

• Post-breakup digital hygiene: what to check and shut down
• What happens in the bedroom stays in the bedroom
• Is telehealth actually safe?
• Predator vs. iPhone: the art of invisible surveillance
• Brain drain: vulnerabilities in mental health apps

Cybercriminals are increasingly moving away from complex malware, relying instead on social engineering that exploits a common user habit: mindlessly clicking through endless digital rituals like CAPTCHAs, cookie consents, and authentication prompts. This habit is often targeted by various kinds of the ClickFix technique, which threat actors have been actively using lately. Researchers recently discovered a new variant of this technique, which they dubbed ConsentFix. It allows attackers to gain unauthorized access to Microsoft 365 accounts via OAuth — a highly popular vector right now — thereby completely bypassing the need to steal passwords. For organizations, these attacks present a severe risk of compromised corporate email, exposed documents, and breached cloud resources.

How ConsentFix works

Social engineering lies at the core of all ClickFix variations. Attackers prompt the user to follow a seemingly harmless set of instructions, which actually lead to the compromise of their device or account. In the case of ConsentFix, these instructions are designed to trick the user into granting the attacker access to their Microsoft 365 account.

The attack begins by making the user believe they must authenticate with their Microsoft account to access a specific page or document. To do this, cybercriminals usually send an email containing a link to a website typically imitating a popular file-sharing service. Attempting to access a specific document on this site prompts the user for verification, which can be obtained by following this sequence of actions:

1. Clicking Verify with Microsoft.
2. Signing in to the work account, at which point the attackers warn that multi-factor authentication (MFA) may be required.
3. After being redirected to a new page, waiting until “localauth” appears in the address bar.
4. Dragging the information icon ⓘ from the browser’s address bar into a drop zone on the page.

In practice, following these instructions causes the user to drag an URL containing a session OAuth token directly onto the attacker’s page. This token is exactly what grants the attackers access to the victim’s email and other Microsoft 365 services.

The potential fallout of a Microsoft 365 session compromise

At first glance, it might seem like attackers only gain access to a mailbox. In reality, the consequences can be significantly more severe. The attacker’s specific capabilities depend on the organization’s Microsoft 365 license, the services deployed, and the privilege level of the compromised account.

Even basic corporate Microsoft 365 subscriptions typically grant access to Outlook, Teams, OneDrive, and SharePoint, while more advanced tiers may include additional services and administrative tools.

Even compromised corporate email alone is a major breach. The attacker not only gains access to email archives that may contain confidential business intelligence, but also can send phishing messages or launch internal BEC attacks on behalf of the user.

Beyond email, attackers are highly likely to gain access to the victim’s Microsoft Teams account, providing them with even deeper insights and broader leverage for various scams. Access to OneDrive and SharePoint is equally dangerous: it allows attackers to exfiltrate corporate documents, upload malware into cloud storage, tamper with existing files, and more. Ultimately, this creates a launchpad for moving laterally within the corporate infrastructure.

Publicly available attack blueprints

According to a Bleeping Computer article, the mechanics of ConsentFix are no secret. Attackers share detailed implementation guides on cybercrime forums, offering not only ready-to-use code but also video tutorials demonstrating how to deploy the attack. They also actively trade tips on how they harvest intelligence on organizations and their employees via LinkedIn and other OSINT channels to craft highly convincing phishing messages.

Collectively, this significantly lowers the barrier to entry for novice cybercriminals. As these guides and turnkey tools continue to proliferate, ConsentFix attacks will likely become increasingly common. Consequently, organizations should immediately factor this technique into both their defense strategies and employee awareness training programs.

How to secure your corporate infrastructure

The emergence of this new ClickFix variation demonstrates that simply blocking a specific keyboard shortcut or an isolated risky action on employee devices is not enough. Clearly, attackers quickly adapt to these measures. Therefore, to protect an organization from these types of attacks, we recommend the following:

• Be sure to deploy a robust security solution at the mail gateway level — this minimizes the likelihood of attackers successfully luring your employees to a page hosting the ConsentFix mechanism.
• Conduct regular cybersecurity awareness training for employees to keep them ahead of the latest social engineering tactics. Our training platform, Kaspersky Automated Security Awareness Platform, can assist with this initiative.

A breakup is a major upheaval — the kind that turns life upside down. The familiar order of things, where two people shared the same interests, moments, and often even the same physical space, collapses in an instant, leaving each of you in a world of your own.

Besides that, couples are connected not just in real life, but digitally too. Shared subscriptions, saved addresses and passwords, access to joint cloud storage — all of that used to be woven into your day-to-day life. Moving on emotionally can take a long time, sure, but there are steps you can take right now that will tighten up your security — even if they won’t help you get over your ex.

Here’s what you need to check after a breakup, which services to disconnect, and why you should do it even if the split was amicable.

Digital hygiene: your insurance policy

Cutting off your ex’s access to your online accounts isn’t paranoia — it’s looking out for your own security. A breakup doesn’t automatically sever your digital ties: any access you granted each other during the relationship stays active until someone manually shuts it down. Most of the time, nobody’s plotting to actually exploit it, yet it can still lead to anything from awkward moments to a genuine risk of being tracked.

Take Aleta Dignard-Fung, of Las Vegas, who told NPR that after breaking up with her boyfriend, it didn’t immediately occur to her that he still knew her Spotify password. One day, in the shower with music playing, she noticed her playlist suddenly switch. Her ex had logged in from another device and started playing his own chosen tracks. “It was just kind of like the Spotify wars, and we’d just spend like 10 minutes trying to override each other’s songs,” she admitted. But the fallout isn’t always this lighthearted.

Relationship coach Susan Winter shared a case from her own practice in that same interview. One of her clients simply couldn’t let go of his ex-girlfriend after their breakup. The two had shared an OpenTable account — the restaurant reservation service — and she’d never revoked his access. He started tracking her bookings: where she was going, what time, how many people. That’s how he kept tabs on whether someone new had entered her life.

These two stories, while showing very different behavior from the exes involved, both trace back to the same root cause: accounts that were never properly locked down. In the former case, it led to an awkward moment. In the latter, it led to anxiety and a real sense of being watched. To make sure neither scenario happens to you, it’s worth running through a quick checklist.

Terminate your partner’s sessions

In the settings for your social media, messaging apps, email, and other key services, pull up the list of active sessions and log out everything except your current devices.

Change your passwords

Update the passwords to any key accounts your partner might have known — say, if you used a meaningful date as your password. If you reused the same password across multiple services, change it everywhere. While you’re at it, review your security questions and your backup email or phone number for account recovery — neither should still be tied to your ex. To skip the hassle of memorizing new credentials while still leveling up your security, we recommend using a password manager — it’ll generate a unique, strong password for every account, and save and sync it across all your devices; the only thing you need to remember is a single main password.

Check two-factor authentication and trusted devices

Make sure verification codes only go to your own device. Remove your partner’s devices from your trusted list — you can do this in your Apple and Google account settings.

Split up your shared Apple/Google accounts

If you shared an Apple or Google account, sign out of that. Turn off Family Sharing, iCloud, and backups. Check your shared photo albums too — anything that lands there automatically is visible to your ex as well.

Review your subscriptions

If you shared subscriptions — streaming services, family plans, that kind of thing — cancel them or set up new ones and link your own card.

Check your bank cards

If your ex’s card is linked to a marketplace, delivery app, or rideshare service account you own, remove it from your saved payment methods. If your card is linked to their account and you no longer have access to it, the safest move is to just get your card reissued.

Revoke smart home access

Cut off your ex’s access to cameras, video doorbells, GPS trackers, and smart speakers. If the devices were on a shared account, change the password or move them over to your own personal account.

Review your privacy settings

Check the privacy settings across your accounts on various services and social media using our free online tool, Privacy Checker. It walks you through a privacy configuration tailored to your operating system, platform, and even your browser.

If you notice signs of stalking

Reach out to advocacy or legal aid organizations — they can help you map out the best course of action. It’s also worth leaning on family and close friends who can support you through a tough situation. On Android devices, you can use our security suite with the Who’s Spying on Me feature. It’s designed to help you detect tracking and stalking so you can take the right steps to protect yourself. The feature includes:

• Stalkerware Detection. Flags apps designed to secretly monitor your life and collect data you’d never want shared with anyone else.
• Device Scanner. Finds planted trackers that let someone follow your movements and always know where you are.
• Permissions Control. Shows which apps have access to permissions that could make it easier for someone to spy on you or compromise your privacy.

What can actually help — and what might make things worse — during this difficult time? Check out our other posts:

• Love, AI & robots
• What happens in the bedroom stays in the bedroom
• How chatting with a bot can lead to tragedy
• AI and the new reality of sextortion
• Digital detox: How to take a safe break from screens
• Subscription security: how to protect your account, your wallet… and your sanity

When targeting an organization to steal information, maintaining a low profile is critical for attackers. They typically aim for long-term persistence, which requires avoiding security alerts while preserving access in case they’re detected and the organization initiates incident response or routine password resets. Malware such as infostealers or ostensibly legitimate remote monitoring and management (RMM) tools fail the first requirement: their use triggers EDR and generates suspicious events in SIEM consoles. Relying on stolen credentials conflicts with the second requirement: the moment the security team suspects a compromise, passwords can be changed immediately — terminating access. If attackers attempt to steal browser cookies instead of passwords, they face a different challenge: many online services now correlate device characteristics with the expected session cookie and block access if the cookie is used on an unrecognized device. Furthermore, cookie theft protection mechanisms implemented in Chromium-based browsers (such as Chrome, Edge, and Opera) this year have made this approach significantly more difficult.

To address this persistence challenge, the ToddyCat APT — whose main game is spying — developed a novel technique. Kaspersky experts discovered this method during an incident investigation and named it Shadow Token via Remote Debug (STRD). This technique allows the attackers to establish reliable, persistent access to a victim’s mailbox and other resources in Google Workspace. With minor adjustments, the same approach could potentially be adapted to target other services that grant third-party application access via OAuth 2.0 authentication.

How an STRD attack works

First, the attackers must compromise the victim’s system with malware. In past campaigns, ToddyCat gained initial access to organizations by exploiting known vulnerabilities in server software and distributing malicious loaders via messaging apps. The specific employee targeted by the attackers might not notice the intrusion at all. This can occur, for example, if the adversary first obtains privileged administrative credentials and uses them to deploy the malware onto targeted machines remotely. Crucially, the deployment and execution of this malware mustn’t trigger immediate security alerts.

Once active, the malware executes an STRD attack, connecting the attackers’ remote service to the victim’s mailbox using the OAuth 2.0 protocol. This process requires no user interaction and shows no visible activity on the screen. To the cloud environment (Google Workspace, in the case at hand), the activity appears as if the user has legitimately authorized a third-party app for email access or data backup.

After that, the malware can terminate its operations and even delete itself from the system. The adversary retains direct access to the mailbox using the acquired OAuth token — and they need no connection to the victim’s endpoint or to the corporate network for that. Depending on the organization’s Google Workspace configuration, this access can persist for an extended period and survive subsequent password resets.

The core concept of Shadow Token via Remote Debug

At the heart of this attack is a connection to Google Workspace services via OAuth 2.0. This is a legitimate workflow used whenever a third-party application requests access to calendar data, emails, or Google Drive files. For example, to display calendar meetings in Zoom and automatically generate conference links, a user must authorize Zoom to access Google services. Similarly, configuring a third-party email client or calendar app requires granting permission. During this authorization process, the service requesting access opens a new browser window. In this window, Google Workspace first prompts the user to select the appropriate account. Once the account is chosen, the subsequent screen displays the specific permissions requested by the third-party app, allowing the user to either approve or deny access. For this scenario to proceed seamlessly, the user must already be authenticated to Google services in their browser — which is typically the case in organizations using Google Workspace. If the user isn’t authenticated, additional steps for entering credentials and completing multi-factor authentication are introduced into the sequence.

The ToddyCat hackers developed a malicious tool called Umbrij to facilitate a two-step covert authorization process when the user is already authenticated in Google. First, the malware identifies the browsers installed on the system, and locates the specific folder storing the user’s active profile for each. The attackers target Chrome and Edge, as these are the browsers most likely serving as the primary ones within the organization.

Next, Umbrij copies the entire user profile folder to a different directory on the machine. It then launches an instance of the browser, specifying the path to the duplicated profile via the command line. Because this duplicate profile contains the user’s session cookies, websites with saved credentials won’t prompt for re-authentication. Furthermore, since this occurs on the exact same computer where the primary browser is running, online services detect no anomalies. The browsing history for this newly launched instance is isolated within the new folder, keeping it hidden from the user’s main account activity.

Crucially, the browser is launched in a dedicated debugging mode typically reserved for web development. The browser window and user interface don’t appear on the screen at all (headless mode). Instead, the browser can be controlled through a debugging port using the DevTools protocol, allowing the malware to issue commands and read the state of the screen. To orchestrate these actions, Umbrij leverages Puppeteer, a legitimate automation library.

After verifying that the debugging browser instance has launched successfully, Umbrij opens a legitimate Google Workspace OAuth screen within it. The request sent to Google is engineered to bypass additional security checks while requesting maximum access privileges. For the application ID — the identity supposedly requesting these extensive permissions — the malware impersonates one of two legitimate tools: Google Workspace Migration for Microsoft Outlook (GWMMO), or Google Workspace Sync for Microsoft Outlook (GWSMO).

When Google opens the window within the headless browser, Umbrij uses debugging tools to programmatically click on the corporate account name and the confirmation buttons. As a result, Google generates an authorization code for the app. Umbrij extracts and saves this code, subsequently forwarding it to the attackers’ command-and-control server. Finally, operating entirely within their own infrastructure rather than on the victim’s computer, the attackers exchange this authorization code for an OAuth access token. This single token is all they need to maintain long-term unrestricted access to the mailbox.

How to protect against OAuth token theft

If a Google Workspace account is compromised, incident response measures must include the following steps after collecting the necessary logs and other forensic data for investigation:

• Resetting the affected user’s password
• Terminating all active web sessions for the user
• Revoking OAuth tokens and third-party app permissions
• Reviewing and removing access granted through legacy App Passwords

In addition, security and IT teams must systematically audit issued OAuth permissions, revoke unjustifiable access rights, and restrict capabilities that allow excessive or unauthorized permission grants. We covered this topic in detail in our article on blocking unwanted AI assistants.

How to prevent exploitation of Shadow Token via Remote Debug

While Kaspersky users are protected against the Umbrij tool, security teams should proactively implement policies that prevent standard users from launching browsers in debugging mode. This functionality is intended exclusively for website and web app developers. This restriction can be enforced through the DeveloperToolsAvailability group policy (available for both Chrome and Edge).

Additionally, configure monitoring within your SIEM/XDR to track the launch of browser instances with an active debugging port. This event serves as a strong indicator of this specific attack technique.

In early June, cybersecurity researchers discovered that a compromised version of the Israel-based Hola Browser for Windows (version 1.251.91.0) was secretly downloading a Monero crypto miner to users’ devices. Shortly after the discovery, Hola confirmed that it had fallen victim to a supply chain attack. In this article, we break down how the attack went down, how the crypto miner works, and what it means for affected users.

What is Hola Browser, and how was the malware discovered?

The Israeli company Hola is best known for its VPN service, which users primarily rely on to bypass geo-restrictions and access region-locked content. In addition to the VPN, the company develops Hola Browser — a Chromium-based browser that comes with built-in VPN and proxy features.

Researchers first spotted signs of trouble during a standard compliance check for the AppEsteem Windows Certified Application program. As part of this certification process, independent cybersecurity firms audit software to ensure it only contains the components it claims to have and is free of unwanted or malicious features. Even after a certificate is granted, apps are regularly re-evaluated to ensure they continue to meet AppEsteem’s strict guidelines.

It was during one of these routine follow-up checks that experts noticed an unauthorized file bundling itself with version 1.251.91.0 of Hola Browser for Windows. Once installed, the file saved itself to the hard drive at C:\Program Files\Hola\me{.}exe. The file immediately raised red flags for researchers due to a laundry list of suspicious characteristics: it wasn’t on the list of approved application files, lacked a timestamp, and had no digital signature. On top of that, its code was heavily obfuscated, and it possessed the ability to inject itself directly into system memory.

Interestingly, researchers noted that the file didn’t show up in every single installation. Because the infection wasn’t widespread across all users, experts suspected early on that a specific stage in the Hola Browser distribution pipeline had been compromised. Hola later confirmed this theory, admitting it had fallen victim to a supply chain attack.

As for the suspicious me{.}exe file itself, closer analysis revealed that it was a stealthy crypto miner configured to mine Monero. We’ll now dive into the technical details of how it works.

How did attackers use Hola Browser to mine Monero?

Crypto miners are programs that harness a computer’s processing power to mine cryptocurrency. While some users install this software intentionally to generate a bit of income, miners that run on a machine without the owner’s knowledge are typically classified as unwanted.

Running a hidden miner can noticeably slow down the device, spike the user’s electricity bill, and shorten the hardware’s lifespan. That being said, it’s worth noting that a crypto miner infection will not actually steal the owner’s cryptocurrency; the damage is strictly limited to the hijackers leeching your computer’s hardware resources to line their own pockets.

As we mentioned above, the malicious download bundled with Hola Browser sneaked a Monero crypto miner onto victims’ devices. Launched in 2014 and built on the CryptoNote protocol, Monero currently trades at around US$330 per coin.

Compared to heavyweights like Bitcoin or Ethereum, Monero is a bit exotic and lesser-known to the general public. This niche status shows in its relatively modest price growth and smaller market capitalization — which is roughly 200 times lower than Bitcoin’s. However, Monero has one defining feature: privacy. While Bitcoin and Ethereum operate on fully transparent, public blockchains, where anyone can trace transactions, Monero is a “privacy coin”. It uses advanced cryptographic mechanisms to mask the sender, receiver, and transaction amounts. This extreme anonymity is exactly why hackers love hidden Monero miners — it makes it difficult for law enforcement and cybersecurity professionals to follow the money trail.

Additionally, Monero’s underlying algorithm is explicitly designed to mine efficiently using standard computer processors (CPUs). This stands in stark contrast to many other popular cryptocurrencies, which require specialized ASIC hardware or high-end graphics cards (GPUs) to be profitable.

But let’s look closer at how this played out with Hola Browser. When researchers dissected the malicious me{.}exe code, they found it was automatically adding its own files to the Microsoft Defender exclusion list. By allowlisting itself, the malware successfully blinded Windows’ built-in antivirus, allowing the crypto miner to run in the background completely unhindered.

Once inside, the program made a copy of itself under the name HolaMonitorService{.}exe, and set up a persistent Windows background service called hola_monitor_svc. This maneuver allowed the malware to entrench itself in the system, automatically launching every time the computer restarted. To avoid raising any red flags with sudden massive performance drops, the miner was programmed to stay dormant, kicking into gear only when the computer was idle.

How to protect your device from crypto miners and malware

To their credit, Hola’s development team responded swiftly to the initial reports of the suspicious file. They confirmed the supply chain breach, but stated that the incident only impacted 0.1% of their user base. The company has since tightened up security around its update distribution pipeline to guarantee that users only receive approved, certified, and digitally-signed software components moving forward.

In light of this incident, we highly recommend that all Hola Browser users update to the latest version immediately — especially those running the application on Windows.

More broadly, this situation is a textbook reminder of why it’s so critical to keep all your software up to date and run a robust cybersecurity solution on all your gadgets. For instance, Kaspersky Premium provides real-time alerts about suspicious software behavior and blocks threats instantly. As an added bonus, a Kaspersky Premium subscription includes a secure and reliable VPN.

Don’t forget that malicious crypto miners don’t just target PCs; they also go after smartphones, often disguising themselves as anything from popular mobile games to official government service apps. Check out our previous posts to learn more:

• Android Trojan posing as government services and Starlink apps
• What happens if you download a cracked program?
• Miner inconvenience: how cybercriminals blackmail YouTubers into promoting malware
• Mario Forever, malware too: a free game with a miner and Trojans inside

The World Cup attracts a great many fans — but also a great many scammers. While millions of fans tune in to watch the matches, cybercriminals are hard at work trying to get at their money and personal data. In fact, we’ve already flagged more than 336 fake websites designed to look exactly like the official World Cup page! As the biggest sporting event of the year heats up, here are the top red flags you need to watch out for.

Totally Legit Free Streams (No Scam)

Scoring a seat at WC26 has turned into quite the mission. Soccer fans are furious over ticket prices, which have officially been dubbed the highest in World Cup history. On top of lodging and travel costs, the situation is made even worse by America’s stringent immigration policies — where referees, team staff, and even players have faced major visa and entry headaches. But fans still want to watch the games, and that’s exactly where fake streaming platforms step in to “help”.

Here’s how the scam plays out: cybercriminals set up fake websites promising free access to World Cup match streams. But the moment you click Watch Now, you’re prompted to sign up and then pay for “lifetime access” to the entire tournament. In the example below, they’re asking for cryptocurrency — which is still a bit unusual, since scammers typically prefer good old-fashioned bank cards.

Fans who are desperate to catch their favorite teams live risk losing not just their money, but also their personal data, which hackers can later weaponize in targeted phishing attacks.

A losing bet

Match result predictions and sports betting always skyrocket in popularity during the World Cup, and scammers waste no time cashing in on the trend. And behind the flashy slogans lie classic scam tactics.

Take this beautifully designed Spanish-language website. To sign up, it demands a massive amount of personal information, including your full name, national ID number, email address, and phone number — and, of course, it asks you to create a password. If a victim uses the exact same password for multiple accounts, they’re essentially handing the keys to their digital life over to cybercriminals.

Another site, specifically targeting users in Colombia, turned the sign-up process into a paid ordeal — and it features every trick in the book.

• To “verify” your profile, you’re forced to use WhatsApp under the guise of avoiding legal complications.
• Before your account is activated, you must make a deposit. This means sending 100 000 Colombian pesos (about $29) to a specified account and texting the receipt to an “administrator” on WhatsApp.
• Next, you’re told to wait 12 hours for the “administrator” to manually activate your profile.
• Only after all of this do the scammers tell you can place unlimited bets (of course not true).

In many countries — including Colombia — sports betting is strictly regulated. Only a handful of licensed operators are legally allowed to run these sites, and users are required by law to verify their identity. Because of this, these shady workarounds can look tempting to people who love to gamble but don’t want to — or can’t — go through the official verification process.

Unfortunately, the scammers always win in this scenario. They walk away with your initial deposit and every single bet you place on their site. At the end of the day, their only real goal is to drain their victims’ wallets for as much as they possibly can.

Discounts for collectors!

The World Cup isn’t just about the matches; it also drives record-breaking sales of collectible merchandise — stickers, scarves, team jerseys, official match balls, and more. Naturally, plenty of scammers are eager to get a piece of that action.

Take a look at this website offering “exclusive, limited-edition” stickers and albums. Notice anything suspicious?

Check out those prices: everything is heavily discounted, even though the tournament is in full swing. All it takes is a quick price check against the real deal to spot the trap. In the screenshot above, the scammers are charging 67 euros for a sticker collection. On actual online marketplaces, that exact same set goes for at least twice as much, and on the official Panini website, it’s three times the price.

Fake websites mimicking popular sporting goods stores also offer to sell you shin guards, socks, jerseys, and any other gear. Of course, you’ll never see the merchandise, and you’ll lose both your money and your bank card details.

Deals that seem too good to be true are one of the biggest red flags. To make matters worse, with the help of AI, fake websites now look just as professional as the real ones, making them harder than ever to spot. That’s why we recommend installing our security suite before you start shopping online. It blocks phishing sites in real time and uses the Safe Money feature to keep your financial data secure.

Soccer by mail

Another attack strategy involves spam campaigns centered around the World Cup. In one email, our experts uncovered an ad for a soccer analytics and betting-tips service. It uses the classic high-pressure playbook: “ONLY 10 SPOTS AVAILABLE” — so hurry up before they run out! Naturally, access comes with a price tag: AU$200.

This scheme targets fans who are into sports betting, and paying for these types of services usually ends one of two ways for them: they either lose their money with zero guarantee of getting actual predictions, or get sucked into an even deeper, multi-step financial trap.

How to avoid falling for the scams

Across all these scenarios, the World Cup is just another convenient pretext for cybercriminals. Once the tournament wraps up, they’ll most certainly pivot back to their usual tricks — like fake job offers or Telegram phishing scams — until the next Olympics or soccer tournament rolls around and they switch right back to sport.

Our research consistently shows that online fraud has evolved into a massive illegal enterprise. You aren’t just up against lone scammers anymore; you’re dealing with large criminal networks. When it comes to defense, the best approach is a proactive one. By installing Kaspersky Premium, you can safeguard all your devices from malware, phishing, spam, and malicious or lookalike websites. Plus, the included Kaspersky Password Manager will generate unique complex passwords, securely store your sensitive data — like documents and bank cards — and stop you from auto-filling your credentials on fake sites.

• Watch the games only on legitimate streaming platforms. Don’t trust fake reviews and never enter your bank card information on unverified sites. Keep an eye out not just for sketchy streaming websites, but also for fake IPTV apps. As we’ve covered in detail before, scammers frequently use these to infect your devices with Trojans.
• Shop smart. The best way to avoid getting ripped off is to buy merchandise exclusively through official channels (where you won’t see suspiciously deep discounts), or simply buy your gear in person at official retail locations.
• Don’t click suspicious links. If a deal that’s too good to be true lands in your inbox — whether it’s exclusive betting tips or anything else — just ignore it and hit delete.
• Avoid logging in through Telegram bots. At the very least, this saves you from future headaches and annoying spam. At best, it keeps your account from being hijacked and your crypto from being stolen.
• Switch to passkeys wherever possible. Unlike traditional passwords, which are easily stolen and can be typed into any fake login page, a passkey is cryptographically tied to a specific website and won’t work on a phishing page. Kaspersky Password Manager can easily store and sync your passkeys across all your devices.

What other ruses do scammers use to make a quick buck? Check out our other posts:

• The Winter Games in Italy: how to avoid the scams
• Summer scams in Paris
• Meme coins and NFTs: how not to lose your shirt
• How to watch soccer safely
• Neither flowers nor gifts: how women get scammed

The concept of a completely autonomous security operations center (SOC) — where data collection, analysis of suspicious events, investigations, and incident response happen without human intervention — is extremely compelling. This is especially true for organizations grappling with a chronic shortage of cybersecurity talent and a threat landscape that’s growing faster and more sophisticated by the day. Organizations everywhere would welcome an approach where automation helps relieve analyst workloads, shortens alert triage times, and finally eliminates the backlog of unaddressed alerts — which, by some estimates, accounts for 67% of all security events in the average corporate SOC.

While many vendors are already pitching solutions in this space, real-world implementation remains highly problematic. Practitioners report tangible success when using these tools for alert enrichment and filtering out low-priority noise or false positives. However, when it comes to autonomous decision-making and response, very few organizations have managed to achieve a meaningful return on investment.

Foundational roadblocks of an autonomous SOC: looking beyond AI

While leveraging AI for data analysis and decision-making sounds like a logical and relatively easy-to-implement idea, actually putting it into practice exposes and amplifies the exact same challenges organizations faced with SIEM, XDR, and SOAR platforms:

Source data quality. Issues with coverage, enrichment quality, tagging and normalization, which detection engineering teams in every SOC battle daily, become even more acute when AI is introduced. AI agents are more sensitive to data gaps than human analysts, so incomplete data can magnify the resulting errors.

Data consolidation and tool integration. The very problem SIEM was once invented to solve remains a headache for most organizations today. Interestingly, marketing for AI-driven SOCs often claims that “the SIEM is dead” because “agents can just query the EDR directly for telemetry”. In reality, however, even in a best-case scenario, this just means the SIEM disappears as a user interface while its core functions remain embedded within the data fabric of the agentic SOC.

Analysts’ trust. Even when AI is restricted to preliminary data gathering and recommendations, human analysts frequently don’t trust the output, leading them to waste time re-collecting and re-analyzing the same data. Practitioners frequently point to several flaws in current AI SOC implementations: poor handling of gray-area verdicts (when an alert is suspicious but not definitively malicious), lack of safe escalation workflows, and systems that fail to learn when a human analyst corrects their mistakes.

Context deficit. SOCs and security teams in general naturally rely on scantily documented information, such as business context and tribal knowledge, to accurately assess alerts and incidents. It’s very difficult to populate an AI system with that knowledge in a systematic way.

AI-specific issues critical for a SOC

Beyond traditional operational hurdles, fully autonomous SOCs face inherent flaws deeply rooted in the fundamental architecture of language models and AI agents.

Hallucinations and prompt injections. In a SOC environment, a single manipulated log field can easily become a viable exploit vector aimed directly at the agent. In a semi-autonomous setup, an AI hallucination is just a frustrating distraction that erodes analyst trust. In a fully autonomous SOC, however, a hallucination can trigger instantaneous, harmful actions across hundreds or thousands of endpoints simultaneously. A prime example of this risk is the widely cited incident at a Fortune 50 company, where an AI agent went rogue and rewrote access policies on its own.

Need for control. To combat hallucinations and over-automation, organizations typically rely on a human-in-the-loop (HITL) model to approve an agent’s actions. While this improves safety, it completely defeats the primary selling point of agentic AI: response times.

Compliance, audits, and accountability. The inherently stochastic nature of LLM outputs makes logging problematic. They often lack reproducibility and explanations. Consequently, an autonomous SOC will likely struggle to pass regulatory compliance audits. Simply put, current compliance frameworks were never designed to handle the unpredictable behavior of multiple interacting AI agents.

Strategies to overcome the challenges of an autonomous SOC

Specialized frameworks are emerging to address these built-in flaws of AI agents and language models. For the most part, these solutions focus on enforcing formal boundaries around AI privileges, and validating its actions.

Rigorous context engineering. Assuming source data is correct and properly enriched, the number of hallucinations can be minimized, and agent decision quality significantly improved by feeding the language model structured layers of context — such as alerts, user accounts, asset data, and enrichment data.

Narrowing the scope of work. AI agents are less likely to go off the rails when confined to highly repetitive, narrow tasks. For example, an “agent for collecting additional host data” is going to be more effective than an “autonomous threat hunter”.

Neurosymbolic validations and guardrails for agent actions. An Agent-Lock pipeline cleans untrusted log fields, and verifies proposed actions against existing CMDB/IAM policies. This approach enforces key rules, such as making it impossible for the AI to disable telemetry, while managing “autonomy budgets”.

Tiered autonomy over all-or-nothing automation. The Trusted Autonomy framework maps out progressive levels of AI independence based on human-in-the-loop roles and trust thresholds across monitoring, detection, and response. Low-risk operations like data enrichment and alert deduplication run fully automated, while high-blast-radius actions require mandatory human approval.

Governance-first architecture. The LanG platform, which utilizes a hierarchical approach: Governance → MCP → Agentic AI → Security, is one example. It enforces two mandatory human analyst check-ins, fully aligning the workflow with NIST SP 800-61 guidelines. The trade-off, however, is that this framework significantly scales back the solution’s autonomy.

Deterministic execution for high-risk actions. Triage and investigation are handled by a probabilistic AI model, but high-impact actions — like deciding to isolate a host or terminate a session — are based on deterministic code. This approach allows the system to satisfy the strict requirements of SOC 2 and other major regulatory frameworks.

Stateful admission control. For example, the recently proposed ACP protocol monitors behavioral patterns across agent execution logs. This makes it possible to catch rogue agents that are executing a series of individually harmless requests that add up to a coordinated attack.

Key takeaways and pitfalls

We can already confidently state that an autonomous SOC is highly unlikely to bring any improvements for organizations burdened by significant technical and operational debt in areas like data collection and enrichment or standardized incident response workflows. No layer of AI infrastructure will function without that baseline foundation firmly in place.

It’s also clear that, while AI streamlines analyst workflows, it doesn’t completely replace them. This is why Gartner’s prediction that there will never be an autonomous SOC still rings true in 2026. Deploying autonomous agents into the SOC shifts the center of gravity to complex investigations, but most importantly, to complex engineering. Teams will simply trade fine-tuning detection rules for managing AI agent playbooks, data pipelines, and decision-handling workflows.

For mature SOCs, the core hypothesis for the next one to two years is this: an autonomous SOC should be viewed as a direction rather than a destination. AI is already delivering tangible value today — specifically in correlation, enrichment, draft detection rules, and attack reconstruction — provided that each capability has proper security guardrails. These include a well-balanced human-in-the-loop review process for any action that impacts production environments. Security teams investing now in a structured, verifiable approach — one that actively anticipates emerging regulations — will be able to gradually integrate new agentic features into their SOC pipelines. Conversely, organizations that skip this layer will almost certainly run into roadblocks, likely forcing them to rebuild their systems and processes from the ground up.

Scientists at Graz University of Technology in Austria recently published a paper detailing a new method for tracking users’ activity through their web browsers. The most fascinating thing about this new technique — which they’ve named FROST — is that it relies on a computer’s solid-state drive (SSD) to do the spying. Without getting bogged down in technical details, here’s how the attack works: a hacker lures a victim to a specially crafted website; as long as the site is kept open, the attacker can track exactly what apps the user is launching, and what other web pages they’re visiting.

So, how do they pull this off? The first instinct is naturally to blame the browser. But in modern web browsers, every website runs in an isolated sandbox and is generally locked out from touching other tabs — let alone the computer’s actual hardware. While hackers do find loopholes in these defenses from time to time, that’s not what’s happening here. The FROST attack doesn’t need to break the browser; it works perfectly even with all standard security measures in place. Instead, it hijacks a completely legitimate browser feature called the origin private file system (OPFS), which gives websites their own virtual storage space to store data. However, while this storage is digitally isolated, the data is still physically written to the exact same SSD that every other app and website opened on the computer is using. The researchers discovered that if a malicious page constantly bombards the SSD with data requests, the microscopic delays in data access can help map out what else is running on the PC. Before we dive into the details of how they manage this, let’s take a quick look at the theory behind the attack.

A quick primer on side-channel attacks

The term “side-channel” refers to a method of spying on a computer — or even a single microchip — indirectly. Instead of intercepting the data itself, an attacker might analyze fluctuations in power consumption, monitor the temperature of specific components, or listen in on electromagnetic radiation, among other things. In theory, this means that someone could eavesdrop on a conversation in a room just by using a computer mouse, since the optical sensor can pick up sound vibrations. Similarly, watching a CPU’s clock speed fluctuate could allow a hacker to steal an encryption key. Even a simple LED light on a badge reader can leak enough data about the device’s inner workings for an attacker to clone a smart card.

The beauty of these indirect data leaks — at least from a hacker’s perspective — is that they’re not easy to spot. Device manufacturers rarely account for them when building security systems. The downside, however, is just as obvious: extracting information through a mechanism that was never meant for data transmission is often complex, slow, and laborious. The Austrian researchers focused on a specific subtype known as a contention side-channel attack. This is where a leak occurs because multiple processes are competing for the same resource. In this case, that contested resource is the storage drive’s bandwidth.

Inside the FROST attack

This specific side channel has actually been studied before, including in a 2025 research paper. Back then, however, the setup was rather straightforward: the researchers ran one program on a computer to act as the data source, while a second program running on the same machine tried to intercept that data. While that’s fine for a theoretical academic study, the attack model wasn’t exactly groundbreaking. After all, if a hacker can already run any program they wish, they don’t need to rely on complex side channels — they have plenty of direct ways to steal the data.

Still, last year’s study wasn’t a complete waste of time. It proved that the resolution obtained from monitoring an SSD is quite high, the data leak is real, and the captured information can actually be useful. The FROST attack is essentially a logical continuation of the same idea.

Here’s how it works in practice. Let’s say there’s a fairly large file on an SSD packed with random data. A specific process reads this data at regular intervals and clocks how fast it gets a response. This speed fluctuates depending on how busy the drive is with other tasks. These access delays are the telltale signs of the drive’s activity. The Austrian researchers demonstrated that plotting these delays over time can help pinpoint with reasonable accuracy what other task is running on the computer at that very moment.

The researchers mapped out latency graphs, like the ones shown above, for a wide variety of websites and locally running apps. What they found were distinct patterns — or digital fingerprints — generated every single time a specific site loads, or an app launches. Capturing these split-second launch or load windows requires monitoring the SSD continuously over a long period of time. However, these patterns proved to be remarkably consistent across different systems; the authors successfully tested their method on both a Linux desktop and an Apple Mac Mini. From there, the next step sounds simple enough: take a catalog of known fingerprints, measure real-world SSD delays, match the two up, and you know exactly what apps the user is opening, and what sites they’re visiting. But how to actually pull off this kind of surveillance under the radar, without planting malware on the victim’s computer?

And that’s where a relatively new browser feature called the origin private file system (OPFS) comes into play. A hypothetical attacker doesn’t have to trick the user into downloading a shady Trojan. All they need do is have the victim visit a specially crafted webpage, and that page will leverage OPFS to quietly track the SSD’s activity. The clever acronym brings all these moving parts together: FROST stands for Fingerprinting Remotely using OPFS-based SSD Timing. Here’s the step-by-step breakdown of how the entire attack plays out:

Method limitations

Like any side-channel attack, FROST isn’t exactly built for speed. It’s a slow, methodical process. To figure out just how slow, the researchers built a dedicated testbed to measure it.

The team ran a program on a computer to transmit data indirectly. Think of it as a digital spy broadcasting a secret message by changing how it interacts with the hard drive. For instance, a 1 in the binary message code could mean the program is actively using the SSD, while a 0 means it’s sitting idle. At the same time, they set up a receiver inside the web browser that accessed the storage drive via OPFS. Because both the browser receiver and the transmitter program were competing for the SSD’s bandwidth, the browser experienced tiny speed delays whenever the transmitter was actively sending data.

This bizarre setup managed to transmit data at 661 bits per second, with nearly 90% accuracy on a Linux desktop with an AMD processor. On an Apple Mac Mini running macOS, the transfer rate hit 719 bits per second, also hovering around 90% accuracy. While these numbers are slightly lower than those in last year’s study — which relied on apps installed directly on the computer — the gap isn’t actually that huge.

That said, the real threat of the FROST attack isn’t raw data transmission; it’s tracking what the user does. Even if a hacker has a database of digital fingerprints for specific apps and websites, the information leaked through a malicious site using OPFS is too noisy. After all, a computer is constantly reading and writing data from/to the SSD in the background. To slice through that digital noise, the researchers turned to a tool that’s becoming standard practice in modern cyberattacks: a neural network. AI trained on known SSD fingerprints could confidently pick out user activity even from a chaotic mess of background data. The final results are eye-opening. On the Apple Mac Mini, the AI accurately identified which website the user opened 89% of the time, and nailed local app launches with 96% accuracy. Crucially, it could even detect what websites were opened in a completely different browser than the one running in the malicious tab. It sounds like a total home run for hackers — except for a massive list of real-world catches.

Is the FROST attack a real-world threat?

Simply knowing which apps are opened or what websites are visited doesn’t give an attacker much leverage. This kind of data is usually useful to advertisers looking to build a user’s digital profile without their permission; however, rolling out this tracking method on a massive scale is hardly realistic. The roadblock comes down to the fundamental way computers handle data: the system regularly dumps frequently accessed data into its RAM. Because the entire FROST attack relies on measuring the relatively slow bandwidth of the physical SSD, the data in RAM is effectively invisible to this method. To bypass this hurdle, the malicious webpage would have to force the OPFS to create a massive file — well over a gigabyte in size. Needless to say, a website that hogs hard drive resources in such an aggressive way would immediately raise red flags. EDR or XDR solutions will most likely flag it as anomalous activity.

Ultimately, this means the FROST attack — like most side-channel spying methods — is only practical for highly targeted operations. But that brings us right back to square one: knowing what apps someone opens or what web pages they browse is a pretty measly reward for the massive effort required to pull off such a sophisticated stunt.

Even so, FROST is light-years ahead of most academic side-channel attacks when it comes to real-world practicality. It doesn’t require preinstalled malware, and the victim doesn’t have to do anything more than open a malicious page. If nothing else, this research is a stark reminder of just how complex modern computers are, and how many unexpected blind spots can lead to data leaks. When building ultra-secure systems for highly classified data, one absolutely has to consider hardware peculiarities. If the prize is big enough, a determined attacker will gladly invest the time to build a hyper-specific complex attack. Research like this serves as proof that, in the world of cybersecurity, that scenario isn’t impossible.

Unchecked AI in the workplace quickly becomes a massive loophole for data leaks and security breaches. All too often, employees drop sensitive company data into public chatbots, or install rogue AI assistants on their own — in the process handing over way too much access. In a previous post, we broke down the different types of risky AI systems, and later shared some tips on how to turn off the built-in AI features on major tech platforms. Today let’s take a look at practical ways to block or restrict the unauthorized “helpers” employees might be using — from ChatGPT and Grammarly, to meeting bots like Fireflies and Read AI.

How to detect and restrict ChatGPT

ChatGPT is the biggest culprit when it comes to unauthorized AI use worldwide. A quick word of warning, though: an outright ban only sends users hunting for sketchy third-party sites or messaging app chatbots that hook into the same service. That’s why it’s always a good idea to offer an approved alternative before pulling the plug.

Detecting it: keep an eye on the NGFW or web filter for traffic heading to chat.openai.com, chatgpt.com, oaistatic.com, oaiusercontent.com, or cdn.oaistatic.com. It’s also smart to use EDR/EPP tools to scan browser histories, installed apps, and browser extensions across corporate devices.

Locking it down: use the firewall or web filter to block the entire AI Services category, and set up DNS to reroute traffic away from those OpenAI domains. Browser policies can also be used to ban ChatGPT-powered extensions. Better yet, block all extensions not on a pre-approved allowlist. Finally, use application controls and EPP solutions to stop users from installing the official desktop app (ChatGPT.exe or com.openai.chat).

How to detect and restrict Claude and Claude Code

Detecting it: use the NGFW or web filter to track traffic going to claude.ai, anthropic.com, *.anthropic.com, and api.anthropic.com. EDR/EPP or application control tools can also be used to scan employee computers for the desktop app (claude.exe).

Locking it down: drop a blanket block on the AI Services category through the NGFW or web filter, and tweak DNS settings to reroute traffic away from the aforementioned Anthropic domains. Next, use browser policies to shut down Claude-powered extensions. Finally, use application controls and the EPP platform to prevent users from installing the desktop app.

How to detect and restrict Perplexity AI

Detecting it: keep tabs on the NGFW or web filter to flag any traffic heading to *.perplexity.ai or pplx.ai.

Locking it down: just like the others, add the AI Services category to the NGFW or web filter blocklist, and use DNS routing to redirect traffic away from those domains.

Configure the browser to block third-party extensions from being installed. If Firefox is used in the organization, be aware that recent versions come with Perplexity built in. Luckily, these AI features can be turned-off company-wide using enterprise policies — specifically, by setting SidebarChatbot = blocked. The full list of tweaks can be found in the Firefox documentation.

How to detect and restrict DeepSeek

Detecting it: keep an eye on the NGFW or web filter for traffic hitting deepseek.com, chat.deepseek.com, api.deepseek.com, or platform.deepseek.com. For better precision, analyze the SNI (server name identification) in TLS connection requests. For mobile devices, look out for the official app (com.deepseek.chat).

Locking it down: blocklist the AI Services category on the NGFW or web filter, and reroute traffic to DeepSeek’s domains via DNS settings. Use browser policies to block third-party extensions, and lean on MDM/EMM tools to restrict the mobile app.

How to detect and restrict Mistral, xAI Grok, and Character.ai

The playbook for these tools is exactly the same as DeepSeek, so here’s the quick list of domains to watch for and block: chat.mistral.ai, mistral.ai, console.mistral.ai, grok.com, x.ai, api.x.ai, character.ai, beta.character.ai, and c.ai.

A quick word of warning on Grok: because Grok is baked into X, blocking this specific AI access point means blocking the entire social media platform.

How to detect and restrict Slack AI

Detecting it: in the Slack workspace admin dashboard, look under Analytics → Slack AI usage. If an enterprise plan is used, the detailed Slack logs can be searched for any events starting with the ai_ prefix.

Blocking it with policies: in the organization’s Slack settings, click through the Workspace settings → Roles & permissions → Feature access, and change the permission to “no one”. Slack has a step-by-step guide in their help center.

Locking it down: shutting this down at the network level is tricky; it can be pulled off with a finely tuned CASB solution in place. Also, don’t forget the importance of blocking rogue integrations and keeping external AI services from tapping into Slack data in the first place. We covered how to lock this down using OAuth controls in a previous post.

How to detect and restrict Zoom AI Companion

Detecting it: if a corporate Zoom subscription is in use, just head to Admin Center → Reports → AI Companion usage. Detecting Zoom’s AI when employees join external meetings or use free accounts is a lot tougher, but email filters can be set up to flag incoming AI-generated meeting notes by scanning for subject lines or text containing “Meeting summary” or “Meeting assets”.

Blocking it with policies: for the company’s own Zoom subscription, go to the Admin Portal → Account Management → Account Settings → Meeting → AI Companion and toggle it OFF for everyone.

Locking it down: unfortunately, AI Companion is baked into Zoom’s DNA, so the only real option is blocking Zoom altogether.

How to detect and restrict Grammarly

What looks like an innocent spellchecker is actually one of the biggest culprits for workplace data leaks.

Detecting it: check the NGFW or web filter logs for traffic hitting grammarly.com, *.grammarly.com, and gnar.grammarly.com. EDR and MDM/EMM tools can also be used to hunt down the standalone desktop apps (Grammarly Desktop.exe and the macOS version), as well as the Grammarly browser extension.

Locking it down: use firewalls to block those domains at the network level, and EPP to stop employees from installing the desktop app, browser extensions, or the Grammarly add-ins for Microsoft Word and Excel.

How to detect and restrict meeting assistants: Fireflies, Read.ai, Tactiq, Fathom, and Granola

This massive category of third-party SaaS tools records and analyzes meetings — creating a massive risk for data leaks. The trickiest part? Outside clients or vendors can bring these bots into a meeting just as easily as employees can.

Detecting them: run an audit on calendar invites, and look for bot participants using email domains like @fireflies.ai, @read.ai, @tactiq.io, @fathom.video, or @granola.ai. Zoom, Teams, or Google Meet logs can also be used to review external participants who joined past calls.

Locking them down: since it’s impossible to control what outsiders do, blocking these bots comes down to tightening meeting rules. The best moves are: blocking users from granting OAuth permissions for bots to join calls, restricting employees from inviting unapproved external participants, or locking down meeting recording access for external users. That last option is usually the least painful way to keep bots out without disrupting business.

How to detect and restrict AI code editors: Cursor, Windsurf, and the like

Detecting them: use EDR/EPP tools to scan for executables like cursor.exe or windsurf.exe. It’s also worth monitoring network traffic heading to cursor.com and windsurf.com, as well as traffic hitting various AI model API providers. Keep in mind that there’s a pretty extensive list of API hosts to monitor here, since these editors aren’t tied to just one specific AI vendor.

Blocking them with policies: these apps can be prevented from being installed by setting up filters based on the developer’s digital signature certificate. Alternatively, a strict application allowlist can be employed where only pre-approved software is allowed to run.

Locking them down: rely on the EPP/EDR platform to actively detect and block these applications from running.

How to detect and restrict local AI tools: Ollama, LM Studio, and GPT4All

On one hand, this category carries fewer data leak risks because the AI models run completely locally on the user’s machine. On the other hand, it opens up a whole new can of worms: these apps themselves aren’t always highly secure, and can become targets for cyberattacks. Plus, it still means that employees can misuse models or process data in unauthorized ways.

Detecting them: EDR/EPP tools are the best line of defense here. They should be used to flag known local AI files and processes like ollama.exe, ollama serve, lmstudio.exe, LM Studio.app, jan.exe, or gpt4all.exe. From a network perspective, it’s worth scanning for open ports on local devices — typically port 1234 for Ollama and LM Studio, or port 8080 for WebUIs (using an additional fingerprint check of the server response). Another massive red flag is the presence of large files (often several gigabytes) containing language model weights. Look out for extensions like .gguf, .bin, or sometimes .safetensors.

Locking them down: use EPP/EDR platforms or windows AppLocker to block these applications by name, or switch to an application allowlist.

How to detect and restrict autonomous agents: OpenClaw, NemoClaw, and NanoClaw

This is easily one of the most dangerous categories of AI tools out there. These agents mix high-level independence with access to untrusted data, making them a massive security headache.

Detecting them: use EPP/EDR tools to sniff out active processes like openclaw, nanoclaw, nemoclaw, or clawdbot. Also keep an eye out for devices running Node.js that suddenly start launching Bash or Python scripts. Another dead giveaway is the appearance of system folders like ~/openclaw, ~/nanoclaw, ~/.claw*, or ~/clawhub. At the network level, monitor connections to the AI model APIs we mentioned earlier, as well as traffic hitting servers like openclaw.ai, nanoclaw.dev, or clawhub.*.

Locking them down: the safest bet is to use strict application allowlisting (only allowing approved software to run), or to specifically ban the known agent apps listed above. On top of that, consider blocking non-developers from installing Node.js and Docker, neither of which they need on their computers anyway.

In April 2026, we discovered a new campaign targeting users of hentai games. Attackers are embedding a remote access Trojan named Argamal into game installers. While concealing its presence, it can remotely control the computer and steal files and personal data.

Here’s how to avoid falling victim to this new Trojan — and how to safely and anonymously enjoy spicy content with (or without) anime girls.

How computers get infected with Argamal

Most of the infected games are distributed through adult game and torrent sites. In some cases, they are posted for download on file-sharing services and linked on gaming websites.

Interestingly, instead of finding a dummy file inside the archive — as is often the case — the user gets the actual game built on popular engines like RenPy or RPG Maker. Infected pirated versions usually turn out to be scams: games fail to launch, folders are full of files with bizarre extensions, making it rather easy to put two and two together. Here, however, the user gets the actual gameplay they expected. Meanwhile, the Trojan lets itself in and keeps a completely low profile.

Tucked right alongside the legitimate files in the archive is a DLL that the game relies on to run, but it’s been rigged: as soon as the user launches the game, the infected DLL automatically loads into memory. There are no outward signs of infection: neither an installer popping up in the background, nor a scary window or prompt asking you to disable your antivirus.

Argamal takes things real slow: instead of immediately rushing to steal files and passwords or throwing a digital rager on your computer, the Trojan first checks whether it’s running in a virtual machine or sandbox, and then goes into standby mode.

During this time, the malware writes hidden parameters to the system, conceals the paths to its DLLs, and delays its own execution. Three days later, the computer connects to GitHub, downloads an encrypted file, decrypts it, and turns it into a working Trojan module.

To ensure persistence, the attackers register the malware under the WindowsColorSystem Calibration Loader system task, a built-in Windows feature that triggers at every user logon to load monitor color profiles. Before shutting down, the malware deletes temporary files and covers its tracks to make it even harder to detect.

What makes Argamal dangerous?

Argamal is a remote access Trojan (RAT), which means attackers can use it to remotely control the victim’s computer. Here’s just a short list of what it may entail:

• Executing arbitrary commands on the computer
• Downloading and running files
• Checking if an antivirus is installed on the PC (by the way, our security solution detects and neutralizes Argamal before it can harm you)
• Searching for and exfiltrating sensitive data from files and system settings
• Taking screenshots and streaming video from the device
• Sending data to the attackers’ server
• Monitoring user activity
• Shutting down or restarting the device

Essentially, the infected computer turns into a remotely controlled machine. The owner may keep calmly going about their day, completely unaware that their device has been compromised. Yet the consequences of such an infection can be devastating.

For example, a single password stolen from a text note can lead to multiple compromised accounts at once if the victim reuses the same credentials across different sites. That’s why we recommend storing strong and unique passwords in an encrypted vault of a password manager rather than in plain text files.

Beyond hijacking accounts, the Trojan lets attackers literally spy on the user — reading their chats, digging into secret files, studying their sexual preferences… The cybercriminals can then use this highly sensitive information for subsequent attacks, blackmail, and extortion. We’ve covered what to do if you find yourself being targeted by extortionists in a previous post.

Another common scenario involves quietly stealing or substituting financial data — for instance, intercepting credentials from banking apps or replacing crypto-wallet addresses in the clipboard, which sends all your money straight to the attackers’ accounts.

In short, there’s a whole laundry list of ways attackers can exploit a victim’s device and data.

Argamal, yamete kudasai! How to protect yourself from similar threats

If you’ve decided to become the proud owner of “Waifu Simulator Ultra Definitive Edition”, stay on your guard:

• Use security software that runs in real time and catches sophisticated malware. Despite the attackers’ best efforts to make the Trojan invisible, Kaspersky Premium instantly detects and removes Argamal from users’ devices.
• Avoid downloading adult apps, installation files, and spicy content from untrusted sources. Clicking a “free XXX game, no signup needed” is a surefire way to invite malware onto your device. That said, even official platforms like Google Play and the App Store unfortunately let infected apps slip through the cracks at times. To stop worrying about accidentally downloading a Trojan or an infostealer, use Kaspersky Premium on all your devices.
• Don’t share more data than you absolutely have to. If an adult game or website insists you sign up, enter personal data, or link third-party accounts instead of just checking your birth date, that’s a huge red flag. Sites rarely collect sensitive data for no reason. In the best-case scenario, it ends up with marketers and ad trackers. In the worst-case, it falls into the hands of bad actors who will use it for blackmail, phishing, or breaking into your other accounts.
• Don’t click ad banners on adult websites. Even the most popular platforms like Pornhub occasionally host ads laced with malware. If you find it hard to hold back, use a security solution that will block malware downloads and prevent redirects to suspicious sites.

• Watching porn safely: a guide for grown-ups
• How to watch porn safely and discreetly
• What happens in the bedroom stays in the bedroom
• AI and the new reality of sextortion