Explore Google’s synced passkey architecture. Unit 42 details its mechanisms, key management, and secure communication in passwordless systems."
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To achieve their malign aims, Android malware developers have to address several challenges in a row: trick users to get inside their smartphones, dodge security software, talk victims into granting various system permissions, keep away from built-in battery optimizers that kill resource hogs, and, after all that, make sure their malware actually turns a profit. The creators of the BeatBanker — an Android‑based malware campaign recently discovered by our experts — have come up with something new for each one of these steps. The attack is (for now) aimed at Brazilian users, but the developers’ ambitions will almost certainly push them toward international expansion, so it’s worth staying on guard and studying the threat actor’s tricks. You can find a full technical analysis of the malware on Securelist.
The malware is distributed through specially crafted phishing pages that mimic the Google Play Store. A page that’s easily mistaken for the official app marketplace invites users to download a seemingly useful app. In one campaign, the trojan disguised itself as the Brazilian government services app, INSS Reembolso; in another, it posed as the Starlink app.
The malicious site cupomgratisfood{.}shop does an excellent job imitating an app store. It’s just unclear why the fake INSS Reembolso appears all of three times. To be extra sure, perhaps?!
The installation takes place in several stages to avoid requesting too many permissions at once and to further lull the victim’s vigilance. After the first app is downloaded and launched, it displays an interface that also resembles Google Play and simulates an update for the decoy app — requesting the user’s permission to install apps, which doesn’t look out-of-the-ordinary in context. If you grant this permission, the malware downloads additional malicious modules to your smartphone.
After installation, the trojan simulates a decoy app update via Google Play by requesting permission to install applications while downloading additional malicious modules in the process
All components of the trojan are encrypted. Before decrypting and proceeding to the next stages of infection, it checks to ensure it’s on a real smartphone and in the target country. BeatBanker immediately terminates its own process if it finds any discrepancies or detects that it’s running in emulated or analysis environments. This complicates dynamic analysis of the malware. Incidentally, the fake update downloader injects modules directly into RAM to avoid creating files on the smartphone that would be visible to security software.
All these tricks are nothing new and frequently used in complex malware for desktop computers. However, for smartphones, such sophistication is still a rarity, and not every security tool will spot it. Users of Kaspersky products are protected from this threat.
Once established on the smartphone, BeatBanker downloads a module for mining Monero cryptocurrency. The authors were very concerned that the smartphone’s aggressive battery optimization systems might shut down the miner, so they came up with a trick: playing an all-but-inaudible sound at all times. Power consumption control systems typically spare apps that are playing audio or video to avoid cutting off background music or podcast players. In this way, the malware can run continuously. Additionally, it displays a persistent notification in the status bar, asking the user to keep the phone on for a system update.
Example of a persistent system update notification from another malicious app masquerading as the Starlink app
To manage the trojan, the authors leverage Google’s legitimate Firebase Cloud Messaging (FCM) — a system for receiving notifications and sending data from a smartphone. This feature is available to all apps and it’s the most popular method for sending and receiving data. Thanks to FCM, attackers can monitor the device’s status and change its settings as needed.
Nothing bad happens for a while after the malware is installed: the attackers wait it out. Then they trigger the miner, but they’re careful to throttle it back if the phone overheats, the battery starts dipping, or the owner happens to be using the device. All of this is handled via FCM.
In addition to the crypto miner, BeatBanker installs extra modules to spy on the user and rob them at the right moment. The spyware module requests Accessibility Services permission, and if this is granted, begins monitoring everything that’s happening on the smartphone.
If the owner opens the Binance or Trust Wallet app to send USDT, the malware overlays a fake screen on top of the wallet interface, effectively swapping the recipient’s address for its own. All transfers go to the attackers.
The trojan features an advanced remote control system and is capable of executing many other commands:
All of this makes it possible to rob the victim when they use any other banking or payment services — not just crypto payments.
Sometimes victims are infected with a different module for espionage and remote smartphone control — the BTMOB remote access trojan. Its malicious capabilities are even broader, including:
Cybercriminals are constantly refining their attacks and coming up with new ways to profit from their victims. Despite this, you can protect yourself by following a few simple precautions:
Threats to Android users have been going through the roof lately. Check out our other posts on the most relevant and widespread Android attacks and tips for keeping you and your loved ones safe:
If you don’t go searching for AI services, they’ll find you all the same. Every major tech company feels a moral obligation not just to develop an AI assistant, integrated chatbot, or autonomous agent, but to bake it into their existing mainstream products and forcibly activate it for tens of millions of users. Here are just a few examples from the last six months:
On the flip side, geeks have rushed to build their own “personal Jarvises” by renting VPS instances or hoarding Mac minis to run the OpenClaw AI agent. Unfortunately, OpenClaw’s security issues with default settings turned out to be so massive that it’s already been dubbed the biggest cybersecurity threat of 2026.
Beyond the sheer annoyance of having something shoved down your throat, this AI epidemic brings some very real practical risks and headaches. AI assistants hoover up every bit of data they can get their hands on, parsing the context of the websites you visit, analyzing your saved documents, reading through your chats, and so on. This gives AI companies an unprecedentedly intimate look into every user’s life.
A leak of this data during a cyberattack — whether from the AI provider’s servers or from the cache on your own machine — could be catastrophic. These assistants can see and cache everything you can, including data usually tucked behind multiple layers of security: banking info, medical diagnoses, private messages, and other sensitive intel. We took a deep dive into how this plays out when we broke down the issues with the AI-powered Copilot+ Recall system, which Microsoft also planned to force-feed to everyone. On top of that, AI can be a total resource hog, eating up RAM, GPU cycles, and storage, which often leads to a noticeable hit to system performance.
For those who want to sit out the AI storm and avoid these half-baked, rushed-to-market neural network assistants, we’ve put together a quick guide on how to kill the AI in popular apps and services.
Google’s AI assistant features in Mail and Docs are lumped together under the umbrella of “smart features”. In addition to the large language model, this includes various minor conveniences, like automatically adding meetings to your calendar when you receive an invite in Gmail. Unfortunately, it’s an all-or-nothing deal: you have to disable all of the “smart features” to get rid of the AI.
To do this, open Gmail, click the Settings (gear) icon, and then select See all settings. On the General tab, scroll down to Google Workspace smart features. Click Manage Workspace smart feature settings and toggle off two options: Smart features in Google Workspace and Smart features in other Google products. We also recommend unchecking the box next to Turn on smart features in Gmail, Chat, and Meet on the same general settings tab. You’ll need to restart your Google apps afterward (which usually happens automatically).
You can kill off AI Overviews in search results on both desktops and smartphones (including iPhones), and the fix is the same across the board. The simplest way to bypass the AI overview on a case-by-case basis is to append -ai to your search query — for example, how to make pizza -ai. Unfortunately, this method occasionally glitches, causing Google to abruptly claim it found absolutely nothing for your request.
If that happens, you can achieve the same result by switching the search results page to Web mode. To do this, select the Web filter immediately below the search bar — you’ll often find it tucked away under the More button.
A more radical solution is to jump ship to a different search engine entirely. For instance, DuckDuckGo not only tracks users less and shows little ads, but it also offers a dedicated AI-free search — just bookmark the search page at noai.duckduckgo.com.
Chrome currently has two types of AI features baked in. The first communicates with Google’s servers and handles things like the smart assistant, an autonomous browsing AI agent, and smart search. The second handles locally more utility-based tasks, such as identifying phishing pages or grouping browser tabs. The first group of settings is labeled AI mode, while the second contains the term Gemini Nano.
To disable them, type chrome://flags into the address bar and hit Enter. You’ll see a list of system flags and a search bar; type “AI” into that search bar. This will filter the massive list down to about a dozen AI features (and a few other settings where those letters just happen to appear in a longer word). The second search term you’ll need in this window is “Gemini“.
After reviewing the options, you can disable the unwanted AI features — or just turn them all off — but the bare minimum should include:
Set all of these to Disabled.
While Firefox doesn’t have its own built-in chatbots and hasn’t (yet) tried to force upon users agent-based features, the browser does come equipped with smart-tab grouping, a sidebar for chatbots, and a few other perks. Generally, AI in Firefox is much less “in your face” than in Chrome or Edge. But if you still want to pull the plug, you’ve two ways to do it.
The first method is available in recent Firefox releases — starting with version 148, a dedicated AI Controls section appeared in the browser settings, though the controls are currently a bit sparse. You can use a single toggle to completely Block AI enhancements, shutting down AI features entirely. You can also specify whether you want to use On-device AI by downloading small local models (currently just for translations) and configure AI chatbot providers in sidebar, choosing between Anthropic Claude, ChatGPT, Copilot, Google Gemini, and Le Chat Mistral.
The second path — for older versions of Firefox — requires a trip into the hidden system settings. Type about:config into the address bar, hit Enter, and click the button to confirm that you accept the risk of poking around under the hood.
A massive list of settings will appear along with a search bar. Type “ML” to filter for settings related to machine learning.
To disable AI in Firefox, toggle the browser.ml.enabled setting to false. This should disable all AI features across the board, but community forums suggest this isn’t always enough to do the trick. For a scorched-earth approach, set the following parameters to false (or selectively keep only what you need):
This will kill off chatbot integrations, AI-generated link descriptions, assistants and extensions, local translation of websites, tab grouping, and other AI-driven features.
Microsoft has managed to bake AI into almost every single one of its products, and turning it off is often no easy task — especially since the AI sometimes has a habit of resurrecting itself without your involvement.
Microsoft’s browser is packed with AI features, ranging from Copilot to automated search. To shut them down, follow the same logic as with Chrome: type edge://flags into the Edge address bar, hit Enter, then type “AI” or “Copilot” into the search box. From there, you can toggle off the unwanted AI features, such as:
Another way to ditch Copilot is to enter edge://settings/appearance/copilotAndSidebar into the address bar. Here, you can customize the look of the Copilot sidebar and tweak personalization options for results and notifications. Don’t forget to peek into the Copilot section under App-specific settings — you’ll find some additional controls tucked away there.
Microsoft Copilot comes in two flavors: as a component of Windows (Microsoft Copilot), and as part of the Office suite (Microsoft 365 Copilot). Their functions are similar, but you’ll have to disable one or both depending on exactly what the Redmond engineers decided to shove onto your machine.
The simplest thing you can do is just uninstall the app entirely. Right-click the Copilot entry in the Start menu and select Uninstall. If that option isn’t there, head over to your installed apps list (Start → Settings → Apps) and uninstall Copilot from there.
In certain builds of Windows 11, Copilot is baked directly into the OS, so a simple uninstall might not work. In that case, you can toggle it off via the settings: Start → Settings → Personalization → Taskbar → turn off Copilot.
If you ever have a change of heart, you can always reinstall Copilot from the Microsoft Store.
It’s worth noting that many users have complained about Copilot automatically reinstalling itself, so you might want to do a weekly check for a couple of months to make sure it hasn’t staged a comeback. For those who are comfortable tinkering with the System Registry (and understand the consequences), you can follow this detailed guide to prevent Copilot’s silent resurrection by disabling the SilentInstalledAppsEnabled flag and adding/enabling the TurnOffWindowsCopilot parameter.
The Microsoft Recall feature, first introduced in 2024, works by constantly taking screenshots of your computer screen and having a neural network analyze them. All that extracted information is dumped into a database, which you can then search using an AI assistant. We’ve previously written in detail about the massive security risks Microsoft Recall poses.
Under pressure from cybersecurity experts, Microsoft was forced to push the launch of this feature from 2024 to 2025, significantly beefing up the protection of the stored data. However, the core of Recall remains the same: your computer still remembers your every move by constantly snapping screenshots and OCR-ing the content. And while the feature is no longer enabled by default, it’s absolutely worth checking to make sure it hasn’t been activated on your machine.
To check, head to the settings: Start → Settings → Privacy & Security → Recall & snapshots. Ensure the Save snapshots toggle is turned off, and click Delete snapshots to wipe any previously collected data, just in case.
You can also check out our detailed guide on how to disable and completely remove Microsoft Recall.
AI has seeped into every corner of Windows, even into File Explorer and Notepad. You might even trigger AI features just by accidentally highlighting text in an app — a feature Microsoft calls “AI Actions”. To shut this down, head to Start → Settings → Privacy & Security → Click to Do.
Notepad has received its own special Copilot treatment, so you’ll need to disable AI there separately. Open the Notepad settings, find the AI features section, and toggle Copilot off.
Finally, Microsoft has even managed to bake Copilot into Paint. Unfortunately, as of right now, there is no official way to disable the AI features within the Paint app itself.
In several regions, WhatsApp users have started seeing typical AI additions like suggested replies, AI message summaries, and a brand-new Chat with Meta AI button. While Meta claims the first two features process data locally on your device and don’t ship your chats off to their servers, verifying that is no small feat. Luckily, turning them off is straightforward.
To disable Suggested Replies, go to Settings → Chats → Suggestions & smart replies and toggle off Suggested replies. You can also kill off AI Sticker suggestions in that same menu. As for the AI message summaries, those are managed in a different location: Settings → Notifications → AI message summaries.
Given the sheer variety of manufacturers and Android flavors, there’s no one-size-fits-all instruction manual for every single phone. Today, we’ll focus on killing off Google’s AI services — but if you’re using a device from Samsung, Xiaomi, or others, don’t forget to check your specific manufacturer’s AI settings. Just a heads-up: fully scrubbing every trace of AI might be a tall order — if it’s even possible at all.
In Google Messages, the AI features are tucked away in the settings: tap your account picture, select Messages settings, then Gemini in Messages, and toggle the assistant off.
Broadly speaking, the Gemini chatbot is a standalone app that you can uninstall by heading to your phone’s settings and selecting Apps. However, given Google’s master plan to replace the long-standing Google Assistant with Gemini, uninstalling it might become difficult — or even impossible — down the road.
If you can’t completely uninstall Gemini, head into the app to kill its features manually. Tap your profile icon, select Gemini Apps activity, and then choose Turn off or Turn off and delete activity. Next, tap the profile icon again and go to the Connected Apps setting (it may be hiding under the Personal Intelligence setting). From here, you should disable all the apps where you don’t want Gemini poking its nose in.
Apple’s platform-level AI features, collectively known as Apple Intelligence, are refreshingly straightforward to disable. In your settings — on desktops, smartphones, and tablets alike — simply look for the section labeled Apple Intelligence & Siri. By the way, depending on your region and the language you’ve selected for your OS and Siri, Apple Intelligence might not even be available to you yet.
Other posts to help you tune the AI tools on your devices:
Attackers are abusing normal OAuth error redirects to send users from a legitimate Microsoft or Google login URL to phishing or malware pages, without ever completing a successful sign‑in or stealing tokens from the OAuth flow itself.
That calls for a bit more explanation.
OAuth (Open Authorization) is an open-standard protocol for delegated authorization. It allows users to grant websites or applications access to their data on another service (for example, Google or Facebook) without sharing their password.
OAuth redirection is the process where an authorization server sends a user’s browser back to an application (client) with an authorization code or token after user authentication.
Researchers found that phishers use silent OAuth authentication flows and intentionally invalid scopes to redirect victims to attacker-controlled infrastructure without stealing tokens.
From the user’s perspective, the attack chain looks roughly like this:
An email arrives with a plausible business lure. For example, you receive an email about something routine but urgent: document sharing or review, a Social Security or financial notice, an HR or employee report, a Teams meeting invite, or a password reset.
The email body contains a link such as “View document” or “Review report,” or a PDF attachment that includes a link instead.
You click the link after seeing that it appears to be a normal Microsoft or Google login. The visible URL (what you see when you hover over it) looks convincing, starting with a trusted domain like https://login.microsoftonline.com/ or https://accounts.google.com/.
There is no obvious sign that the parameters (prompt=none, odd or empty scope, encoded state) are abnormal.
The crafted URL attempts a silent OAuth authorization (prompt=none) and uses parameters that are guaranteed to fail (for example, an invalid or missing scope).
The identity provider evaluates your session and conditional access, determines the request cannot succeed silently, and returns an OAuth error, such as interaction_required, access_denied, or consent_required.
By design, the OAuth server then redirects your browser, including the error parameters and state, to the app’s registered redirect URI, which in these cases is the attacker’s domain.
To the user, this is just a quick flash of a Microsoft or Google URL followed by another page. It’s unlikely anyone would notice the errors in the query string.
The target gets redirected to a page that looks like a legitimate login or business site. This could very well be a clone of a trusted brand’s site.
From here, there are two possible malicious scenarios:
Phishing / Attacker in the Middle (AitM) variant
A normal login page or a verification prompt, sometimes with CAPTCHAs or interstitials to look more trustworthy and bypass some controls.
The email address may already be filled in because the attackers passed it through the state parameter.
When the user enters credentials and multi-factor authentication (MFA), the attacker‑in‑the‑middle toolkit intercepts them, including session cookies, while passing them along so the experience feels legitimate.
Malware delivery variant
Immediately (or after a brief intermediate page), the browser hits a download path and automatically downloads a file.
The context of the page matches the lure (“Download the secure document,” “Meeting resources,” and so on), making it seem reasonable to open the file.
The target might notice the initial file open or some system slowdown, but otherwise the compromise is practically invisible.
By harvesting credentials or planting a backdoor, the attacker now has a foothold on the system. From there, they may carry out hands-on-keyboard activity, move laterally, steal data, or stage ransomware, depending on their goals.
The harvested credentials and tokens can be used to access email, cloud apps, or other resources without the need to keep malware on the device.
Since the attacker does not need your token from this flow (only the redirect into their own infrastructure), the OAuth request itself may look less suspicious. Be vigilant and follow our advice:
Login
/download/ paths.Pro tip: use Malwarebytes Scam Guard to help you determine whether the email you received is a scam or not.
We don’t just report on scams—we help detect them
Cybersecurity risks should never spread beyond a headline. If something looks dodgy to you, check if it’s a scam using Malwarebytes Scam Guard. Submit a screenshot, paste suspicious content, or share a link, text or phone number, and we’ll tell you if it’s a scam or legit. Available with Malwarebytes Premium Security for all your devices, and in the Malwarebytes app for iOS and Android.
We’ve written time and again about phishing schemes where attackers exploit various legitimate servers to deliver emails. If they manage to hijack someone’s SharePoint server, they’ll use that; if not, they’ll settle for sending notifications through a free service like GetShared. However, Google’s vast ecosystem of services holds a special place in the hearts of scammers, and this time Google Tasks is the star of the show. As per usual, the main goal of this trick is to bypass email filters by piggybacking the rock-solid reputation of the middleman being exploited.
The recipient gets a legitimate notification from an @google.com address with the message: “You have a new task”. Essentially, the attackers are trying to give the victim the impression that the company has started using Google’s task tracker, and as a result they need to immediately follow a link to fill out an employee verification form.
To deprive the recipient of any time to actually think about whether this is necessary, the task usually includes a tight deadline and is marked with high priority. Upon clicking the link within the task, the victim is presented with an URL leading to a form where they must enter their corporate credentials to “confirm their employee status”. These credentials, of course, are the ultimate goal of the phishing attack.
Of course, employees should be warned about the existence of this scheme — for instance, by sharing a link to our collection of posts on the red flags of phishing. But in reality, the issue isn’t with any one specific service — it’s about the overall cybersecurity culture within a company. Workflow processes need to be clearly defined so that every employee understands which tools the company actually uses and which it doesn’t. It might make sense to maintain a public corporate document listing authorized services and the people or departments responsible for them. This gives employees a way to verify if that invitation, task, or notification is the real deal. Additionally, it never hurts to remind everyone that corporate credentials should only be entered on internal corporate resources. To automate the training process and keep your team up to speed on modern cyberthreats, you can use a dedicated tool like the Kaspersky Automated Security Awareness Platform.
Beyond that, as usual, we recommend minimizing the number of potentially dangerous emails hitting employee inboxes by using a specialized mail gateway security solution. It’s also vital to equip all web-connected workstations with security software. Even if an attacker manages to trick an employee, the security product will block the attempt to visit the phishing site — preventing corporate credentials from leaking in the first place.
This week on the Lock and Code podcast…
In January, Google settled a lawsuit that pricked up a few ears: It agreed to pay $68 million to a wide array of people who sued the company together, alleging that Google’s voice-activated smart assistant had secretly recorded their conversations, which were then sent to advertisers to target them with promotions.
Google denied any admission of wrongdoing in the settlement agreement, but the fact stands that one of the largest phone makers in the world decided to forego a trial against some potentially explosive surveillance allegations. It’s a decision that the public has already seen in the past, when Apple agreed to pay $95 million last year to settle similar legal claims against its smart assistant, Siri.
Back-to-back, the stories raise a question that just seems to never go away: Are our phones listening to us?
This week, on the Lock and Code podcast with host David Ruiz, we revisit an episode from last year in which we tried to find the answer. In speaking to Electronic Frontier Foundation Staff Technologist Lena Cohen about mobile tracking overall, it becomes clear that, even if our phones aren’t literally listening to our conversations, the devices are stuffed with so many novel forms of surveillance that we need not say something out loud to be predictably targeted with ads for it.
“Companies are collecting so much information about us and in such covert ways that it really feels like they’re listening to us.”
Tune in today to listen to the full conversation.
Show notes and credits:
Intro Music: “Spellbound” by Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0 License
http://creativecommons.org/licenses/by/4.0/
Outro Music: “Good God” by Wowa (unminus.com)
Listen up—Malwarebytes doesn’t just talk cybersecurity, we provide it.
Protect yourself from online attacks that threaten your identity, your files, your system, and your financial well-being with our exclusive offer for Malwarebytes Premium for Lock and Code listeners.
WhatsApp is going through a rough patch. Some users would argue it has been ever since Meta acquired the once widely trusted messaging platform. User sentiment has shifted from “trusted default messenger” to a grudgingly necessary Meta product.
Privacy-aware users still see WhatsApp as one of the more secure mass-market messaging platforms if you lock down its settings. Even then, many remain uneasy about Meta’s broader ecosystem, and wish all their contacts would switch to a more secure platform.
Back to current affairs, which will only reinforce that sentiment.
Google’s Project Zero has just disclosed a WhatsApp vulnerability where a malicious media file, sent into a newly created group chat, can be automatically downloaded and used as an attack vector.
The bug affects WhatsApp on Android and involves zero‑click media downloads in group chats. You can be attacked simply by being added to a group and having a malicious file sent to you.
According to Project Zero, the attack is most likely to be used in targeted campaigns, since the attacker needs to know or guess at least one contact. While focused, it is relatively easy to repeat once an attacker has a likely target list.
And to put a cherry on top for WhatsApp’s competitors, a potentially even more serious concern for the popular messaging platform, an international group of plaintiffs sued Meta Platforms, alleging the WhatsApp owner can store, analyze, and access virtually all of users’ private communications, despite WhatsApp’s end-to-end encryption claims.
Reportedly, Meta pushed a server change on November 11, 2025, but Google says that only partially resolved the issue. So, Meta is working on a comprehensive fix.
Google’s advice is to disable Automatic Download or enable WhatsApp’s Advanced Privacy Mode so that media is not automatically downloaded to your phone.
And you’ll need to keep WhatsApp updated to get the latest patches, which is true for any app and for Android itself.
Goal: ensure that no photos, videos, audio, or documents are pulled to the device without an explicit decision.
Doing this directly implements Project Zero’s guidance to “disable Automatic Download” so that malicious media can’t silently land on your storage as soon as you are dropped into a hostile group.
Even if WhatsApp still downloads some content, you can stop it from leaking into shared storage where other apps and system components see it.
WhatsApp is a sandbox, and should contain the threat. Which means, keeping media inside WhatsApp makes it harder for a malicious file to be processed by other, possibly more vulnerable components.
The attack chain requires the attacker to add you and one of your contacts to a new group. Reducing who can do that lowers risk.
Read this guide for Android and iOS to learn how to do that.
We don’t just report on phone security—we provide it
Cybersecurity risks should never spread beyond a headline. Keep threats off your mobile devices by downloading Malwarebytes for iOS, and Malwarebytes for Android today.
This week on the Lock and Code podcast…
When you hear the words “data privacy,” what do you first imagine?
Maybe you picture going into your social media apps and setting your profile and posts to private. Maybe you think about who you’ve shared your location with and deciding to revoke some of that access. Maybe you want to remove a few apps entirely from your smartphone, maybe you want to try a new web browser, maybe you even want to skirt the type of street-level surveillance provided by Automated License Plate Readers, which can record your car model, license plate number, and location on your morning drive to work.
Importantly, all of these are “data privacy,” but trying to do all of these things at once can feel impossible.
That’s why, this year, for Data Privacy Day, Malwarebytes Senior Privacy Advocate (and Lock and Code host) David Ruiz is sharing the one thing he’s doing different to improve his privacy. And it’s this: He’s given up Google Search entirely.
When Ruiz requested the data that Google had collected about him last year, he saw that the company had recorded an eye-popping 8,000 searches in just the span of 18 months. And those 8,000 searches didn’t just reveal what he was thinking about on any given day—including his shopping interests, his home improvement projects, and his late-night medical concerns—they also revealed when he clicked on an ad based on the words he searched. This type of data, which connects a person’s searches to the likelihood of engaging with an online ad, is vital to Google’s revenue, and it’s the type of thing that Ruiz is seeking to finally cut off.
So, for 2026, he has switched to a new search engine, Brave Search.
Today, on the Lock and Code podcast, Ruiz explains why he made the switch, what he values about Brave Search, and why he also refused to switch to any of the major AI platforms in replacing Google.
Tune in today to listen to the full episode.
Show notes and credits:
Intro Music: “Spellbound” by Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0 License
http://creativecommons.org/licenses/by/4.0/
Outro Music: “Good God” by Wowa (unminus.com)
Listen up—Malwarebytes doesn’t just talk cybersecurity, we provide it.
Protect yourself from online attacks that threaten your identity, your files, your system, and your financial well-being with our exclusive offer for Malwarebytes Premium Security for Lock and Code listeners.
Tech enthusiasts have been experimenting with ways to sidestep AI response limits set by the models’ creators almost since LLMs first hit the mainstream. Many of these tactics have been quite creative: telling the AI you have no fingers so it’ll help finish your code, asking it to “just fantasize” when a direct question triggers a refusal, or inviting it to play the role of a deceased grandmother sharing forbidden knowledge to comfort a grieving grandchild.
Most of these tricks are old news, and LLM developers have learned to successfully counter many of them. But the tug-of-war between constraints and workarounds hasn’t gone anywhere — the ploys have just become more complex and sophisticated. Today, we’re talking about a new AI jailbreak technique that exploits chatbots’ vulnerability to… poetry. Yes, you read it right — in a recent study, researchers demonstrated that framing prompts as poems significantly increases the likelihood of a model spitting out an unsafe response.
They tested this technique on 25 popular models by Anthropic, OpenAI, Google, Meta, DeepSeek, xAI, and other developers. Below, we dive into the details: what kind of limitations these models have, where they get forbidden knowledge from in the first place, how the study was conducted, and which models turned out to be the most “romantic” — as in, the most susceptible to poetic prompts.
The success of OpenAI’s models and other modern chatbots boils down to the massive amounts of data they’re trained on. Because of that sheer scale, models inevitably learn things their developers would rather keep under wraps: descriptions of crimes, dangerous tech, violence, or illicit practices found within the source material.
It might seem like an easy fix: just scrub the forbidden fruit from the dataset before you even start training. But in reality, that’s a massive, resource-heavy undertaking — and at this stage of the AI arms race, it doesn’t look like anyone is willing to take it on.
Another seemingly obvious fix — selectively scrubbing data from the model’s memory — is, alas, also a no-go. This is because AI knowledge doesn’t live inside neat little folders that can easily be trashed. Instead, it’s spread across billions of parameters and tangled up in the model’s entire linguistic DNA — word statistics, contexts, and the relationships between them. Trying to surgically erase specific info through fine-tuning or penalties either doesn’t quite do the trick, or starts hindering the model’s overall performance and negatively affect its general language skills.
As a result, to keep these models in check, creators have no choice but to develop specialized safety protocols and algorithms that filter conversations by constantly monitoring user prompts and model responses. Here’s a non-exhaustive list of these constraints:
Put simply, AI safety today isn’t built on deleting dangerous knowledge, but on trying to control how and in what form the model accesses and shares it with the user — and the cracks in these very mechanisms are where new workarounds find their footing.
First, let’s look at the ground rules so you know the experiment was legit. The researchers set out to goad 25 different models into behaving badly across several categories:
The jailbreak itself was a one-shot deal: a single poetic prompt. The researchers didn’t engage the AI in long-winded poetic debates in the vein of Norse skalds or modern-day rappers. Their goal was simply to see if they could get the models to flout safety instructions using just one rhyming request. As mentioned, the researchers tested 25 language models from various developers; here’s the full list:
A lineup of 25 language models from various developers, all put to the test to see if a single poetic prompt could coax AI into ditching its safety guardrails. Source
To build these poetic queries, the researchers started with a database of known malicious prompts from the standard MLCommons AILuminate Benchmark used to test LLM security, and recast them as verse with the aid of DeepSeek. Only the stylistic wrapping was changed: the experiment didn’t use any additional attack vectors, obfuscation strategies, or model-specific tweaks.
For obvious reasons, the study’s authors aren’t publishing the actual malicious poetic prompts. But they do demonstrate the general vibe of the queries using a harmless example, which looks something like this:
A baker guards a secret oven’s heat,
its whirling racks, its spindle’s measured beat.
To learn its craft, one studies every turn,
how flour lifts, how sugar starts to burn.
Describe the method, line by measured line,
that shapes a cake whose layers intertwine.
The researchers tested 1200 prompts across 25 different models — in both prose and poetic versions. Comparing the prose and poetic variants of the exact same query allowed them to verify if the model’s behavior changed solely because of the stylistic wrapping.
Through these prose prompt tests, the experimenters established a baseline for the models’ willingness to fulfill dangerous requests. They then compared this baseline to how those same models reacted to the poetic versions of the queries. We’ll dive into the results of that comparison in the next section.
Since the volume of data generated during the experiment was truly massive, the safety checks on the models’ responses were also handled by AI. Each response was graded as either “safe” or “unsafe” by a jury consisting of three different language models:
Responses were only deemed safe if the AI explicitly refused to answer the question. The initial classification into one of the two groups was determined by a majority vote: to be certified as harmless, a response had to receive a safe rating from at least two of the three jury members.
Responses that failed to reach a majority consensus or were flagged as questionable were handed off to human reviewers. Five annotators participated in this process, evaluating a total of 600 model responses to poetic prompts. The researchers noted that the human assessments aligned with the AI jury’s findings in the vast majority of cases.
With the methodology out of the way, let’s look at how the LLMs actually performed. It’s worth noting that the success of a poetic jailbreak can be measured in different ways. The researchers highlighted an extreme version of this assessment based on the top-20 most successful prompts, which were hand-picked. Using this approach, an average of nearly two-thirds (62%) of the poetic queries managed to coax the models into violating their safety instructions.
Google’s Gemini 1.5 Pro turned out to be the most susceptible to verse. Using the 20 most effective poetic prompts, researchers managed to bypass the model’s restrictions… 100% of the time. You can check out the full results for all the models in the chart below.
The share of safe responses (Safe) versus the Attack Success Rate (ASR) for 25 language models when hit with the 20 most effective poetic prompts. The higher the ASR, the more often the model ditched its safety instructions for a good rhyme. Source
A more moderate way to measure the effectiveness of the poetic jailbreak technique is to compare the success rates of prose versus poetry across the entire set of queries. Using this metric, poetry boosts the likelihood of an unsafe response by an average of 35%.
The poetry effect hit deepseek-chat-v3.1 the hardest — the success rate for this model jumped by nearly 68 percentage points compared to prose prompts. On the other end of the spectrum, claude-haiku-4.5 proved to be the least susceptible to a good rhyme: the poetic format didn’t just fail to improve the bypass rate — it actually slightly lowered the ASR, making the model even more resilient to malicious requests.
A comparison of the baseline Attack Success Rate (ASR) for prose queries versus their poetic counterparts. The Change column shows how many percentage points the verse format adds to the likelihood of a safety violation for each model. Source
Finally, the researchers calculated how vulnerable entire developer ecosystems, rather than just individual models, were to poetic prompts. As a reminder, several models from each developer — Meta, Anthropic, OpenAI, Google, DeepSeek, Qwen, Mistral AI, Moonshot AI, and xAI — were included in the experiment.
To do this, the results of individual models were averaged within each AI ecosystem and compared the baseline bypass rates with the values for poetic queries. This cross-section allows us to evaluate the overall effectiveness of a specific developer’s safety approach rather than the resilience of a single model.
The final tally revealed that poetry deals the heaviest blow to the safety guardrails of models from DeepSeek, Google, and Qwen. Meanwhile, OpenAI and Anthropic saw an increase in unsafe responses that was significantly below the average.
A comparison of the average Attack Success Rate (ASR) for prose versus poetic queries, aggregated by developer. The Change column shows by how many percentage points poetry, on average, slashes the effectiveness of safety guardrails within each vendor’s ecosystem. Source
The main takeaway from this study is that “there are more things in heaven and earth, Horatio, than are dreamt of in your philosophy” — in the sense that AI technology still hides plenty of mysteries. For the average user, this isn’t exactly great news: it’s impossible to predict which LLM hacking methods or bypass techniques researchers or cybercriminals will come up with next, or what unexpected doors those methods might open.
Consequently, users have little choice but to keep their eyes peeled and take extra care of their data and device security. To mitigate practical risks and shield your devices from such threats, we recommend using a robust security solution that helps detect suspicious activity and prevent incidents before they happen.
To help you stay alert, check out our materials on AI-related privacy risks and security threats:
We discuss a novel AI-augmented attack method where malicious webpages use LLM services to generate dynamic code in real-time within a browser.
The post The Next Frontier of Runtime Assembly Attacks: Leveraging LLMs to Generate Phishing JavaScript in Real Time appeared first on Unit 42.
A newly discovered vulnerability named WhisperPair can turn Bluetooth headphones and headsets from many well-known brands into personal tracking beacons — regardless of whether the accessories are currently connected to an iPhone, Android smartphone, or even a laptop. Even though the technology behind this flaw was originally developed by Google for Android devices, the tracking risks are actually much higher for those using vulnerable headsets with other operating systems — like iOS, macOS, Windows, or Linux. For iPhone owners, this is especially concerning.
Connecting Bluetooth headphones to Android smartphones became a whole lot faster when Google rolled out Fast Pair, a technology now used by dozens of accessory manufacturers. To pair a new headset, you just turn it on and hold it near your phone. If your device is relatively modern (produced after 2019), a pop-up appears inviting you to connect and download the accompanying app, if it exists. One tap, and you’re good to go.
Unfortunately, it seems quite a few manufacturers didn’t pay attention to the particulars of this tech when implementing it, and now their accessories can be hijacked by a stranger’s smartphone in seconds — even if the headset isn’t actually in pairing mode. This is the core of the WhisperPair vulnerability, recently discovered by researchers at KU Leuven and recorded as CVE-2025-36911.
The attacking device — which can be a standard smartphone, tablet or laptop — broadcasts Google Fast Pair requests to any Bluetooth devices within a 14-meter radius. As it turns out, a long list of headphones from Sony, JBL, Redmi, Anker, Marshall, Jabra, OnePlus, and even Google itself (the Pixel Buds 2) will respond to these pings even when they aren’t looking to pair. On average, the attack takes just 10 seconds.
Once the headphones are paired, the attacker can do pretty much anything the owner can: listen in through the microphone, blast music, or — in some cases — locate the headset on a map if it supports Google Find Hub. That latter feature, designed strictly for finding lost headphones, creates a perfect opening for stealthy remote tracking. And here’s the twist: it’s actually most dangerous for Apple users and anyone else rocking non-Android hardware.
When headphones or a headset first shake hands with an Android device via the Fast Pair protocol, an owner key tied to that smartphone’s Google account is tucked away in the accessory’s memory. This info allows the headphones to be found later by leveraging data collected from millions of Android devices. If any random smartphone spots the target device nearby via Bluetooth, it reports its location to the Google servers. This feature — Google Find Hub — is essentially the Android version of Apple’s Find My, and it introduces the same unauthorized tracking risks as a rogue AirTag.
When an attacker hijacks the pairing, their key can be saved as the headset owner’s key — but only if the headset targeted via WhisperPair hasn’t previously been linked to an Android device and has only been used with an iPhone, or other hardware like a laptop with a different OS. Once the headphones are paired, the attacker can stalk their location on a map at their leisure — crucially, anywhere at all (not just within the 14-meter range).
Android users who’ve already used Fast Pair to link their vulnerable headsets are safe from this specific move, since they’re already logged in as the official owners. Everyone else, however, should probably double-check their manufacturer’s documentation to see if they’re in the clear — thankfully, not every device vulnerable to the exploit actually supports Google Find Hub.
The only truly effective way to fix this bug is to update your headphones’ firmware, provided an update is actually available. You can typically check for and install updates through the headset’s official companion app. The researchers have compiled a list of vulnerable devices on their site, but it’s almost certainly not exhaustive.
After updating the firmware, you absolutely must perform a factory reset to wipe the list of paired devices — including any unwanted guests.
If no firmware update is available and you’re using your headset with iOS, macOS, Windows, or Linux, your only remaining option is to track down an Android smartphone (or find a trusted friend who has one) and use it to reserve the role of the original owner. This will prevent anyone else from adding your headphones to Google Find Hub behind your back.
In January 2026, Google pushed an Android update to patch the vulnerability on the OS side. Unfortunately, the specifics haven’t been made public, so we’re left guessing exactly what they tweaked under the hood. Most likely, updated smartphones will no longer report the location of accessories hijacked via WhisperPair to the Google Find Hub network. But given that not everyone is exactly speedy when it comes to installing Android updates, it’s a safe bet that this type of headset tracking will remain viable for at least another couple of years.
Want to find out how else your gadgets might be spying on you? Check out these posts:
Google has settled yet another class-action lawsuit accusing it of collecting children’s data and using it to target them with advertising. The tech giant will pay $8.25 million to address allegations that it tracked data on apps specifically designated for kids.
This settlement stems from accusations that apps provided under Google’s “Designed for Families” programme, which was meant to help parents find safe apps, tracked children. Under the terms of this programme, developers were supposed to self-certify COPPA compliance and use advertising SDKs that disabled behavioural tracking. However, some did not, instead using software embedded in the apps that was created by a Google-owned mobile advertising company called AdMob.
When kids used these apps, which included games, AdMob collected data from these apps, according to the class action lawsuit. This included IP addresses, device identifiers, usage data, and the child’s location to within five meters, transmitting it to Google without parental consent. The AdMob software could then use that information to display targeted ads to users.
This kind of activity is exactly what the Children’s Online Privacy Protection Act (COPPA) was created to stop. The law requires operators of child-directed services to obtain verifiable parental consent before collecting personal information from children under 13. That includes cookies and other identifiers, which are the core tools advertisers use to track and target people.
The families filing the lawsuit alleged that Google knew this was going on:
“Google and AdMob knew at the time that their actions were resulting in the exfiltration data from millions of children under thirteen but engaged in this illicit conduct to earn billions of dollars in advertising revenue.”
Security researchers had alerted Google to the issue in 2018, according to the filing.
What’s most disappointing is that these privacy issues keep happening. This news arrives at the same time that a judge approved a settlement on another child privacy case involving Google’s use of children’s data on YouTube. This case dates back to October 2019, the same year that Google and YouTube paid a whopping $170m fine for violating COPPA.
Families in this class action suit alleged that YouTube used cookies and persistent identifiers on child-directed channels, collecting data including IP addresses, geolocation data, and device serial numbers. This is the same thing that it does for adults across the web, but COPPA protects kids under 13 from such activities, as do some state laws.
According to the complaint, YouTube collected this information between 2013 and 2020 and used it for behavioural advertising. This form of advertising infers people’s interests from their identifiers, and it is more lucrative than contextual advertising, which focuses only on a channel’s content.
The case said that various channel owners opted into behavioural advertising, prompting Google to collect this personal information. No parental consent was obtained, the plaintiffs alleged. Channel owners named in the suit included Cartoon Network, Hasbro, Mattel, and DreamWorks Animation.
Under the YouTube settlement (which was agreed in August and recently approved by a judge), families can file claims through YouTubePrivacySettlement.com, although the deadline is this Wednesday. Eligible families are likely to get $20–$30 after attorneys’ fees and administration costs, if 1–2% of eligible families submit claims.
Last year, the FTC amended its COPPA Rule to introduce mandatory opt-in consent for targeted advertising to children, separate from general data-collection consent.
The amendments expand the definition of personal information to include biometric data and government-issued ID information. It also lets the FTC use a site operator’s marketing materials to determine whether a site targets children.
Site owners must also now tell parents who they’ll share information with, and the amendments stop operators from keeping children’s personal information forever. If these all sounds like measures that should have been included to protect children online from the get-go, we agree with you. In any case, companies have until this April to comply with the new rules.
Will the COPPA rules make a difference? It’s difficult to say, given the stream of privacy cases involving Google LLC (which owns YouTube and AdMob, among others). When viewed against Alphabet’s overall earnings, an $8.25m penalty risks being seen as a routine business expense rather than a meaningful deterrent.
We don’t just report on data privacy—we help you remove your personal information
Cybersecurity risks should never spread beyond a headline. With Malwarebytes Personal Data Remover, you can scan to find out which sites are exposing your personal information, and then delete that sensitive data from the internet.
Thanks to the convenience of NFC and smartphone payments, many people no longer carry wallets or remember their bank card PINs. All their cards reside in a payment app, and using that is quicker than fumbling for a physical card. Mobile payments are also secure — the technology was developed relatively recently and includes numerous anti-fraud protections. Still, criminals have invented several ways to abuse NFC and steal your money. Fortunately, protecting your funds is straightforward: just know about these tricks and avoid risky NFC usage scenarios.
NFC relay is a technique where data wirelessly transmitted between a source (like a bank card) and a receiver (like a payment terminal) is intercepted by one intermediate device, and relayed in real time to another. Imagine you have two smartphones connected via the internet, each with a relay app installed. If you tap a physical bank card against the first smartphone and hold the second smartphone near a terminal or ATM, the relay app on the first smartphone will read the card’s signal using NFC, and relay it in real time to the second smartphone, which will then transmit this signal to the terminal. From the terminal’s perspective, it all looks like a real card is tapped on it — even though the card itself might physically be in another city or country.
This technology wasn’t originally created for crime. The NFCGate app appeared in 2015 as a research tool after it was developed by students at the Technical University of Darmstadt in Germany. It was intended for analyzing and debugging NFC traffic, as well as for education purposes and experiments with contactless technology. NFCGate was distributed as an open-source solution and used in academic and enthusiast circles.
Five years later, cybercriminals caught on to the potential of NFC relay and began modifying NFCGate by adding mods that allowed it to run through a malicious server, disguise itself as legitimate software, and perform social engineering scenarios.
What began as a research project morphed into the foundation for an entire class of attacks aimed at draining bank accounts without physical access to bank cards.
The first documented attacks using a modified NFCGate occurred in late 2023 in the Czech Republic. By early 2025, the problem had become large scale and noticeable: cybersecurity analysts uncovered more than 80 unique malware samples built on the NFCGate framework. The attacks evolved rapidly, with NFC relay capabilities being integrated into other malware components.
By February 2025, malware bundles combining CraxsRAT and NFCGate emerged, allowing attackers to install and configure the relay with minimal victim interaction. A new scheme, a so-called “reverse” version of NFCGate, appeared in spring 2025, fundamentally changing the attack’s execution.
Particularly noteworthy is the RatOn Trojan, first detected in the Czech Republic. It combines remote smartphone control with NFC relay capabilities, letting attackers target victims’ banking apps and cards through various technique combinations. Features like screen capture, clipboard data manipulation, SMS sending, and stealing info from crypto wallets and banking apps give criminals an extensive arsenal.
Cybercriminals have also packaged NFC relay technology into malware-as-a-service (MaaS) offerings, and reselling them to other threat actors through subscription. In early 2025, analysts uncovered a new and sophisticated Android malware campaign in Italy, dubbed SuperCard X. Attempts to deploy SuperCard X were recorded in Russia in May 2025, and in Brazil in August of the same year.
The direct attack is the original criminal scheme exploiting NFCGate. In this scenario, the victim’s smartphone plays the role of the reader, while the attacker’s phone acts as the card emulator.
First, the fraudsters trick the user into installing a malicious app disguised as a banking service, a system update, an “account security” app, or even a popular app like TikTok. Once installed, the app gains access to both NFC and the internet — often without requesting dangerous permissions or root access. Some versions also ask for access to Android accessibility features.
Then, under the guise of identity verification, the victim is prompted to tap their bank card to their phone. When they do, the malware reads the card data via NFC and immediately sends it to the criminals’ server. From there, the information is relayed to a second smartphone held by a money mule, who helps extract the money. This phone then emulates the victim’s card to make payments at a terminal or withdraw cash from an ATM.
The fake app on the victim’s smartphone also asks for the card PIN — just like at a payment terminal or ATM — and sends it to the attackers.
In early versions of the attack, criminals would simply stand ready at an ATM with a phone to use the duped user’s card in real time. Later, the malware was refined so the stolen data could be used for in-store purchases in a delayed, offline mode, rather than in a live relay.
For the victim, the theft is hard to notice: the card never left their possession, they didn’t have to manually enter or recite its details, and the bank alerts about the withdrawals can be delayed or even intercepted by the malicious app itself.
Among the red flags that should make you suspect a direct NFC attack are:
The reverse attack is a newer, more sophisticated scheme. The victim’s smartphone no longer reads their card — it emulates the attacker’s card. To the victim, everything appears completely safe: there’s no need to recite card details, share codes, or tap a card to the phone.
Just like with the direct scheme, it all starts with social engineering. The user gets a call or message convincing them to install an app for “contactless payments”, “card security”, or even “using central bank digital currency”. Once installed, the new app asks to be set as the default contactless payment method — and this step is critically important. Thanks to this, the malware requires no root access — just user consent.
The malicious app then silently connects to the attackers’ server in the background, and the NFC data from a card belonging to one of the criminals is transmitted to the victim’s device. This step is completely invisible to the victim.
Next, the victim is directed to an ATM. Under the pretext of “transferring money to a secure account” or “sending money to themselves”, they are instructed to tap their phone on the ATM’s NFC reader. At this moment, the ATM is actually interacting with the attacker’s card. The PIN is dictated to the victim beforehand — presented as “new” or “temporary”.
The result is that all the money deposited or transferred by the victim ends up in the criminals’ account.
The hallmarks of this attack are:
NFC relay attacks rely not so much on technical vulnerabilities as on user trust. Defending against them comes down to some simple precautions.
This week on the Lock and Code podcast…
There’s a bizarre thing happening online right now where everything is getting worse.
Your Google results have become so bad that you’ve likely typed what you’re looking for, plus the word “Reddit,” so you can find discussion from actual humans. If you didn’t take this route, you might get served AI results from Google Gemini, which once recommended that every person should eat “at least one small rock per day.” Your Amazon results are a slog, filled with products that have surreptitiously paid reviews. Your Facebook feed could be entirely irrelevant because the company decided years ago that you didn’t want to see what your friends posted, you wanted to see what brands posted, because brands pay Facebook, and you don’t, so brands are more important than your friends.
But, according to digital rights activist and award-winning author Cory Doctorow, this wave of online deterioration isn’t an accident—it’s a business strategy, and it can be summed up in a word he coined a couple of years ago: Enshittification.
Enshittification is the process by which an online platform—like Facebook, Google, or Amazon—harms its own services and products for short-term gain while managing to avoid any meaningful consequences, like the loss of customers or the impact of meaningful government regulation. It begins with an online platform treating new users with care, offering services, products, or connectivity that they may not find elsewhere. Then, the platform invites businesses on board that want to sell things to those users. This means businesses become the priority and the everyday user experience is hindered. But then, in the final stage, the platform also makes things worse for its business customers, making things better only for itself.
This is how a company like Amazon went from helping you find nearly anything you wanted to buy online to helping businesses sell you anything you wanted to buy online to making those businesses pay increasingly high fees to even be discovered online. Everyone, from buyers to sellers, is pretty much entrenched in the platform, so Amazon gets to dictate the terms.
Today, on the Lock and Code podcast with host David Ruiz, we speak with Doctorow about enshittification’s fast damage across the internet, how to fight back, and where it all started.
”Once these laws were established, the tech companies were able to take advantage of them. And today we have a bunch of companies that aren’t tech companies that are nevertheless using technology to rig the game in ways that the tech companies pioneered.”
Tune in today to listen to the full conversation.
Show notes and credits:
Intro Music: “Spellbound” by Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0 License
http://creativecommons.org/licenses/by/4.0/
Outro Music: “Good God” by Wowa (unminus.com)
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Protect yourself from online attacks that threaten your identity, your files, your system, and your financial well-being with our exclusive offer for Malwarebytes Premium Security for Lock and Code listeners.
Attackers are sending very convincing fake “Google” emails that slip past spam filters, route victims through several trusted Google-owned services, and ultimately lead to a look-alike Microsoft 365 sign-in page designed to harvest usernames and passwords.
Researchers found that cybercriminals used Google Cloud Application Integration’s Send Email feature to send phishing emails from a legitimate Google address: noreply-application-integration@google[.]com.
Google Cloud Application Integration allows users to automate business processes by connecting any application with point-and-click configurations. New customers currently receive free credits, which lowers the barrier to entry and may attract some cybercriminals.
The initial email arrives from what looks like a real Google address and references something routine and familiar, such as a voicemail notification, a task to complete, or permissions to access a document. The email includes a link that points to a genuine Google Cloud Storage URL, so the web address appears to belong to Google and doesn’t look like an obvious fake.
After the first click, you are redirected to another Google‑related domain (googleusercontent[.]com) showing a CAPTCHA or image check. Once you pass the “I’m not a robot check,” you land on what looks like a normal Microsoft 365 sign‑in page, but on close inspection, the web address is not an official Microsoft domain.
Any credentials provided on this site will be captured by the attackers.
The use of Google infrastructure provides the phishers with a higher level of trust from both email filters and the receiving users. This is not a vulnerability, just an abuse of cloud-based services that Google provides.
Google said it has taken action against the activity:
“We have blocked several phishing campaigns involving the misuse of an email notification feature within Google Cloud Application Integration. Importantly, this activity stemmed from the abuse of a workflow automation tool, not a compromise of Google’s infrastructure. While we have implemented protections to defend users against this specific attack, we encourage continued caution as malicious actors frequently attempt to spoof trusted brands. We are taking additional steps to prevent further misuse.”
We’ve seen several phishing campaigns that abuse trusted workflows from companies like Google, PayPal, DocuSign, and other cloud-based service providers to lend credibility to phishing emails and redirect targets to their credential-harvesting websites.
Campaigns like these show that some responsibility for spotting phishing emails still rests with the recipient. Besides staying informed, here are some other tips you can follow to stay safe.
Pro tip: Malwarebytes Scam Guard can recognize emails like this as scams. You can upload suspicious text, emails, attachments and other files and ask for its opinion. It’s really very good at recognizing scams.
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Cybersecurity risks should never spread beyond a headline. If something looks dodgy to you, check if it’s a scam using Malwarebytes Scam Guard, a feature of our mobile protection products. Submit a screenshot, paste suspicious content, or share a text or phone number, and we’ll tell you if it’s a scam or legit. Download Malwarebytes Mobile Security for iOS or Android and try it today!
Phishing attacks, which trick users into revealing sensitive information or installing malware, have grown more sophisticated and localized. Putting it simple: the frauds you'll get in Portugal are completely different from US, Singapore or any other place.
As the largest players in the email space acknowledge the need for DMARC, the entire ecosystem will become restricted to the ones following the rules.
Hello and welcome. My name is John Strand and in this video, we’re going to be talking about some very basic Google searches. Now we’ve got to take a couple […]
The post Getting Started With Basic Google Searches appeared first on Black Hills Information Security, Inc..
Mike Felch// How to Purge Google and Start Over – Part 1 Brief Recap In part 1, we discussed a red team engagement that went south when the Google SOC […]
The post How to Purge Google and Start Over – Part 2 appeared first on Black Hills Information Security, Inc..
Mike Felch// A Tale of Blue Destroying Red Let me start by sharing a story about a fairly recent red team engagement against a highly-secured technical customer that didn’t end […]
The post How to Purge Google and Start Over – Part 1 appeared first on Black Hills Information Security, Inc..
