Some of you might notice something different about the title of this post.

You’re not imagining it: we’re bringing back the original name. What started in 2018 as the Ad Blocker Developer Summit later became the Ad-Filtering Dev Summit — but this year, we’re going back to our roots. Old-new name, same community.

And speaking of coming back: Ad Blocker Dev Summit 2026 is officially happening!

This year we're bringing it somewhere new: Copenhagen, Denmark, on November 3–4. ABDS 2026 is once again co-organized by AdGuard and eyeo, and it's free to attend.

Zoku Hotel, Copenhagen

Have something to share?

The call for speakers is open! If you’re working on something relevant to ad blocking or privacy protection, we want to hear from you.

Talk submission deadline: August 16, 2026.

After reviewing all submissions, we’ll get back to you in late August with decisions.

Just want to attend as a guest?

Join us in Copenhagen for two days of insightful talks and engaging conversations. Attending ABDS is free — just be sure to register in advance:
Register as a guest

What is the Ad Blocker Dev Summit?

If you’re new here: ABDS (formerly Ad-Filtering Dev Summit) is the annual gathering for everyone who works on making the web a less intrusive, more private place — developers, filter list maintainers, browser engineers, privacy researchers and advocates, and everyone in between. Last year’s edition in Limassol brought together speakers from AdGuard, Brave, eyeo, Ghostery, Google, Malwarebytes, Mozilla, Opera, and more.

Watch the trailer from 2025:

And here is the full playlist of talks from last year’s summit.

If you have any questions, please reach the org team at contact@adblockerdevsummit.com.

We hope to see you in Copenhagen this November ;)

Because that code lives in Chromium itself, the change will also affect Chromium-based browsers such as Edge, Opera, Brave, and Vivaldi. While browser vendors are technically free to continue supporting Manifest V2 on their own, doing so would require maintaining legacy code after it has been removed upstream. Google engineers have described the remaining MV2 infrastructure as increasingly costly to maintain and pointed to bugs and security issues associated with the old platform. As a result, browsers that want to keep supporting MV2 will have to invest their own engineering resources rather than relying on Chromium to do the work for them — making long-term MV2 support increasingly unlikely.

The news has reignited a familiar narrative: that Google is finally killing ad blockers. Similar claims have accompanied almost every major milestone in the Manifest V3 transition since it was first announced in 2019. The reality is that the most significant part of the Manifest V2 phaseout happened years ago. Major content blockers have adapted, and the web did not suddenly become unfilterable. What is happening now is largely the removal of compatibility mechanisms that outlived the transition itself.

So rather than add to the noise, let me walk you through an objective timeline of what has actually been happening.

The story

This whole story about "Chrome disabling ad blockers" begins back in 2019. Around that time, Google had finally staffed up the team responsible for browser extensions in Chrome, and they set out to tackle some longstanding problems. The biggest issue was that the Chrome Web Store had become flooded with malicious extensions — Google's moderation was frankly poor, and all sorts of junk kept slipping through. A second, related problem was the abundance of low-quality extensions that hurt browser performance.

Google's solution was to launch a new version of the extension platform, called Manifest V3 (MV3), designed to replace the older Manifest V2 platform. The catch was that the new platform removed or restricted a number of capabilities that extensions had previously relied on. Whether it actually solved the original problems remains debatable.

On the security front, nobody has ever convincingly explained how MV3 helps. On performance, though, the story is different: MV3 genuinely reduces the impact that poorly written extensions can have on the browser.

To soften the blow for content blockers, Google introduced a new set of APIs intended to compensate for some of the functionality being taken away — most notably the declarativeNetRequest API. But to be honest, had Google shipped MV3 in the form originally proposed in 2019, it really could have been the death of ad blockers — and many other extensions too.

The collaboration

What prevented that outcome was years of collaboration. That same year, Google came to the annual ad blocker dev conference to present the new platform and ask what they needed to do so that ad blockers could keep working normally — and they have returned to that conference every year since. In parallel, Google joined Mozilla and Apple to form the W3C WebExtensions Community Group, a standards body through which we, the extension developers, worked alongside all of them to improve MV3 and make it something that could satisfy all parties.

It was a long road, but through that collective effort MV3 was eventually brought into a workable state. Only five years after the first announcement did Google finally complete the transition to MV3 in Chrome, by which point many extensions — ad blockers included — migrated over to it. As for how well ad blockers perform today, I won't pretend the transition was painless: compared to the previous version, our lives got a little harder and the product became somewhat tougher to maintain. But end users are unlikely to notice much of a difference. Ad blockers are very much alive.

The current state

That brings us to what is happening right now. Even though Chrome itself moved to MV3 back in 2024, its codebase still retained the ability to run the old MV2 extensions. All of that legacy code was still present — and while Chrome no longer relied on it, the third-party browsers built on the Chromium engine (such as Opera, Edge, and Brave) did. Starting with version 150, that old code is being removed from Chromium, which means MV2 extensions will stop working in those third-party Chromium-based browsers. And realistically, the developers behind them are unlikely to have the resources to maintain MV2 on their own, since the code is complex and reaches deep into a large number of browser components.

The bottom line

Google isn't suddenly pulling the plug on anything today — all the important events already happened between 2019 and 2024. Ad blockers are fine. We were never thrilled about the move to MV3, but the predicted apocalypse never arrived. The real casualties of these changes aren't ad blockers, but the third-party browsers that had kept supporting old MV2 extensions up to now (and used that as a competitive advantage over Chrome).

And if you’re someone who relies on the full power of the webRequest API — the kind of deep, flexible filtering that MV3's declarative approach can’t fully replicate — don’t forget that there’s always Firefox. Mozilla continues to support the blocking capabilities of the webRequest API alongside MV3, which means the most demanding content blockers can keep doing everything they’ve always done. Of course, switching browsers isn't something everyone wants to do. Fortunately, browser extensions aren't the only way to block ads. Network-level and system-wide solutions such as AdGuard don't depend on Chrome's extension platform at all, making them unaffected by the MV2-to-MV3 transition.

Less noise, more match

Visiting a sports page should feel like getting a ticket to the match, not becoming a witness to a parade of autoplay videos and flashing banners. AdGuard Ad Blocker reduces visual clutter and blocks intrusive ads and trackers, making pages cleaner and less interruptive for fans following World Cup 2026 live stream links. That cleaner browsing layer helps when you are looking for ways to watch FIFA World Cup free and land on ad-heavy pages that break the viewing flow.

Get AdGuard

Keep the inbox for what matters

The road to kickoff brings subscriptions, confirmations, and promotional emails from broadcasters and services, and it is easy to lose track of the messages that actually matter in the noise. AdGuard Mail provides aliases and temporary email addresses so fans can sign up for match alerts, ticketing services, and streaming trials without exposing their primary inbox, which keeps communications around the World Cup 2026 schedule tidy and manageable. This small change spares readers the post‑tournament ritual of mass unsubscriptions and inbox clean-up.

Get AdGuard Mail

Protect the connection when you’re on the move

More fans than ever will be watching away from home — in airports, hotels, and cafés — and in those moments connection security matters. AdGuard VPN masks IP addresses and encrypts traffic so browsing and streaming become more private on public networks, a practical help for anyone who wants to watch world cup from anywhere while keeping their data safer. It’s not a workaround for broadcast rights, but it is a sensible tool for travel‑age viewing habits and a relevant companion for those searching for a VPN for the World Cup.

Get AdGuard VPN

A home solution for every device

Not all devices support browser extensions, and some living rooms are governed by smart TVs that can’t run regular blockers. AdGuard DNS lets households filter ads and trackers at the network level, protecting any connected device and giving families a smoother, ad‑reduced experience across phones, laptops, and TVs — a useful complement to ad blockers and a way to bring cleaner viewing into the shared spaces where many World Cup evenings happen.

Get AdGuard DNS

How it fits the way people watch

People follow the tournament in different ways: some tune in to the official World Cup 2026 broadcasters by country, others look for World Cup 2026 free stream options, and many simply want to check the live score without being interrupted. AdGuard doesn’t replace broadcasters; it improves the surrounding experience — removing distractions, limiting promotional noise, and securing connections when needed — which aligns with common search intents such as how to watch World Cup in the USA, how to watch World Cup in the UK, and how to unblock World Cup stream.

Practical setup tips for fans

• If you want a cleaner browser experience while hunting for a World Cup 2026 live stream, install an ad‑blocking extension on your desktop or mobile browser to reduce popups and overlays.
• Use an email alias or temporary address when signing up for streaming trials or ticketing alerts to keep your main inbox focused on essentials.
• When watching on public Wi‑Fi, consider enabling a VPN to protect your session and reduce the risk of interception.
• For shared household setups or devices without extension support, configure DNS filtering on your router to block ads and trackers network‑wide.

A note on access and rights

It’s important to be clear: these tools improve the viewing experience, but do not create legal access to paywalled broadcasts. Availability of streams and rights to show matches depend on regional broadcasters and platform agreements, so fans should check the official World Cup 2026 broadcasters by country and the World Cup 2026 schedule when planning what and where to watch.

A finishing note

Watching the World Cup is as much about presence as it is about access. By clearing the noise, tidying the inbox, and securing the connection when you’re away from home, AdGuard products help make that presence easier to maintain — so the focus stays on the moments that matter on the pitch.

Pre-2009: The Jurassic Period of ads

AdGuard was first created in 2009, and it was designed — you guessed it — to block ads. Specifically, it was designed to block ads that existed in 2009. By that time, the internet had already evolved past its first early stages of the 90s, but compared to what it looks like today, the web was still a very different beast. And the ads were very different too.

If you were around at that time, you know what we mean: it was the era of ‘fair’ ads, at least by modern standards. You type a URL into your browser’s address bar, you press Enter, the website loads. On those websites, there may be banners advertising this thing or that thing; if you click on them, they take you to the advertised website, end of story. What you see is what you get, with the occasional scam ad here and there, but probably not on big, popular sites.

Cartoon Network’s website in 2000s

Visually, these banners could be anything, from static pictures and ‘download’ buttons to animated GIFs and popups. The ads of that era were many things, but they were not subtle or sophisticated. And, as a reflection of them, the ad blockers of the time were pretty simple as well. By 2009, ‘hosts’ files were still a popular way to combat ads. HOSTS.TXT is basically a plain text file that maps domain names to IP addresses, and it overrules whatever the DNS server tells it. This method was popular because it worked well: list all the ‘bad’ domains associated with ads and route them all to a 0.0.0.0 ‘black hole,’ and you’ve successfully blocked a lion’s share of ad banners.

The ad blockers of that time were not too different in that regard. They were more powerful than hosts files, of course, as they could do more advanced things like some cosmetic filtering or distinguishing page context, but the practical difference was not hugely noticeable for an average user.

And this is what the ad landscape looked like by the time the first AdGuard version saw the light of day. Ad blocking was on the rise, but still a very niche thing for those ‘in the know.’ Andrey Meshkov, one of the founders of AdGuard, wrote it all by himself, and this is how our story began.

A screenshot of one of the oldest AdGuard versions

2009–2011: The Flashpocalypse

Not everyone may remember Flash now, but it used to be an insanely popular multimedia software platform, used to create animation, web applications, browser games, and many other things. And it’s not to say that there was no Flash before 2009, because Flash was already kind of old by that point, having existed for well over a decade. It had been extensively used to create animated ads, but 2009 marked an important shift. In terms of its role in the ad ecosystem, around this time, Flash started transitioning from “something used to display annoying banners and popups” to “a tool used to create tracking infrastructure and execute heavy third-party code.”

A ‘Flash player required’ message

Flash had something called Local Shared Objects, also commonly known as Flash cookies. The name isn’t a coincidence: they functioned very similarly to regular cookies. They could be used to store user preferences, save data from Flash games, or even track users’ Internet activity. Unlike cookies, they could store much more data, up to 100 kB, and could not be cleared just by deleting browser history. In 2009 a group of Berkeley researchers found that over half of the top 100 websites used Flash cookies, which would sometimes even restore the deleted HTTP cookies.

Earlier ad blocking was mostly about visual clutter: hide this huge banner, block that annoying popup. But this period in time is when the threat started to turn invisible, in no small part because of Flash with its hard-to-delete tracking. Blocking the visible graphics would not do anything to stop the underlying tracking layer. The problem was prominent enough that dedicated tools like NoScript and Flashblock became especially relevant: they replaced Flash content with placeholders and required a click to run it.

Ad blockers started to adapt too, with filtering rules aimed at blocking tracking scripts and features designed specifically to block or otherwise restrict Flash. As for AdGuard, even at the earliest stages of its life we were curating our own filters, and fighting tracking in general (and Flash tracking in particular) was part of it. A bit later we added a dedicated option to block Flash entirely, which you can still find inside Advanced Settings of AdGuard for Windows.

“Block Flash” option in AdGuard

Flash was not, of course, the only thing happening at that time. Behavioral advertising and real-time bidding already existed, for example, but we will get a better chance to talk about them in later chapters. Broadly speaking, this era was characterized by the growing role of tracking compared to pure advertising, and moving forward this distinction would become more and more obvious.

2011–2013: Social tracking and retargeting

Some of the clearest examples of the rise of tracking in that era were social widget tracking and, especially, retargeting. The concept of ad retargeting is familiar to anyone now, even if the word itself is not: you browse the web and come across an electric toothbrush. Maybe you even entertain the thought and visit the seller’s website, but ultimately decide against buying it. But the machine has already started moving: now anywhere you go, the toothbrush follows you around, across news sites, blogs, even on Facebook — and this could go on for days. This is the point in time when previously invisible tracking became apparent to regular users, and it was an unfamiliar, spooky feeling.

Retargeting proved to be wildly effective, and so ad blockers had to react quickly. And they did — old filters were modified with trackers in mind and new ones emerged, designed specifically to block tracking. AdGuard also created a new separate filter for spyware and trackers around that time — it has evolved into the Tracking Protection filter that we still maintain today. As a response to the demands of the time, this period also saw tools that were not classic ad blockers at all, but were instead focused solely on detecting and controlling JavaScript tags, trackers, cookie-based tracking, and such.

Another boogeyman of this era was a different type of tracking — social widget tracking. ‘Like’ and ‘Share’ buttons, floating share bars, counters, and so on — they were everywhere, and they were tracking you. These widgets were not just buttons. They were third-party embeds that could tell Facebook, Twitter, Google, and others which pages a user loaded, often regardless of whether the user clicked the widget or not.

Plethora of Facebook widgets from 2012

As for ad blockers’ response, you could simply block the tracking behind these widgets, like any other trackers, by using anti-tracking filtering rules and filter lists. But for many users the widgets themselves got so tiresome that some opted to remove them altogether. And for these people AdGuard offered Social media filter that removed social media integrations, which is also still available today.

2013–2016: The Programmatic web

Around that time, web advertising started being much less about manually placing ads on web pages and more about auctioning the ad space to the highest bidder to display the ‘perfect’ ad tailored to the specific user. User profiling became key and Real-Time Bidding (RTB), despite having first appeared much earlier, was scaling fast and became the defining factor for the adtech landscape of the time. It is still highly relevant, with an estimated total market value of USD 16.3 billion in 2024 and projected to reach USD 39.6 billion by 2030. So what exactly is RTB and how does it work?

RTB is a method of buying and selling ad space in real time to the advertiser with the highest bid. Imagine you visit a website, and the site owner (publisher) has an ad space on that website that they would like to sell, and so they initiate an auction. The publisher reaches out to the Ad Exchange — a place where the auction takes place — and sends over your profile. This profile may include your device ID, information about the device’s settings, its location, things like your age bracket, your likely interests — anything goes; the more, the better. The ad exchange then offers this information to advertisers who bid on how much they are willing to pay to show their ad to you. The highest bidder wins, and this is the ad you see on the website — more often than not, highly relevant and aimed specifically at you.

How real-time bidding works. Image credit: Adjust

In reality, it’s a fair bit more complicated than that: for example, both publishers and advertisers have their own ‘representatives,’ called Supply Side Platforms (SSPs) and Demand Side Platforms (DSPs), and they add a whole new layer to this scheme. But despite being so complex, this entire process is highly automated and takes mere fractions of a second from start to finish. And these auctions happen all the time. Just in Europe and the U.S., 178 trillion auctions take place every year.

RTB doesn’t operate in a vacuum — it feeds on, and in turn accelerates, the broader infrastructure of identity tracking built around it. One of the scariest things about all this is how easy it is to obtain information about any individual. Notice that in any RTB auction, in order for advertisers to be able to know how much they want to bid, the ad exchange shares with them all the information about you that it received from the publisher. If you’re an advertiser, you may receive valuable data even if you never win the auction. In 2017, the researchers from University of Washington conducted an experiment in which they were able to track the movements of a specific person by participating in RTB auctions — all they had to do was spend about $1000.

RTB went hand in hand with the expansion of analytics, tracking pixels, and identity infrastructure. The more an ad system could infer about a visitor — their interests, device, location, browsing history, or likelihood to convert — the more precisely that impression could be valued and bid on. This period was characterized by heavy cross-device and cross-channel measurement: Universal Analytics, the then-new version of Google Analytics, pushed it further toward a “multi-screen, multi-device” model, while Facebook’s tracking pixel, launched in 2015, turned ordinary websites into sources of conversion data, audience building, and retargeting. All in all, this was the period when today’s adtech landscape began to take shape — and in many ways, it still follows the same logic.

Unfortunately, it’s not easy to illustrate how ad blockers in general and AdGuard in particular reacted to the new challenges. Just as the threat itself was invisible to a user, the response was too. The advancement of ad blocking syntax, new filtering rules, new anti-tracking filters — unless you were a hardcore ad-blocking user who frequented specialized forums, you wouldn’t know much, if anything at all, about the constant struggle that was going on behind the scenes.

Stealth Mode module in one of the older AdGuard versions

A big step forward for AdGuard was the addition of Stealth Mode module in early 2016 — before it, the tracking protection filter was the only line of defense from tracking. Stealth Mode was designed to protect users’ privacy in various ways, including blocking third-party cookies, preventing WebRTC leaks, and many others. It is now known simply as the Tracking protection module and has accumulated dozens of new features over the last decade, some of which we will touch on later.

2016–2019: Fingerprinting fiesta

For years, websites relied on cookies to collect information on internet users. But by that time, browsers and ad blockers had become pretty good at limiting or blocking cookies, and so trackers looked for ways to recognize people without storing anything obvious on their devices. This is where fingerprinting enters the stage. The naming is not random: just like you can identify a person by the unique pattern of their finger ridges, it is possible to identify a user by a unique combination of factors: time zone, language, browser settings, installed plugins, fonts, screen size, device characteristics — the list can go on and on. None of these signs are good enough to definitively identify a person, but combine enough of them into a single user profile, and you can reach a very high degree of certainty.

The idea of collecting information about users’ browsers and devices first appeared a long time ago, but it took some time for it to become widespread. For example, in 2013, out of Alexa top 10,000 websites only 40 were using fingerprinting scripts. It wasn’t long until this tracking method skyrocketed in popularity, though — and it remains popular even today, as it wasn’t affected by the increased cookie restrictions.

There are different types of fingerprinting, and the poster child is definitely Canvas fingerprinting. The idea is surprisingly simple: a website asks your browser to draw something invisibly, then reads the subtle rendering differences caused by the OS, GPU, drivers, fonts, and browser. It was so effective that, according to a study by Steven Englehardt and Arvind Narayanan of Princeton University, by 2016 nearly all prominent trackers stopped using canvas fingerprinting because the public backlash got so strong; the overall number of domains employing it, however, had increased considerably compared to 2014 (but its use was already shifting from behavioral tracking to fraud and bot detection).

An example of an image generated for canvas fingerprinting

Fingerprinting is not just about making your browser draw invisible images and measuring the result. Canvas fingerprinting is only one example of a much broader technique: collecting small details about your browser, operating system, and device, then combining them into a profile that may be distinctive enough to recognize you later. We already mentioned a lot of parameters that can be measured, here are some more examples: your audio stack, screen resolution, touch support, battery status — these and countless other signals can all become pieces of the puzzle.

And as devices became more capable, the puzzle gained more pieces. Modern PCs, laptops, and especially smartphones exposed more sensors, APIs, rendering features, and hardware characteristics to websites. Each new capability was introduced for legitimate reasons — richer graphics, better media playback, responsive design, mobile-friendly interfaces — but many of them also gave fingerprinting scripts another field to fill in. Even the fact of using a specific ad blocker could become another line in your profile.

Speaking of, is it possible to fight fingerprinting with a trusty old ad blocker? Yes and no. When it comes to recognizable third-party fingerprinting scripts, it’s as easy as adding necessary rules to the filter list. But ad blockers struggle when fingerprinting is embedded into ordinary first-party code. It is possible to block script execution or limit JavaScript with an ad blocker — which would help against fingerprinting — but it could easily lead to site breakage, so it isn’t a solution for everyone.

A random User Agent enforced via AdGuard’s Tracking protection module

As for AdGuard, the aforementioned Stealth Mode/Tracking protection module offered (and still offers) a number of settings, like Custom User-Agent, that could mitigate fingerprinting in theory, but truth be told, this is one area where browsers are much more well-equipped for the fight, just by their nature. They could much more easily implement things like fingerprint randomization — this is a good illustration of why you should treat ad blockers as one of the tools in your privacy-protection suite, and not as an be-all and end-all solution for all problems.

2019–2022: Tracking hiding in plain sight

By this point, more and more users were becoming aware of the dangers of tracking. Having an ad blocker extension installed in your browser became the norm. More privacy-oriented apps and extensions were popping up, and even historically ‘privacy-neutral’ software like browsers shifted more and more toward adding privacy protection features. Trackers were facing serious pushback and had to adapt to the new reality. One of the ways of doing so was making third-party tracking look like first-party traffic by using techniques like CNAME Cloaking.

To understand how CNAME cloaking works, you need to know what first-party and third-party mean in the context of browsers. Normally, browsers treat subdomains of the same site as belonging to the same party. For example, if you visit www.company.example, a request to stats.company.example still looks like a first-party request. It can receive the site’s cookies and may even set cookies that belong to company.example.

A diagram of how CNAME cloaking works

CNAME cloaking (CNAME stands for canonical name) abuses this trust. A website owner can configure a subdomain, such as stats.company.example, so that behind the scenes it actually points to a third-party tracking service. To the browser (and to the ad blocker), the request still appears to go to the original website. But in reality, the tracker is hiding behind a first-party-looking subdomain, gaining many of the privileges that browsers normally reserve for the site itself.

CNAME cloaking wasn’t dangerous because it invented some new breakthrough way of data collection. It was dangerous because it blurred the line between the site you visit and the third parties attached to it, and this distinction was one of the core ideas that privacy tools like ad blockers relied on. For example, first-party cookies are often necessary for websites’ proper functioning, and messing with them can be dangerous. Even if you open AdGuard’s Tracking protection module, you will find “Not recommended” in bright red letters next to the Delete first-party cookies setting — nothing like that next to the same setting for third-party cookies.

Another prime example of tracking moving beyond traditional methods is server-side tracking. Instead of loading every tracker directly in the browser, a website collects data through its own first-party endpoint and passes it along behind the scenes. In fact, this endpoint can be used for something completely legitimate, like loading image or video assets, and so blocking it entirely would effectively render the website useless for the visitor.

By 2020, ad blockers and advertisers were firmly caught in a never-ending arms race. As you can see, these new, inventive ways of tracking users and showing ads were nothing like naive banners of the 2000s, and simply installing a browser extension wasn’t cutting it anymore. This is when DNS filtering moved to the forefront of the battle against trackers. It could see domain resolution, including CNAME chains, and it could protect devices like smart TVs and routers that were impossible to shield with regular ad blockers. AdGuard DNS was officially released in late 2018, and by 2020 it was integrated with all AdGuard apps.

AdGuard DNS dashboard

Advanced filter rules also played their part — they were not just blocking ad URLs, but identifying trackers hidden behind first-party-looking domains. But it became clearer than ever that filtering rules alone were not sufficient for all-round protection; your ad blocker had to cover all fronts. This was also the time when network-level blockers like AdGuard Home rose in popularity, as users became much more knowledgeable and technically competent and wanted full control over their network, rather than delegate it to someone else.

2022 onwards: AI arrives on the scene

AI took the internet by storm and quickly spread into every corner of the web, including advertising and tracking. However, AI didn’t suddenly allow advertisers to discover a new way to follow users. Instead, it gave them better tools to interpret, predict, generate, and optimize around the data they were already collecting.

Remember retargeting and how the same ads followed you around the web based on what you’ve already seen and what you clicked? AI elevated this to the next level with predictive targeting. Instead of showing you the same ads over and over based on what interests are listed in your profile, AI analyzes signals such as browsing history, clicks, purchases, and looks for patterns in that data. Then, based on these patterns, the AI predicts the likelihood of you responding to an ad and even tailors the ad personally for you. This includes not just how the ad looks, but also its timing and the specific offer. To put it briefly, the targeting now is not about what you’ve done, but about what you are likely to do next.

Another boon that AI gives to advertisers is that they don’t have to know that much about you to achieve the same (or even better) precision. Whatever they don’t know, they can now predict with a high degree of certainty. Trackers use cross-platform profiling to build a better picture of you, meaning they connect your activity across different apps and websites by using logins, device signals, ad identifiers, and browsing patterns to decide which signals belong to the same person. AI makes this method more powerful because it can link weak signals across different sources and infer hidden traits from patterns that may look unrelated at first glance. Now it’s much more valuable to have many weaker signals than very few strong ones, and so platforms with logged-in ecosystems have a natural advantage.

In that sense, AI didn’t change very much about how ad blockers deal with the ads and trackers themselves: filter lists remain the foundation for ad blocking, DNS and network filtering still matter, cosmetic filtering still matters. What became far more important than before is how much personal data you leak in the first place. Every website login, every permission request approved, every bit of information about yourself that you make available online now feeds the machine. Privacy tools can help here, too, but the onus is more and more on the users themselves to be vigilant and very conservative about how they interact with the web — although it is not an easy task at all in our hyper-digital age.

AI also opened up one potential ‘new frontier’ for ad blocking: ads being woven into chatbot conversations. As of now, almost all ads associated with chatbots are separate from the responses and labeled as sponsored, but this may become an issue in the future.

Screenshot of ChatGPT ‘Sponsored’ tag by Tibor Blaho on X

Functionally, they are not very different from any other ad you may encounter on a news website, for instance, so blocking them is a matter of promptly updating the filter list. But it is technically very possible to incorporate an ad into the very text of the chatbot’s response. Unless you already know the bot is trying to advertise something to you, there would be no way of knowing if some recommendation is genuine or was forced into the answer for advertising purposes only. Luckily, for now it seems that AI companies value their public image and fear the backlash enough to avoid this tactic, but it’s not out of the question that it will become reality at some point. You would think that this is where ad blockers will throw a white flag, but not necessarily. It will require a completely new approach, though — ironically, involving the use of AI as well. Ad blockers will have to move from understanding code to understanding meaning. Even as far back as three years ago we wrote about possible ways of dealing with this issue, so ad blocker developers keep their finger on the pulse: this may become a real threat sooner than you might think.

But AI is not a net negative for ad blocking. Far from it, in fact: the use of AI in content filtering was one of the main topics of last year’s Ad-Filtering Dev Summit. The devs discussed how AI can be recruited to automate blocking cookie popups, how it can help with quality assurance, and even how AI can assist in actual identification and blocking of the ads. Maxim Topciu, Browser Extensions Team Lead at AdGuard, created and showcased several working prototypes that used different methods to distinguish the ad content on the page and block it. It is absolutely clear that AI will remain one of the main points of discussion at the next Summit, and, to be honest, probably at every Summit going forward.

It is impossible to fit 17 years of web threats into one blog article, even if you stretch it a bit, and it wasn’t the goal anyway. The goal was to demonstrate how different the challenges were that the ad blockers had to face over time, how it wasn’t straightforward at all to bring you the best tools for protection against ads and trackers, but how we tried our very best at every point in time. We hope that you were happy with AdGuard no matter how long you’ve been using it, whether it’s just a year, or maybe five, or, who knows, all seventeen.

We can’t wait to see what the next seventeen years will bring! One thing will remain certain: we will continue doing whatever it takes to keep up with any threats of the future and to protect you.

LayerX researchers analyzed privacy policies of 6,666 extensions. Using a combination of AI classification and manual review, they identified at least 82 extensions whose policies explicitly allow user data to be sold, shared, licensed, or commercially transferred to third parties. Of them 75 were still listed in the Chrome Web Store at the time of the research which came out in April 2026.

Many extensions disclose the fact that they may sell or “share” users’ data in somewhat vague language hidden deep inside privacy policies. Some of the wordings include:

“We may sell or share your personal information with third parties.”

“This information may be sold to or shared with business partners.”

That little word “may” does a lot of heavy lifting.

Some extensions openly state that they collect browsing activity, behavioral profiles, streaming history, demographic information, and inferred interests for “analytics,” “marketing,” or “commercial purposes.”

Others go a different route. According to the researchers, the majority of extensions in the Chrome Web Store do not publish any privacy policy at all — which may be an even bigger red flag. According to LayerX’s earlier report, around 71% of Chrome extensions do not publish a privacy policy whatsoever. Under Google's Chrome Web Store policies, extensions that handle user data are required to publish a privacy policy. While some of these extensions may genuinely not process user data, most likely do since many popular extension categories inherently rely on access to browsing activity or webpage content to just work.

If an extension developer did not even bother publishing a privacy policy despite Chrome Web Store rules, there is little reason to expect they will suddenly become careful or transparent when it comes to handling your information. Realistically, such extensions are far more likely to collect, share, or monetize user data than the ones openly admitting they do it.

This all may sound abstract until you remember how valuable behavioral data has become. We have already covered how seemingly harmless commercial datasets increasingly fuel surveillance industries, profiling systems, and even law enforcement investigations through data brokers and location intelligence firms. What starts as “analytics” can eventually end up inside giant behavioral databases far removed from the original purpose users thought they signed up for.

Ad-blocking extensions that ‘sell’ your data

Among the most ironic findings in the report were ad blockers. Researchers identified multiple ad-blocking extensions whose privacy policies explicitly allow user data collection and sharing with third parties. Combined, these extensions reportedly reach more than 5.5 million users.

Some examples highlighted in the report include:

• Stands AdBlocker (3 million users)
• Poper Blocker (2 million users)
• All Block — ad blocker for YouTube (500,000 users)
• TwiBlocker (80,000 users)
• Urban AdBlocker (10,000 users)

According to LayerX, some of these extensions disclose collecting browsing activity, behavioral profiles, ad interaction data, and even inferred sensitive interests derived from visited URLs.

To be clear: these are not mainstream privacy-focused tools like AdGuard, uBlock Origin, or Ghostery. But they still have massive audiences numbering in the millions. And this is likely only the visible tip of the iceberg. In fact, the problem is far from new. More than five years ago, we identified dozens of fake ad blockers that amassed millions of installs while engaging in deceptive behavior, ranging from collecting user data to injecting ads and tracking scripts into web pages.

That is why transparency should be one of the first things users evaluate before installing any browser extension. AdGuard Browser Extension can serve as an example of what users should look for. In the AdGuard AdBlocker Chrome Web Store listing, it is clearly stated whether user data is collected, shared, or sold, alongside additional disclosures about how that data is handled. And for users who want to dig deeper, the full AdGuard Privacy Policy is publicly available and linked directly from the listing itself.

Netflix, streaming extensions, and the data economy behind entertainment

The report also uncovered a network of streaming-related browser extensions operating across Netflix, Hulu, Disney+, Prime Video, HBO Max, Apple TV+, and other major platforms. The extensions were all linked to a single publisher network operating under the “dogooodapp” brand and registered through HideApp LLC in Wyoming.

Some of the largest extensions included:

• Custom Profile Picture for Netflix (200K users)
• Hulu Ad Skipper (100K users)
• Netflix Picture in Picture (100K users)
• Ad Skipper for Prime Video (60K users)
• Netflix Extended (60K users)

According to the researchers, the associated privacy policies disclose collecting viewing history, content preferences, streaming behavior, subscription information, demographics, and engagement patterns. This data can later be shared or sold to advertisers, analytics firms, and media research companies.

And that is where the irony becomes hard to ignore. Many of these extensions exist around platforms that are themselves rapidly turning into advertising ecosystems. Netflix has been aggressively expanding ad-supported tiers while making premium ad-free plans more expensive. Amazon Prime Video automatically introduced ads for users who are not willing to pay to remove them. Across the industry, streaming platforms are increasingly betting on advertising growth and behavioral profiling rather than subscriptions alone.

And then there are the screens carrying all that entertainment: smart TVs, where most streaming happens anyway, and which have long since cemented themselves as some of the most aggressive players in the data-harvesting and ad-targeting economy. As we recently wrote on the blog while covering smart TVs inserting ads over HDMI inputs and gameplay, TV vendors are no longer just monetizing apps and home screens — they are steadily moving toward monetizing the viewing experience itself.

These extensions are effectively piggybacking on the exact same ecosystem — harvesting data around what people watch, click, skip, and engage with because that information has become far more valuable to advertisers and analytics companies than the hardware or subscriptions themselves.

Why this matters

It might be tempting to see browser extension tracking as harmless compared to malware or outright theft of your credentials like a password or a PIN. The thing is, modern data collection is just one piece of a much larger surveillance puzzle.

The data collected through seemingly harmless analytics can have real-world consequences. It can lead to higher insurance premiums, make it easier for companies to charge different people different prices for the same products, and expose users to increasingly aggressive ads and scams tailored to their interests. Browser extensions may not know your exact physical location the way mobile apps do, but they can still collect enormous amounts of behavioral data. This includes browsing history, search queries, shopping activity, streaming habits, clicked links, opened tabs, interests inferred from visited websites, and sometimes even the contents of pages you interact with.

On their own, these datasets may seem relatively mundane. But once enriched with information from advertisers, data brokers and public records, they can become surprisingly revealing — exposing financial situation, political affiliation, health concerns, the list goes on.

We recently wrote about how commercial location and advertising ecosystems increasingly enable this kind of large-scale profiling and surveillance, and how the modern ad-tech industry effectively operates as a real-time data broadcasting system where user information is constantly shared, traded, and analyzed behind the scenes.

And unlike a hacked password, behavioral profiles cannot simply be changed once exposed.

What to do before installing browser extensions

No extension is automatically trustworthy simply because it appears in an official browser store. Browser extensions also often require extremely broad permissions, including the ability to read and modify data on every page you visit. That does not automatically mean this kind of extension is about to steal your data, though. Some categories of extensions legitimately require broad access to function. Ad blockers, for example, need permission to read and modify webpage content in order to remove ads, block trackers, and filter malicious scripts before they load.

Before installing an extension, it’s worth running through a quick checklist:

• Read the privacy policy for red flags, especially phrases like:

  • “may share”
  • “business partners”
  • “analytics purposes”
  • “commercial purposes”
  • “affiliates and third parties”

• Be wary of extensions with no privacy policy at all

• Check who developed the extension

• Look at install counts, but remember they can be artificially inflated

• Read reviews critically: fake reviews are common and a large number of similar-sounding positive reviews should be considered a red flag

• Avoid installing unnecessary extensions entirely

• Go for well-established open-source privacy tools when possible

Interestingly, the report also highlighted a few extensions that openly compensate users for voluntary data sharing. At least there the arrangement is transparent: users knowingly trade data for money. The bigger problem is the far larger ecosystem quietly collecting and monetizing user behavior behind deliberately vague legal language most people never read. And ultimately, an ad blocker that profits from selling browsing data back into the advertising ecosystem is not really fighting ads. It is simply feeding a different part of the advertising system.

A more comfortable interface

We polished the interface, gave the app a fresh look with new colors, and improved the overall experience to make everything feel cleaner and more intuitive.

We also simplified navigation by removing swipe animations and replacing them with visible action buttons. Less hidden functionality, fewer accidental gestures, and quicker access to the actions you actually need.

Keeping AdGuard Mail up to date is now much easier

Starting with v1.5, the app can notify you whenever a new version is available. Once you see the update prompt, simply click it to begin the update process directly from the interface.

What if you’re already using the latest version? Then nothing changes — which means users updating to v1.5 won’t notice this feature just yet.

Temp mail takes the spotlight

We’re gradually shifting more focus toward temporary email addresses, and the app’s main screen now reflects that. From now on, the default screen opens with Temp mail instead of Aliases.

Aliases are still very much part of AdGuard Mail and, to make sure they remain easy to find, the new home screen includes a reminder that your aliases are still there whenever you need them.

Tell us what you think

Your feedback plays a huge role in improving AdGuard Mail, and we’d love to hear what you think about the new release. If you have suggestions, ideas, bug reports, or just want to share your experience, send us your feedback via the
AdGuard Mail form.

Upd. This promotion is over. If you didn’t get a chance to buy AdGuard Ad Blocker, AdGuard VPN or AdGuard DNS at a discount, don’t worry — we often run other promotions. Not to miss the next one, subscribe to our newsletter — we’ll keep you in the loop!

Long, long ago, when the Internet was a very different place — full of Flash animations and clumsy ad algorithms, with no Instagram and no mobile-first everything — AdGuard was born. Hard to believe, but that was 17 years ago!

AdGuard then and AdGuard now are worlds apart. But some things have stayed with us from the start: we believe in the cleaner Internet and better privacy for everyone. And yes, no AdGuard birthday is complete without discounts.

This time, we’ve prepared discounts for:

🎂 AdGuard Ad Blocker: 50% off 1-year licenses and 45% off lifetime ones
🎂 AdGuard VPN: 83% off 2-year subscriptions
🎂 AdGuard DNS: 60% off Personal and Team yearly plans

Mark AdGuard’s birthday by removing annoying ads, protecting your traffic from third parties, and blocking unwanted DNS requests. Extend or upgrade your current subscriptions and licenses — or get new ones for yourself or as a gift. Either way, we’ll be happy to have you celebrate with us.

To celebrate our 17th anniversary, we’ve put together the most honest collection of exactly those features. From the ones that are always front and center to those buried deep in settings. This includes our developers’ favorites and the features users most often rave about in social media comments and forums.

Get comfortable — we’re sharing our most beloved and useful picks.

Smart routes for your traffic

You’re the kind of person who takes their privacy seriously — VPN always on, no exceptions. Until some app decides it simply won’t cooperate with it, and on occasion some other one won’t work without it. So there you are, toggling VPN on and off like it’s your part-time job, slowly losing your mind.

These features will free you from that headache forever.

1. Exclusions in AdGuard VPN

When we asked the team which feature they love most, several people immediately named this one. In Exclusions, you can configure which apps and websites open directly and which work through the VPN. Traffic splits on its own, without your involvement — meaning no more finger gymnastics constantly toggling VPN on and off.

2. Saved locations in AdGuard VPN

AdGuard’s Head of Product admits that at first, Saved Locations seemed like just a nice bonus. There weren’t many servers, and who really jumps between countries every day? But gradually the use cases piled up: checking a banking site from abroad, watching football, paying for a subscription in the right currency. “At some point I caught myself always connecting to the same countries,” he says. “Now I open the tray menu and everything I need is right there in one click.” A small thing that saves real minutes.

3. Allowlist in AdGuard Ad Blocker

The Extensions team lead calls this feature simple but very useful. The Allowlist resolves a dilemma familiar to everyone: you like a website, you want to support its creators by seeing their ads, but turning off protection entirely is like going out in the rain without an umbrella. With the Allowlist you simply add the site to your blocker’s exceptions. One click — and filtering is disabled for that site, while the rest of the internet stays protected.

4. Inverted Allowlist in AdGuard Ad Blocker

Wait, haven’t we seen this one already? Not quite — here we’re talking about the inverted Allowlist. This feature was born from user feedback, since users play a decisive role in which features make it into AdGuard. It’s a mode where AdGuard takes a break on all sites except the ones you’ve added to the list yourself. Say you know a couple of particularly spam-heavy resources: you add them — and protection works precisely on those, leaving everything else untouched. A gem for those who don’t want total blocking but are tired of screaming banners in a few specific places.

Perfect order on the page

You open a favorite website — and there’s a banner taking up half the screen. Or an empty block left over because the blocker cut out the ad but forgot about the container itself. The screen looks like someone took a bite out of it. The next features bring perfect polish to your pages.

5. Custom filtering rules

For those who want to build their own protection system, there are custom filtering rules. Our CTO describes them philosophically: “The whole rules story is complicated, but very flexible.” Essentially, you take control of content into your own hands. You can create rules that will let some page elements load just fine while cutting others off before they even arrive. This is the choice of advanced users for whom standard filters aren’t enough, and who want to tune the internet down to the millimeter.

6. Block element on page

If hardcore scripts aren’t your thing, but you really want to remove that annoying banner and that other floating strip from your screen, a new hero steps in. Blocking elements on pages is one of the features in the AdGuard Assistant and a crowd favorite that gets praised frequently. You simply click Block ads on this website,” click on whatever is bothering you — and it disappears. Forever. Perfectionists call this their favorite feature, and we get it: the page becomes clean as an airbrushed magazine cover, and all without writing a single line of code.

7. Disable protection for 30 seconds

The favorite feature of our Editor-in-Chief. Sometimes a site breaks because of a poorly integrated script (and applying filtering to such elements may make things even worse), or you urgently need to see a page in its “original,” if spammy, form. Rather than fetching up the UI every time you want to turn the protection off and risk forgetting to turn it back on later, you simply pause protection for a short moment. Exactly thirty seconds later, AdGuard will silently return to its duties.

Full control under the hood (level: geek)

On milestone anniversaries like this, you think not only of the headline features but also of those that have quietly been working somewhere in the background all along, deep under the hood.

8. Userscripts

For geeks who always find the browser’s capabilities lacking, we have userscripts. AdGuard for Android and desktop can work as a full-featured script manager. This means you can expand website functionality directly through the blocker — add new buttons, redesign interfaces, and feel like a bit of a wizard. This topic periodically explodes in Reddit discussions, and for good reason.

9. Filtering log

One of the PR team lead’s favorites. Here’s how she describes her feelings: “You can watch in real time which trackers are knocking on your door and from where. You open a page and see the requests and blocks flying... All from one tiny little page!” The sight is mesmerizing and gives you a complete picture of what's happening.

10. Access to services on your terms with AdGuard Home

The Go team lead names one of his favorite scenarios: blocking services like YouTube, Reddit, and other social networks (including on a set schedule, only during certain hours). This helps you stay focused and eliminate distractions during work time.

11. Traffic control across all devices with AdGuard Home

Another favorite from the same colleague: the ability to monitor and control what’s actually happening on your devices — “to understand what [censored] is reaching out to what and block all of that stuff,” to give the exact quote.

12. TrustTunnel

The tool our CTO launches every day. TrustTunnel is our open-source client for custom connections. When a regular VPN isn’t enough — when you want not just to pick a server from a list but to build the entire route yourself — TrustTunnel gives you complete freedom. The CTO says: “I use it almost more than a regular VPN.” And these aren’t just empty words — for those who value independence online, this tool quickly becomes their primary one.

13. Connection log in TrustTunnel

Where does your traffic actually go? Connection log will tell you. The mobile development team lead explains: “This option is very helpful for analyzing where traffic went, so you can quickly add the necessary IPs to exceptions.” Essentially, you get an X-ray of your own connection.

Security, cleanliness, and quiet

These features work on the “set it and forget it” principle. Like superheroes fighting ads and tracking, bringing order to the internet while you sleep. The Extensions team lead calls this very philosophy the coolest part when talking about AdGuard Ad Blocker: “It works by itself, until one day you accidentally turn it off — and suddenly you see just how much advertising actually exists in the world.”

14. AdGuard Mail and Temp Mail integration

It’s not entirely fair to list a whole product as one of the top features, but we simply have to tell you about this one. While traffic flows along all the right routes, somewhere on the surface your personal inbox keeps filling up with spam. Every one-time site registration, every online store with a suspicious order form — and hello, a hundred unwanted emails.

For this we have AdGuard Mail with Temp Mail integration. Here’s how our content manager describes her experience: “I don’t even give my real address to anyone anymore. I get all the promo codes and newsletters on my temp address with a silly name (thanks to the devs for having a sense of humor!).”

You can also set up email relays to forward messages to your private address. This way, your main mailbox stays safe and practically gleams with cleanliness.

15. Tracking protection in AdGuard Ad Blocker

Privacy advocates particularly love this feature. The anti-tracking module doesn’t just remove ads — it strips UTM tags from links, hides your search queries, masks your IP address, and prevents sites from building your digital profile.

16. Phishing protection

The feature you completely forget about until it kicks in at the most unexpected moment. You click on an ordinary, unremarkable link, and in a fraction of a second AdGuard checks the site against its database. If the site is masquerading as a bank page or online store to steal your data, you’ll see a warning. Phishing protection is the feature you don’t think about — until it saves you from a fraudulent site.

17. Ad blocking inside apps and games

Mobile gamers feel the benefit of this feature most vividly. Anyone who has ever launched a free game knows: the ads don’t ask for permission. They burst in between levels, pop up over the interface, and steal precious seconds. In-app ad blocking is the feature for which our developers receive the warmest thanks from the gaming community.

A word from the author instead of a conclusion

As for me? I, as an SMM manager, value TrustTunnel and Exclusions in AdGuard VPN most of all. I spend a lot of time posting and replying on various social media platforms, and I wouldn’t want to get caught there with VPN protection off. But I also don’t want to toggle the protection every time I need to check my banking app or if I feel like watching an episode of my favorite show on a streaming platform, so Exclusions are a lifesaver. And the speed of TrustTunnel — have you seen it? Everything flies so fast that it’s easy to get addicted!

Of course, there are other favorites — Custom filters in AdGuard Ad Blocker, for instance. A while back I did a series of posts where I showed subscribers how to create their own rules and implement them in the app without deep coding knowledge. Honestly, I really struggled with it at the time — but that’s exactly why I came to love this feature. It taught me so much!

Thank you for staying with us all these 17 years. The internet keeps changing (and not always for the better), but we promise to keep clearing away the digital noise for you. Happy birthday, AdGuard!

As frustrating as it sounds, this is increasingly becoming the reality of modern high-end smart TVs.

What happened

That exact scenario recently played out for the owner of an LG OLED TV who shared a screenshot on Reddit showing a promotional banner appearing in the bottom-left corner of the screen during a PlayStation 5 startup sequence. The ad, promoting “pizza and movie-night favorites,” appeared directly over the console feed delivered through HDMI rather than within LG’s own smart TV interface. According to the user, the banner only started appearing after a recent firmware update.

The incident also does not appear to be isolated. Around the same time, another LG OLED owner posted on Reddit complaining about “pop up Instacart ordering ads” appearing on their $1,500 TV while using a PS5. The user explained that they had already gone through the settings and disabled advertising features when they first bought the TV, only for the ads to seemingly return later anyway.

“I had to dig in settings to turn it off, at least I thought I did, but I turned my TV on, booted up my PS5 and another popped up. Buy a flagship TV and I get fed ads. Unreal. Not buying LG again, and if every other manufacturer is doing this then I just won't buy a TV. I'm heated.”

Another commenter in the same thread described a nearly identical experience, saying they had disabled all ads as soon as they purchased the TV, only to later encounter another promotional overlay while using their console:

“Yesterday I turned it on along with my PS5, and had some sort of ad pop up on the bottom of the screen… Pissed me off immensely.”

Why it feels especially unfair

What makes this different from the usual smart TV clutter is where the ad appears. This is not an ad inside Netflix, YouTube, or LG’s home screen. It is being overlaid on top of content coming from an external device the user already paid for.

We have already been conditioned to tolerate ads and recommendations inside streaming apps and smart TV menus, but advertising layered over a console input arguably feels significantly more invasive. At that point you are no longer interacting with LG’s platform or a streaming service, you are simply using your PlayStation. That is precisely what makes the whole thing feel so intrusive, and a little depressing.

Another particularly frustrating aspect is that these changes apparently arrived through a firmware update, meaning owners did not originally buy the TV in this state. They purchased one product, and over time software updates quietly transformed it — or rather degraded it — into something else entirely. A premium TV is no longer behaving like a static product that fully belongs to the person who bought it, but increasingly like a remotely controlled advertising platform whose behavior, features, and level of intrusiveness can be altered at the manufacturer’s discretion long after the purchase has already been made.

Crossing the line

The problem is not simply that ads exist on smart TVs. That battle was largely lost years ago. Home screens filled with sponsored content, autoplay trailers, recommendations, and streaming promotions have already become a staple across most major TV platforms. The problem is that manufacturers are no longer limiting advertising to their own software ecosystems.

Displaying ads over HDMI inputs fundamentally changes the relationship between the user and the device. If someone is using a PlayStation, Xbox, Apple TV, Blu-ray player, or PC, the TV is supposed to function as a display, not as an active advertising layer sitting between the user and their own hardware. At least that is what still intuitively feels right, even if the boundaries of what is considered “acceptable” on smart TVs have been steadily eroding for years.

But in many ways, we saw it coming. A few years back, we wrote about Roku filing a patent describing technology that would display ads over devices connected through HDMI inputs, including game consoles, streaming boxes, and media players. The system outlined in the patent would essentially allow the TV to detect pauses or certain moments during external content playback and temporarily inject advertisements directly onto the screen, even if the user was not interacting with Roku’s own software ecosystem at all. At the time, many people dismissed it as just another speculative patent filing, but it reflected a much broader direction the smart TV industry has been moving toward for years: treating every possible surface, including HDMI inputs traditionally considered “safe” from platform interference, as monetizable advertising space. What is now happening on LG TVs shows that this future is no longer hypothetical.

LG being one of the pioneers of turning TVs into ad platforms

This did not happen overnight. LG has been steadily expanding advertising and viewer monetization across its smart TV ecosystem for years.

Back in 2021, it was reported that LG OLED TVs had started autoplaying video ads with sound inside the company’s app store while users were simply updating applications. At the time, the experience was dubbed as surprisingly aggressive even by smart TV standards.

In 2024, reports emerged that some LG TVs had started displaying ads during idle screensaver mode. More recently, LG went even further by integrating AI-driven advertising technology capable of analyzing viewer behavior and emotional engagement in order to personalize ads. The idea that a television is not only tracking what people watch, but also attempting to infer how they emotionally respond to content in order to serve more effective advertising, pushes smart TVs into territory that feels less like consumer electronics and more like invasive surveillance infrastructure embedded directly into the living room.

LG’s forays into the ad industry are indicative of a much larger industry shift that has been happening over the past decade. Television manufacturers increasingly stopped treating smart TV software as a secondary feature and started treating it as a long-term advertising business. Roku became one of the clearest examples of this transition, openly positioning itself as an advertising company built around television engagement data. Samsung, Vizio, Amazon, Google TV, and LG all followed similar paths.

Regardless of what these companies say, the real long-term value for them increasingly comes from collecting behavioral data, serving ads, tracking engagement, and monetizing viewers long after the TV leaves the store. Some manufacturers are no longer even trying to hide this shift. In 2023, a company named Telly began offering consumers “free” 55-inch 4K TVs built entirely around constant advertising and data collection, with a second built-in display permanently dedicated to news tickers, sponsored content, and ads. At least in that case the tradeoff is explicit from the beginning. At the same time, you might still subconsciously expect premium products to remain somewhat exempt from these practices. While aggressive monetization on cheap budget hardware may seem fair, hardly anyone expects a flagship OLED TV costing well over $1,000 to behave like a digital billboard that increasingly turns into an ad vending machine after purchase.

What powers smart TV ads

The reason smart TVs can do this at all comes down to the amount of data they collect about users and their viewing habits. Modern smart TVs rely heavily on a technology called Automatic Content Recognition, or ACR. This system allows televisions to identify and analyze content playing on the screen regardless of where it comes from: streaming apps, cable boxes, live TV, media players, and even devices connected through HDMI.

According to LG’s own privacy policy, the company may collect:

• Information about viewed channels and programs
• Streaming services and apps used
• Viewing duration
• Playback actions such as play, pause, stop, and clicks
• Input methods, including HDMI devices
• Information related to gaming consoles and media players
• Ad exposure data
• Subscription activations and cancellations
• Voice command interactions
• Device identifiers and behavioral analytics

LG explicitly states that its ACR technology can identify content “regardless of the source,” including gaming consoles, set-top boxes, and external media devices connected through HDMI.

Its advertising business, LG Ad Solutions, openly markets this capability to advertisers. The company promotes targeting based on gaming behavior, app usage, viewing habits, streaming preferences, subscription activity, and even exposure to specific ads.

Among the targeting categories promoted by LG are:

• Gamers using specific consoles and gaming platforms
• Viewers of particular streaming services and genres
• Users exposed to competitor advertisements
• Heavy or light TV viewers
• Subscription activations and cancellations
• Regional and location-based targeting

LG describes this information as “deterministic viewership data” collected “at the glass level.”

All in all, this incessant collection of granular data is what makes modern smart TVs fundamentally different from older televisions. They are not passive displays anymore. They are connected analytics platforms constantly collecting behavioral data to optimize advertising, recommendations, and viewer targeting.

How to get rid of smart TV ads

The most effective way to limit smart TV ads, tracking, and unwanted promotions is to disconnect the TV from the internet entirely and use it purely as a display paired with external devices like Apple TV, Chromecast, gaming consoles, or streaming boxes. Without internet access, the TV loses much of its ability to download new advertising modules, fetch promotional content, transmit analytics, and silently introduce additional features through firmware updates. Of course, this is also a massive tradeoff and in many ways defeats the entire purpose of buying a “smart” TV in the first place.

If you want to keep your TVs connected, you can still try disabling some of LG’s advertising and recommendation features manually. On LG TVs, this can usually be done by opening the Settings menu, navigating to General > System > Additional Settings > Home Settings, and turning off options like “Home Promotion” and “Content Recommendations.” The problem is that firmware updates have repeatedly been accused of re-enabling promotional systems or quietly introducing new advertising behavior later on. It’s worth noting that even with ACR disabled, your smart TV will still be able to collect some data about you, potentially including information about your location and the apps that you use.

Another option is blocking ads and trackers at the network level by changing the TV’s DNS server. Since LG TVs run WebOS, users cannot simply install traditional ad-blocking apps or browser extensions directly onto the device, which makes DNS filtering one of the few practical ways to limit ads on TV. DNS filtering works by stopping the TV from connecting to known advertising, analytics, telemetry, and tracking domains in the first place.

However, it is not a perfect fix. Some ads and promotional elements may already be built directly into the TV’s software and can still appear even without contacting external servers. DNS filtering also cannot block ads delivered through the same domains as legitimate TV services or apps, since blocking those domains entirely would also break the services themselves.

Services like AdGuard DNS let users apply this kind of filtering simply by changing the TV’s DNS server. Users who want more control can also use Private AdGuard DNS or tools like AdGuard Home or Pi-hole to set up their own private DNS server with customizable filtering, analytics, blocklists, and rules tailored specifically for smart TVs and other connected devices.

AdGuard Mini has always worked quietly in the background — and that’s exactly how it should be. But in v2.2, we’re giving you a way to check in on all that invisible work. Meet the new statistics stories.

New statistics stories

AdGuard Mini now includes two statistics stories in the menu bar popup — one for blocked ads, one for trackers. Check the exact count since you first launched the app.

AdGuard Mini now shows the number of blocked elements directly on the icon in Safari. Click the icon to see a full breakdown of blocked ads and trackers for the current page.

The count resets each time you open a new page. If you’d prefer to hide it, click the AdGuard Mini icon → gear icon → Settings and toggle off Indicate the number of blocked ads on the AdGuard Mini icon in Safari.

Share your feedback

Have a suggestion or spotted a bug? Let us know on GitHub or through our social media channels. Your feedback helps us make AdGuard Mini better with every update.

Filtering: Faster, stronger, better

Custom filters in MV3 can now update independently from extension updates again, so ad blocking stays accurate and responds to changes more quickly.

We’ve also fixed several issues where filters could be added but wouldn’t work properly. For example, the extension now validates sources when you add a custom filter by URL — this prevents situations where a filter appears in your list but doesn’t actually apply.

Beyond filter improvements, we’ve refined Allowlist: if you add a full page URL to your exclusions (say, copied straight from the address bar), the extension will automatically extract the domain, and the exclusion will work as expected.

Settings import: Show, don’t tell

You can now share your extension settings. This makes it easy to set up AdGuard on a new device or after reinstalling your browser. You can also help a friend configure their extension, or share your setup with the support team — they'll see your exact settings instantly. Just go to General and click Share settings.

If you’re an active GitHub user, you can now import settings directly from Issues — especially useful for filter developers and those helping test fixes.

Interface: Better prompted than puzzled

We’ve added a few small improvements to prevent confusion and make the extension behave more predictably.

• Warning for Opera users. Opera doesn’t grant extensions access to search engine pages by default, which prevents AdGuard from blocking ads in search results. The extension popup now shows a notice prompting you to allow this access. Click Go to Settings in the popup — you’ll be redirected to the extension settings in Opera, where you can turn on the Allow access to search page results toggle.

• Confirmation for Invert Allowlist. The Invert Allowlist feature reverses how the extension works: blocking is disabled everywhere except sites on the list. To prevent accidental activation, we’ve added a confirmation dialog that explains what will happen when you enable it.

More sharing: This time, your thoughts

Enjoying the update? Have ideas for what’s next? Share your feedback on our social channels or start a conversation on GitHub. Every comment helps us improve.

Although Location History is technically off by default, Google repeatedly prompts users to turn it on across Android setup screens and apps like Maps, Photos, and Assistant. Once enabled, it keeps collecting location data in the background, even when you are not actively using Google services. Over time, it builds an extremely detailed timeline of your movements, routines, and habits.

That timeline can reveal far more than many people realize: where you sleep, where you work, which clinics you visit, which bars you frequent, when you attend religious services, therapy appointments, or someone else’s apartment at 11 p.m.

Most users would probably consider that information deeply private. The US government, however, is now arguing otherwise. And that argument sits at the center of a major Supreme Court case that could reshape digital privacy in America

The case that can change how location data is seen

The case revolves around Okello Chatrie, who was seen on surveillance footage speaking on his cellphone while robbing the Call Federal Credit Union in Midlothian, Virginia, on May 20, 2019. According to investigators, Chatrie entered the bank armed, threatened employees, and escaped with roughly $195,000 in cash.

Police had few leads, but they noticed him talking on the phone during the robbery. That detail led investigators to request a geofence warrant from Google. A geofence warrant is a type of warrant that forces the company to hand over location data for every device detected within a certain area during a certain timeframe. In this case, authorities requested data for all devices within roughly 150 meters of the bank during the robbery window. Privacy advocates supporting Chatrie later compared the search area to several football fields laid side by side — large enough to sweep in nearby homes, businesses, and even a church, not just the bank itself.

Google then searched through its Location History database and returned anonymized data tied to devices that had been inside the area. Investigators initially received information linked to 19 devices. From there, without obtaining additional warrants, police requested additional location history for selected devices over a longer time window to study their movements before and after the robbery. Eventually, authorities asked Google to fully de-anonymize three accounts.

One of them belonged to Okello Chatrie. Investigators later searched his home and reportedly found around $173,000 in cash, along with firearms and clothing connected to the robbery. The location data ultimately became one of the key pieces of evidence used in the case against him.

As of 2026, the case — Chatrie v. United States — is being debated at the US Supreme Court, which will decide whether these kinds of geofence warrants violate the Fourth Amendment’s protections against unreasonable searches.

Private or not?

The US government’s position is essentially this: users voluntarily enabled Location History, voluntarily shared that data with Google, and therefore cannot expect it to stay private. Prosecutors also argue that location data reflects movements people made in public spaces anyway, so collecting those records is not the same as rummaging through someone’s house or personal diary. Privacy advocates and Chatrie’s legal team strongly disagree with that framing.

For starters, while Location History is technically optional, Google has spent years aggressively nudging users to enable it. During Android setup, inside Google Maps, Photos, Assistant, and other apps, users are repeatedly encouraged to turn it on in order to “improve” their experience or unlock certain features. Once enabled, the setting quietly expands across devices and services, continuously collecting location data in the background. Turning it back off is possible, but Google hardly makes that process obvious. Internal company messages cited in court filings even described parts of the interface as feeling designed to discourage people from figuring out how to fully disable tracking.

And then there is the bigger issue: just because something technically happens “in public” does not mean people expect the government and less so a private company like Google to build searchable historical records of it.

You may walk into a pharmacy in public. You may visit a therapist’s office, a casino, or someone else’s apartment building in public. That does not mean most people expect every one of those visits to be logged, stored for years, and later searchable by police through a giant corporate database.

For its part, Chatrie’s legal team argues that Location History is far more revealing than ordinary business records that the government compares it to. Over time, it can expose routines, relationships, political activities, medical concerns, religious beliefs, and countless other deeply personal details. And despite Google initially providing anonymized device IDs, privacy advocates argue that location data is notoriously easy to re-identify. A few location points are often enough to determine where someone lives, where they work, and ultimately who they are.

That concern is not theoretical. Court filings in the case note that Google itself has the ability to de-anonymize users internally. Researchers and privacy experts have also repeatedly demonstrated how supposedly anonymous location datasets can be linked back to real individuals using publicly available information.

In other words, the government is effectively arguing that one of the most sensitive forms of personal data people generate today should receive weaker constitutional protections simply because it happens to sit on Google’s servers instead of inside a filing cabinet at home.

Why it raises privacy concerns

Now let’s zoom out a bit and look at why geofence warrants worry privacy advocates far beyond this one robbery case.

The Fourth Amendment was written specifically to protect people against broad, suspicionless government searches. It states that warrants must be based on probable cause and must particularly describe the place to be searched, and the persons or things to be seized. In simple terms, the government is supposed to know who or what it is looking for before it starts digging through private information.

Traditionally, investigators identified a suspect first and then sought permission to search that person’s property or records. Geofence warrants turned that logic entirely upside down. Police now first collect data on everyone present within a digital perimeter and only afterward narrow down potential suspects. In practice, these warrants quietly pull innocent people into investigations simply because their devices happened to be nearby. Residents, employees, customers, commuters, delivery workers, and passersby can all end up inside a law enforcement dragnet without ever knowing it.

And while authorities often describe the process as anonymous, location data is rarely anonymous in any meaningful sense. Movement patterns are deeply personal by nature. A few location points can often expose where someone lives, where they work, who they spend time with, and what places they regularly visit.

We already explored how revealing mobile location data can become in our article on Webloc and the hidden market for location intelligence. The same kinds of datasets collected for advertising, analytics, and app features have quietly fueled an entire industry built around tracking people’s movements, profiling behavior, and selling location intelligence to private companies and government agencies alike. Geofence warrants effectively tap into that same ecosystem. If you want a deeper look at how valuable and invasive location data has become, that story is well worth reading.

Treating this kind of information as fair game simply because it was uploaded to a cloud service risks normalizing a surveillance model where authorities can retrospectively map the movements of entire groups of people whenever they choose. And once systems like that exist, history suggests they rarely remain limited for long.

What begins as a tool for investigating serious crimes can gradually expand into broader forms of monitoring, especially once governments grow accustomed to having access to massive pools of behavioral data collected by private companies.

Google moved location history on device, but problem is still here

Partially in response to the growing backlash around geofence warrants and mass location tracking, in December 2023, the company said it would begin moving Location History data from the cloud directly onto users’ devices, with the transition rolling out throughout 2024. By July 2025, large-scale geofence searches against Google’s centralized Location History database were effectively no longer possible in the same form, simply because Google no longer stored everyone’s movement history together on its own servers.

That was undeniably a good thing for privacy. But the bigger problem did not magically disappear together with Google’s old cloud-based database.

As the Electronic Frontier Foundation (EFF), the ACLU, and other privacy groups warned in their Supreme Court filing supporting Chatrie, this case was never really just about Google. It is about the broader idea that companies can quietly accumulate enormous amounts of behavioral data on millions of people and that governments may later treat those databases as fair game for investigations.

Google is hardly the only company collecting location data. Countless apps, data brokers, advertising firms, telecom providers, and analytics companies still gather and monetize extremely detailed information about where people go and what they do. Entire industries now exist around buying, selling, analyzing, and sharing location intelligence.

That is exactly why this case matters so much even after Google changed its systems, and this is where the case stops being just about one robbery and starts becoming a much bigger fight over what “private” even means in the digital age.

What you can do

The uncomfortable reality is that modern smartphones are tracking machines by design. There is no magic switch that gives you every convenience feature without any privacy tradeoff.

That said, reducing how much location data gets collected in the first place still matters a lot.

If you do not actively use Google Maps Timeline or similar features, consider turning off Location History entirely and deleting old location records from your Google account. It is also worth reviewing which apps actually need constant access to your location and switching unnecessary permissions to “While Using the App” — or removing them altogether. In most cases, there is little reason to keep precise geolocation enabled all the time if you are not actively using navigation, maps, or location-based features at that moment. And more broadly, it is worth remembering that convenience features often quietly outlive the reasons you originally enabled them for.

The Supreme Court’s decision, expected later this summer, could end up affecting far more than just geofence warrants. The case may help decide how much privacy people actually have over sensitive digital data stored by companies like Google, and how easily governments can access it.

How AI helps Google detect ‘bad ads’

Google’s main claim in favor of an AI-based approach when evaluating an ad’s legitimacy is that it doesn’t base the enforcement decision purely on keywords, but rather can understand and analyze more complex signals like account age, behavioral cues, and campaign patterns. Bad actors often design their scam ads to mimic legitimate ones, and they take advantage of generative AI to quickly mass-produce different variants, so that some would eventually trick the old pattern matching-based enforcement systems.

Before AI, these older systems looked more like a checklist, checking whether an ad contains certain words, symbols, URL mismatches, formatting tricks, or policy-triggering product categories. Does the ad use banned wording? Does the landing page match the display URL? Does it contain suspicious formatting like F₹€€!? These checks are useful, but they are also fragile and much easier to circumvent by inventive word choices and other clever ploys. For example, something like ‘Lose 20 pounds in a week!’ would be rather easy to detect and flag even under the old system. But imagine a landing page full of false claims, fake testimonials, and hidden subscription terms — and it becomes much harder. No single element indicates a scam, so the checklist approach has a high chance of approving the ad. But an AI system that understands context has a better chance of marking the ad as ‘bad’ with a higher degree of certainty. A good analogy would be airport security marking an individual as suspicious not just based on the illegal items they have in their baggage (the old system), but instead because of their weird behavior, like using different names, only buying one-way tickets, or changing routes frequently.

Gemini takes all this context into account to determine the intent behind the ad and is (at least according to Google itself) very good at identifying scams — over 600 million ads associated with scams were removed and 4 million accounts were suspended for scam-related activity in 2025. Another thing going for Gemini is the ability to automatically process user feedback. According to Google, its teams were able to take action on four times as many user reports as in 2024 as a result of AI integration.

AI shift is happening in ad blocking

The clash between the old and the new approaches to detecting bad ads in Google’s ad ecosystem is not without similarities to ad blocking in general. Many years ago, blocking an ad was as simple as matching the server used to deliver the ad to a set list of ‘bad’ domains. Anything coming from adserver.example.com would get blocked, and that’s that. DNS filtering still works more or less in the same way: it is less flexible, but very efficient, lightweight, and system-wide. Today, ad blockers face entirely different, much harder challenges. Ads and other unwanted requests often blend in with the useful content. Modern filtering rules are nothing like the short, simple rules from the early days of ad blocking. They are extremely complex, and filtering syntax resembles a literal programming language more than anything else.

Ad blocking syntax has been constantly evolving to keep up with ever steeper challenges — so far, rather successfully. But the fact that the traditional, filtering rules-based approach hasn’t been replaced by AI so far doesn’t mean that ad blocker developers have dismissed the thought of using AI in ad blocking. On the contrary, they have been exploring AI’s potential in the context of blocking ads, and often in quite unexpected ways. Attempts to use various forms of machine learning (ML) for ad blocking go as far back as at least 2019, when Brave developed AdGraph, a tool that blocked ads and trackers in real time. It showed surprisingly high accuracy, but required deep browser integration and constant maintenance, so it didn’t take off in popularity. There were a few other experiments and research projects that tried to take advantage of ML, but none managed to achieve widespread adoption.

In recent years, with the rapid advancement of AI technologies, the idea of using AI for ad filtering has come up increasingly often. For instance, it was one of the main points of discussion at the last year’s Ad Filtering Dev Summit. At AFDS 2025, several speakers touched on the role of AI in the ad-blocking landscape in their presentations — Ritik Roongta from NYU spoke about how AI can help evaluate ad content, especially for allow-listed ads that may be non-intrusive but still harmful, and Anton Lazarev from Brave explained why ad blockers will stay highly relevant even in the era of AI agents and agentic browsers.

AdGuard’s experiment: Can an LLM spot an ad?

AdGuard has been exploring the same direction. Maxim Topciu, AdGuard’s Web Extensions division Team Lead, has conducted his own research to answer the question: can a blocker understand what appears on the page and decide whether it should be hidden? As we already mentioned, filter lists remain powerful but have limitations: they require manual maintenance, struggle with native advertising, and face additional constraints, like the ones introduced under Manifest V3. Wouldn’t it be great if an ad blocker could determine what is an ad and what is not all by itself? The idea itself wasn’t new, as is evident from the past attempts by Brave and others to achieve similar results, but Maxim went a bit further. One of the advantages of LLMs is that they can make it relatively quick to turn an idea into a working prototype. So Maxim created not one, but three of such prototypes, each analyzing and blocking ads in its own way.

Maxim tested the prototypes on X’s feed. One blurred all the posts, analyzed their content, and then unblurred the ‘good’ ones. The second prototype did the same, but analyzed each post as an image, not as a block of code. The third one allowed the user to set certain criteria, and the LLM would check if the post matched them before deciding whether to hide the post or not. All three approaches worked, but all had their own drawbacks — after all, they were prototypes and very far from being end products.

The experiment showed that AI-based ad blocking is technically possible, but at the same time it became apparent that AI is not yet ready to replace the traditional filter-based approach.

Google’s use of Gemini to identify ‘bad’ ads and AdGuard’s own experiment, despite all their differences and despite serving different purposes, are pointing in the same direction: ad filtering is becoming more semantic. AdGuard’s experiment showed that LLMs can classify content by meaning, not only by selectors or URLs. A vision-based approach can analyze what users actually see, which helps when text is minimal or HTML is obfuscated. The crux of the decision when blocking an ad gradually shifts from “Does this web element match a rule?” to “What is it trying to do? What was the intent behind it?” If you could reliably detect every ad, sponsored post, tracker, and scam by determining their intents, there would be no need for filtering rules. But, evidently, we are not there yet. LLM-based approaches are still largely limited by cost, speed, and practicality. It appears that, while the role of AI in ad blocking is going to grow, it is not going to realistically replace traditional ad blockers in the near future, but rather complement them where filtering rules alone struggle.

Platform safety is not the same as user control

But this is also where the comparison between Google and independent ad blockers ends. The fundamental difference between Google’s use of Gemini and ad blockers’ use of AI lies in their goals. Google uses AI to enforce its own ad policies, while ad blockers exist to enforce the user’s preferences. Currently, users set these preferences by selecting the desired filter lists or by adding custom filtering rules. But AdGuard’s experiment showed that it is entirely within the realm of possibility to introduce user-controlled criteria to a future AI-based ad blocker, too. This is different from Google’s algorithms that do, indeed, block or restrict malicious and dangerous ads — which deserves praise — but doing so also lines up with Google’s own interests. Users don’t have any say in what exactly gets blocked and what comes through. An ad doesn’t have to violate Google’s guidelines to be unwanted. There are plenty of reasons why someone wouldn’t want to see an ad: it may be distracting, privacy-invasive, heavy, or simply irrelevant to the viewer. This is where the roots of the conflict lie: Google’s only concern is whether an ad is allowed inside its ecosystem and follows its rules. From the user’s point of view, the question is broader: do I want this ad on my device?

The anti-scam work that Google does is necessary, but also expected: it is its direct responsibility. The Ads Safety Report should not be read as a final answer to the problem of bad ads. Blocking billions of ads is cool, but even more ads remain. These numbers really put into perspective just how much harmful or questionable material flows through the online ad ecosystem. And this is where the true reason behind Google’s efforts lies. Google is an ad company first and foremost. Its business model is not based on selling Android phones or anything like that — it is centered around the ad ecosystem it has built, and most of its other, numerous branches support it in one way or another. Google has shown time and again that protecting its advertising business weighs heavily in its product decisions. Its safety work is no exception: it is also a necessary concession to keep users within the Google ad ecosystem.

We are not trying to say that Google’s anti-scam efforts are meaningless — of course, it’s better to have no, or close to no, fraudulent and dangerous ads on your phone. It’s even better when you, the user, are the one who controls what else you want or don’t want there. Google’s Ads Safety Report demonstrated how efficient AI can be at identifying unwanted content. Now it’s ad blockers’ turn to find an even better use for this powerful weapon and make it serve a good cause.

This is exactly what happened to Khasif Hoda who unknowingly became a star of the viral experiment, in which he, along with many others, was recorded and identified in real time with the help of Ray-Ban Meta smart glasses. The man wearing the glasses was AnhPhu Nguyen, one of the creators of the I-XRAY — the system behind the experiment. When the glasses detected a face, that footage was immediately fed into an AI program that would scour the internet to find more pictures of that person. Then, the program took advantage of data sources like online articles and voter registration databases to determine the person’s personal details like name, phone number, and even home address and relatives’ names. This information was then sent back to an app on Nguyen’s phone — all within seconds.

Photo credit: Josh Edelson

The developers of the I-XRAY system did not create it to stalk people. Quite the opposite, their goal is to raise awareness and to demonstrate the capabilities of smart glasses and how, combined with LLMs, public databases, and face search engines, they may be misused in malicious ways. In fact, they even provide a guide to remove your information from data sources used to power this technology.

Smart glasses are not facial recognition tools… yet

It’s worth noting that while smart glasses can be used to perform facial analysis like in the experiment above, they currently lack the processing power to conduct facial analysis in real time on their own. But experts believe it is just a matter of time until they can and that it will have far-going consequences. According to some reports, Meta already has plans to integrate real-time facial recognition technology into their smart glasses. Privacy advocates are already sounding the alarm: on April 13th, over 75 advocacy organizations published an open letter addressed to Mark Zuckerberg, in which they warn about the dangers of building facial recognition technology into common consumer items like glasses. Their concern largely revolves around the potential misuse of the tech and how it opens the door to harassment, stalking, and fraud, especially against marginalized and vulnerable groups like girls and women, immigrants, or political activists. But the experts emphasize that anyone can be at risk. For example, real-time facial recognition could be weaponized by scammers to identify and track their victims in various scam schemes.

Legal gray area

While Meta glasses are not capable of identifying faces in real time, they still draw criticism for allowing the owner to record people without their knowledge. This is a tricky matter, as in many cases it is indeed legal to record people in public places without their consent. However, the legality of the act depends heavily on the country, on whether audio is recorded or not, and on what you are doing with the footage. There is still plenty of room for using the smart glasses in a malicious way. Responding to critics, Meta points to its older statements, saying that per terms of service, “users are responsible for complying with all applicable laws and for using Ray-Ban Meta glasses in a safe, respectful manner.” This sounds great on paper, but ‘paper promises’ like this hardly stop anyone who doesn’t mean well in the first place.

It’s also worth noting that there has been some positive progress in developing new laws addressing these scenarios. Back in February in California, a bill was introduced to specifically prohibit secret recordings with wearable devices in business spaces. The bill has already passed two hearings and is currently set for the next one on May 4. In certain locations, like courtrooms in Philadelphia, smart glasses are already outright banned. The legislation around wearable smart tech is still in its infancy, though, and the advancement in technology so far seems to outpace the legal work that should accompany it.

Meta also emphasizes that their glasses have a built-in LED light that indicates whenever the device is recording, and that they are designed to detect and prevent any attempts to tamper with it. However, in practice, people have already successfully managed to cover, remove, or otherwise render the LED indicator useless, so it can’t be considered a good enough safety measure.

In the end, the conversation should not be centered around the question of “Is it ok to use smart tech to record and surveil other people?” Because many, including us, will agree that the answer is “no,” and also that ethics alone have never stopped the would-be-perpetrators. The much more important question is, should this tech exist at all, at least in the form of a consumer product? It seems pretty clear that the existing legislative framework is not yet suited to deal with the onslaught of privacy violations that will likely come with the advancement of instant facial recognition technology.

Most apps aren’t built from scratch. Instead, developers include third-party software development kits (SDKs) that add functionality like tracking and ads to help them make money. These SDKs are usually provided by ad tech and analytics companies, and they can quietly collect data from your device — things like location, device details, and app activity. This data doesn’t just stay with the app developer. It’s sent back to the SDK provider and may then be passed along to data brokers who can enrich it and resell it further down the chain. And even when apps don’t use those third-party SDKs but still show ads, they take part in real-time bidding (RTB) auctions. Every time an ad loads, your data is broadcast to dozens or even hundreds of companies, who can collect it, store, and reuse — even if they don't end up showing you an ad after all.

This way ads become a kind of hidden Trojan horse for your data. Not just for advertisers, but for governments and surveillance vendors. That’s what turns this system from a nuisance into a real privacy and security threat.
Researchers from Citizen Lab put together a report showing how this system works around the globe, stretching its tentacles into at least 20 countries and enabling tracking based on data from up to 500 million mobile devices. The report is based on a mix of leaked internal documents, public procurement records, technical analysis of server infrastructure. It focuses on the Webloc system, a location-tracking system that collects data from millions of mobile phones through commercial data brokers, and sells them to intelligence actors.

In this post, we’ll break down the most important findings from that report — and show you how to protect yourself.

What is Webloc: scope and scale of the surveillance ecosystem

Webloc was originally developed by Cobwebs Technologies — a company founded in 2015 by former members of Israeli intelligence and special forces. In 2023, the company was acquired by the US investment firm Spire Capital and merged into Penlink, a long-time provider of surveillance tools for law enforcement. The people who were once behind Cobwebs have since taken on key roles within Penlink’s operations. Cobwebs Technologies also has links to Quadream, a spyware developer whose tools have reportedly been weaponized to target civil society members, journalists, and political opposition figures. Cobwebs’ founder, Omri Timianker, has been connected to both organizations. Given these associations, it’s perhaps no surprise that Penlink’s broader work continues to be rooted in controversial data collection and surveillance practices.

In simple terms, Webloc works by tapping into the ad-tech ecosystem — taking behavioral data originally collected to serve advertisements and repurposing it for tracking people’s movements and building detailed behavioral profiles.

According to the documents analyzed in the report, Webloc can track data from up to 500 million mobile devices globally. It processes billions of location signals every day, updating location data every few hours. In some cases, it also keeps historical location records going back up to around three years.

One example from the report shows just how detailed this can get. A person based in Abu Dhabi had 141 apps on their phone, and in just five days the system logged 81 different GPS locations, plus additional hits from nearby Wi-Fi networks. Put together, this turns raw location pings into a continuous behavioral map — where scattered signals are stitched into patterns that reveal routines, habits, and long-term movement behavior over time.

The report points out that Webloc is just one piece of a larger toolkit. First introduced in 2020, it is now sold as an add-on to Tangles — an “open-source intelligence” platform that scrapes and analyzes data from social media, the web, and parts of the dark web. Together, these tools form a powerful surveillance stack. But Webloc is arguably the most invasive part — because it doesn’t rely on what you post publicly. It relies on data quietly harvested from your apps.

Geographically, the system has been in use by governments around the world. Confirmed customers include:

• United States: federal agencies, local police, and military units
• Hungary: domestic intelligence agencies
• El Salvador: national police

Beyond that, there are signs of customers or potential customers in other regions too, including the Netherlands, Germany, Italy, France, Sweden, the United Kingdom, Singapore, Vietnam, Mexico, and the United Arab Emirates.

Even that is likely just the tip of the iceberg. Server infrastructure linked to these tools shows up in dozens more. Besides, in many cases law enforcement agencies simply refused to answer questions about whether they use it. So the real scale of the system’s deployment might be even bigger.

Under the Webloc’s hood: how it collects the data

As we’ve already mentioned, Webloc is built on top of the same data flows that power mobile advertising. And the two main channels that fuel this industry are data collected through advertising SDKs and real-time bidding (RTB) systems.

Researchers from Citizen Lab concluded that Webloc likely relies more on SDK-based data, than RTB advertising data, or a combination of both. Penlink, for its part, told the researchers that it “obtains its location data from providers who obtain user consent for location data sharing through SDKs and who filter out sensitive locations from their datasets, consistent with FTC mandates.” In other words, they claim the data comes from apps where users have agreed to share location through built-in SDKs, with certain protections applied to sensitive places. It’s worth noting that even if we take Penlink’s claims at face value, these kinds of safeguards are hard to verify in practice. Consent is often buried in long, opaque app privacy policies, or not clearly explained at all, and users usually have no real visibility into how many different apps are actually feeding data into these systems.

That said, the researchers point out that Webloc was likely drawing from SDK-based data sources, at least in materials they reviewed. One of the key reasons is technical: Webloc’s interface shows Wi-Fi access point data, including network names. While SDKs embedded in apps can access Wi-Fi-level information directly from devices, RTB advertising bidstream data cannot.

Types of data collected: more than just ‘location points’

Researchers reviewing El Salvador, Vietnam, and US Navy documents from 2021 describe Webloc as working with a wide mix of mobile and ad-tech data. At the core are mobile advertising IDs (MAIDs) and IP addresses, which act as persistent identifiers tied to devices. On top of that, the system collects GPS coordinates and Wi-Fi-based location signals, including Wi-Fi network names (SSIDs) and connection details. Every record is time-stamped, which means movement isn’t just captured as single points, but as something that can be followed over time.

This means that whoever uses the system can figure out things like your likely home and work locations, places you visit often, and repeated movement patterns. It also includes device details such as phone model, operating system, language settings, and device type. App-related signals appear too, like which apps are installed and when they show up on a device.

On top of this, ad-tech systems can sometimes add guessed attributes like age range, gender, and behavioral categories (for example that a person is into ‘luxury shopping’). These are not taken directly from the device, but inferred through advertising models and then linked back to device IDs.

Penlink disputes this broader interpretation. In its response to Citizen Lab, the company says Webloc only contains location data tied to device identifiers and claims it does not include things like age, gender, parental status, interest categories, or browsing history. But in the materials seen by the researchers, persons in the dataset are given labels such as “parent”, “gamer”, “traveler,” and their interests give a pretty clear picture of a person. Even if you only look at location data, calling it “just location” is misleading. Over time, location history alone can reveal where someone lives, where they work, their daily routines, travel habits, and general lifestyle patterns, especially when tied to a persistent device ID.

One Penlink-branded document cited in the report describes Webloc as producing “demographic insights” and enabling “detailed identity and lifestyle pattern resolution” which sounds more like the truth, as it suggests that the tool is built precisely to interpret behavior, not just log movement. The researchers also note that Penlink itself says in its privacy policy that it may receive data from “data brokers” and other commercially available sources, including name, email, phone number, and “historical information about the precise geolocation of your device,” and that it may disclose this information to its customers.

‘Mission creep’ or when temptation to surveil is too big

Some would argue that tools like Webloc exist for a good reason. In theory, they are meant to help law enforcement with combating serious crimes like trafficking, terrorism, and assisting in finding missing persons. In a perfect world, that might even sound reasonable: a tightly limited system used only when there is clear necessity and strong oversight.

But that’s not how these tools tend to be used in practice. Once a system like this exists, the temptation to use it beyond its original purpose is hard to resist. And this is where things start to go south.

The researchers refer to the phenomenon as a mission creep — a slow shift where powerful surveillance tools become investigative shortcuts for petty crimes. One example from the report illustrates this clearly: in Tucson, Texas, police described Tangles and Webloc as tools purchased for sex trafficking investigations. But in practice, it was used to look into minor crimes, even theft of relatively small items, like “thousands of dollars of cigarettes.” What’s important here is that these systems don’t just affect suspects. They also pull in people who were never part of the crime in the first place. Anyone whose device happened to be in the wrong place at the wrong time can end up in the dataset, cross-checked, linked, or mapped into an unrelated investigation. Even if this data is never actively used, it often remains stored somewhere, effectively lingering in the background. That alone increases the risk of misuse, unauthorized access, or data breaches.

Even more worrying is the report that Cobwebs Technologies provided its location-tracking capabilities and Tangles platform to a private intelligence initiative like Skull Games — a loosely organized network of volunteers who meet in “hackathon”-style events to identify suspected traffickers and sex workers using open-source intelligence. Although access to Cobwebs’ most powerful tools during a 2023 event appears to have been restricted to a company representative running queries on behalf of participants, the partnership still exposed people with no formal authority to highly sensitive surveillance capabilities, blurring the line between the use of such tools for legitimate investigation and vigilantism.

Bottom line, it’s almost inevitable that even if a system is introduced with a narrow justification, it will become a general-purpose surveillance tool, leading to the creation of a society where surveillance is easy, constant, and increasingly normalized.

Why privacy laws struggle to stop it and what you can actually do

What makes systems like Webloc hard to pin down is that they don’t technically operate outside the law. Instead, they operate inside it, or rather at the edges of it — in a gray zone. We’ve already seen how the data flows behind mobile ads and tracking SDKs are not really a secret, and we’ve covered the mechanics of it before in more detail here.

On paper, regulations like GDPR in Europe and in the US are supposed to protect users. GDPR requires clear, informed consent and says data should only be used for the purpose it was collected for. In the US, the FTC has already clarified that advertising identifiers are not truly anonymous and can be tied back to real people — names, phone numbers, addresses. That’s also part of what led to enforcement actions like the recent first-of-its-kind ban on a data broker for selling sensitive location data.

But in practice, the system is messy. Data is collected through apps, passed through SDKs, enriched, resold, and eventually ends up in places like surveillance platforms. Even when each step is “technically compliant,” the end result is still the same: large-scale behavioral data about people who never really understood they were part of this chain.
The uncomfortable but undeniable fact is that this surveillance system is built on the normal plumbing of the internet. The ad-delivery ecosystem on which most apps rely on to stay free becomes the same channel that feeds data into these larger surveillance systems.

That’s why ad blocking tools come into the equation from a different side than before. Not because ads are the problem in themselves, but because they are one of the main pipes through which your data leaks out. Tools like AdGuard can help reduce exposure by blocking tracking requests, and cutting down RTB-style data flows at the browser and network level. On mobile it’s more restricted due to operating system limits, but partial protection is still possible, especially with DNS filtering and system-wide blocking features.

That said, no tool fully solves it on its own. The broader strategy also comes down to a few other habits:

• Be mindful of what apps you install, especially obscure utility apps
• Check their reputation before installing if something feels off
• Give the minimum permissions needed (especially location, contacts, background activity)
• The fewer apps you use, the less data you leak
• The fewer permissions you grant, the less can be collected and shared

What all of this shows is that the infrastructure behind ad-based tracking is no longer just about ads. And blocking them isn’t only about getting rid of visual clutter — it’s also about reducing your digital footprint and lowering exposure to surveillance risks. That said, none of these measures eliminate the threat entirely. A real fix would require broader changes across the whole ecosystem, from how data is collected and shared in the ad industry, to how it’s regulated and enforced at national and international level.

AI today has seemingly found its way into every single aspect of life, its applications ranging from obvious areas like coding or image processing to less apparent ones like disease diagnostics and legal work. AI is absolutely everywhere. And even if you know very little about how it works, you have probably at least heard that every AI requires troves of data to learn from before it can be put to use.

This data has to come from somewhere, and this gets us to the first question of today’s TechTok:

Are apps and websites using my data to train AI without me knowing?

There is no short definitive answer to this question. The best we can come up with is: “Yes, they do, but not necessarily in the way you might think.” We are aware that you probably didn’t come here to get a broad answer like that. But before we dive any deeper, let’s get one thing clear: “training AI” and “collecting data” are not synonyms, although they are related. To put it simply, to train AI you need data, so finding ways to obtain that data is one of the biggest challenges when you’re building an AI system. However, there are countless other reasons why someone might want to get their hands on your information.

The thing is, the concept of online data collection has existed for decades, long before AI even appeared on the digital horizon, and the main driving force behind gathering user data for many years has been advertising. Insanely complex systems have been built to create user profiles and to track users across various apps and websites, all with the goal of knowing exactly which ad to show to which person at what time and to increase the probability that that person clicks the banner. The digital ad market is estimated at about USD 600 billion to USD 700 billion per year, and at the foundation of this market is user data — this should give you an idea about why data is so often called the new oil.

Of course, there were other reasons why companies would seek digital data: personalization, recommendations, fraud detection, billing, retention, product analytics — often important in sectors like finance, retail, telecom, and marketplaces. The exact reasons are beyond the point. What we want to highlight here is that global and rampant data collection was not spawned by the emergence and subsequent spread of AI. In fact, in many cases, the collection methods used today to gather data for AI training are the same that have been used for years for other purposes, so AI companies didn’t have to reinvent the wheel, or at least they had a very solid foundation to stand on.

The types of data required for ad tracking and AI training overlap heavily too — which might come as a surprise for some. In the minds of many people, the terms ‘AI’ and ‘LLM’ (large language model) are synonyms. Indeed, chatbots (which are basically user-facing shells with an LLM underneath) are perhaps the most commonly interacted with type of AI for an average user. Common sense dictates that training a generative AI used in a chatbot would require datasets that include tons of user-generated text — such as posts and comments on online platforms like Reddit or X, chat inputs, reviews, etc. This is correct, as these LLMs need to learn how people actually talk, how to answer questions, how real-life conversations flow; things like humor, slang, tone. But what many people do not realize is how many different types of AI other than generative there are, built for so many different purposes — recommendation systems, search ranking, ad targeting, just to name a few. For these AI systems, behavioral data is king, while content itself matters much less. And many modern platforms combine both approaches: they need raw content, but also they want to know what you click and when.

So, circling back to the initial question: yes, some AI companies take advantage of your data to train their systems, but they largely do that the same way they (and other companies) have been collecting your data before AI for other purposes. And here comes the tricky part — technically, most companies do not collect data behind your back, to train AI or otherwise — doing so is illegal in many jurisdictions. Some go as far as making a public announcement about their incentive to use your data for AI training, although some sugarcoat it more than others. At the same time, it’s a fairly common practice to hide the ongoing data collection behind lengthy privacy policies, tedious terms of service, and other long and boring legal documents. Those with a darker sense of humor might even find it funny that privacy policies that cover data collection for AI training often use the same vague language and broad wording as you would find in similar documents about gathering information for ad tracking purposes.

But even if you do your due diligence and power through all the legalese to confirm that the app you want to install doesn’t use your data to feed the proverbial machine, the sad reality is, you are still not in the clear. Sometimes the developers ‘forget’ to mention it, as was in the very recent case where OkCupid, a popular dating app, shared 3 million user photos with an AI company to train on — all without telling its users about it. This is nothing new; the same shady practices have existed forever even before AI. Unfortunately, where there is profit to be gained, there always will be those willing to turn a blind eye to the law to their advantage.

How does your data end up training AI?

Let’s now take a step back. We’ve touched a little on the topic of which data is being used to train AI and mentioned that anything goes: both raw content, like texts and photos, and behavioral data, like clicks and other interactions. But many readers would probably like us to be more specific and wonder: “What exactly of my data could end up being used for AI, and how?” Well, not all data is used in the same ways. Some data may be more sensitive, and data from different sources may feed AI differently. If your goal is to train AI, there are countless potential sources to get the training data from. For the purposes of this article we will identify four categories, depending on the way the data is collected:

• Social media (publicly available data)
• Chatbot conversations (direct input)
• Platform interactions (behavioral data)
• Third-party apps and websites

First off, if you post or comment something publicly — on Reddit, YouTube, X, Facebook, etc. — that does not automatically mean anyone can use it for AI training, but you also usually lack any real means to ban the platform from training AI on your content or sharing your data with third parties. Of course, everything varies greatly from platform to platform, but the rule of thumb remains: if it’s public, you probably don’t control it. The platforms that don’t make use of users’ data themselves often sell or share it to others, in some form or fashion. Users from the EU are generally protected better than others, thanks to the EU’s advanced privacy legislation. Regulations like GDPR and the EU AI act give EU citizens the rights to be informed, object to certain processing, request access or deletion of their data in some cases, and challenge or restrict the use of their personal data for AI training.

But what if you talk to a chatbot directly, what are the chances that your input will be used for AI training? Depends on the service, of course, but more often than not with consumer-facing AI tools anything that you type in or upload may be used for improving that service. Even if you are on a paid plan, unless it’s a corporate/enterprise (not an individual) plan, your data is still mostly treated as fair game. It needs to be mentioned that many AI chatbots at least offer an opt-out feature for users, even if in many cases they are buried somewhere deep in the settings. We imagine that for many readers of this article this is one of the key questions: “How do I opt out of data collection when talking to my chatbot?" It seems important to provide some practical advice here rather than settle for some general words. There are hundreds and even thousands of chatbots, so let’s focus on some of the most common ones (we assume personal use everywhere, and not enterprise or analogs):

1. ChatGPT. Open ChatGPT, go to your profile, then Settings → Data Controls, and turn off “Improve the model for everyone.” OpenAI says this stops your chats from being used to train ChatGPT going forward, though some retention may still apply. OpenAI also used to grant opt-out status upon receiving a message to support. If you did that at some point in the past, OpenAI claims to still honor that request, but this path is no more available to newer users.

2. Perplexity. Open Account settings → Preferences and switch off “AI data retention.” Note that this opt-out will only affect future data, anything collected before the opt-out date may be used by Perplexity for AI training and cannot be deleted or removed.

3. Gemini. In your Google account, go to Data & privacy and find “Gemini Apps Activity,” then select “Turn off” or “Turn off and delete activity.” This will only prevent future sampling and will not affect any past interactions. Mind that with multiple Google products that use Gemini, the exact training/privacy behavior will depend on the product.

4. Claude. Claude doesn’t train its models on your conversations by default, only giving you an option to opt in manually if you’d like to. If you delete a conversation, Anthropic removes it from their systems within approximately 30 days.

As for behavioral data collection, a simple (but mostly accurate) way of thinking about it is: the larger the platform, the more they rely on your behavioral data; smaller narrowly functional apps and services rarely engage in tracking your behavior. Big content platforms like YouTube, TikTok, or Netflix, search engines, e-commerce platforms like Amazon or eBay — these are the ones that you can be sure about. They will collect as much data about your activities as they can to hone their recommendation and ranking algorithms. It doesn’t mean that smaller apps don’t do that at all, but for them this kind of tracking is much less relevant.

But what about the ‘regular,’ smaller apps and websites that we use every day? Not everything is a chatbot or a huge platform, what if you just install a random app or a game, or visit a smaller website? Again, it is impossible to give a single answer for all of them, as there are literally millions. But, in general, such smaller apps and websites are not interested in your data to train any AIs of their own, and also they rarely directly sell users’ data to someone else who might be. However, it is beyond common for the developers of such apps and websites to include analytics SDKs, ad networks, and other tracking tools for monetization purposes. These tools can, and very much do, collect stuff like behavioral data, device info, usage patterns, and so on. And when this data gets to ad networks, data brokers, and analytics firms, it gets aggregated and can easily be used for modelling, sold, or can otherwise indirectly contribute to AI training (among many other things, of course).

When you look at all these ways in which your data can end up in some AI’s training dataset, you might think: “That’s a lot to worry about!” That is true, somewhat, but also keep in mind that not every single bit of information that you provide gets used, and not all companies behave the same way. And, last but not least, there are ways to minimize the amount of data collected about you. Which brings us to the second question of today’s TechTok:

Can using an ad blocker and/or a VPN stop AI tracking and data collection?

As you just saw, AI tracking takes so many different forms that it is impossible to give a “yes or no” answer to this question. Both an ad blocker and a VPN can help, each in its own way, but not against everything.

First of all, neither of them will help if you actively provide data: talk to a chatbot, post on social media, leave comments. Ad blockers and VPNs can’t magically prohibit the platform from using something you have already given them, directly or indirectly. Against that type of data collection, your best bet is privacy settings, opt-out toggles, and laws aimed at protecting privacy. Check out privacy policies and available privacy settings of the platforms and apps you engage with, and if you don’t like what you see, consider picking a different option.

What ad blockers can help with is third-party trackers that collect data about you for future use and, to some extent, behavioral tracking. Stopping third-party analytics is, without question, the strongest suit of ad blockers when it comes to preventing your data from leaking. Ad blockers like AdGuard can deal with most, if not nearly all, third-party trackers on websites. Inside apps, things might get trickier, but this is true in general — Android and iOS have rather strict limitations when it comes to interfering with the traffic of other apps.

Ad blockers can also help stop the collection of behavioral data, but not entirely. Unfortunately, most major platforms rely heavily on first-party tracking and don’t need third parties to build recommendations, train models, and analyze behavior. Often, blocking first-party tracking, especially on large platforms, interferes with the useful functionality — imagine that you block first-party tracking on YouTube and the videos suddenly stop loading. And yet again, these problems are more pronounced in mobile apps than on websites.

Still, an ad blocker is one of the best resources available to you if your goal is to starve the AI training algorithms. But what about VPNs?

VPNs are great — some may even say essential — for privacy protection. But when it comes specifically to stopping your data from being used for AI training, their use is limited. Still, they can be helpful, but not in a direct way. VPNs hide your IP and mask your location, making it harder for websites and third-party trackers to link your activity across different sites or build a profile based on your network identity. However, a VPN does not stop the platforms you use from seeing what you do on them. If you are logged into an account, or even just interacting with a website or app, your clicks, searches, and inputs are still recorded directly by that service. A VPN also will not stop third-party trackers from gathering information about you — leave that job to ad blockers (although a VPN may make tracking less precise).

Let’s recap: ad blockers and VPNs are great tools in your privacy protection arsenal, and they certainly will not hurt if you seek to protect your data from becoming AI training fodder — especially ad blockers. But in the end, your data’s safety is first and foremost dependent on your own attentiveness and diligence. If you study the privacy policies before using apps and services, if you are mindful about what you post online and what information you share with a chatbot — the chances of your personal details becoming a part of some future AI’s learning dataset can go down significantly. It’s good to have strong tools on your side, but nothing beats good old caution.

Upd. This promotion is over. If you didn’t get a chance to buy AdGuard Ad Blocker, AdGuard VPN or AdGuard DNS at a discount, don’t worry — we often run other promotions. Not to miss the next one, subscribe to our newsletter — we’ll keep you in the loop!

We often think of self-care as something that happens offline — mental health walks, the treasured eight hours of sleep, guided meditations. But sometimes we forget that feeling in control online matters just as much.

Constant notifications, ads, and endless scrolling can make spending time online overwhelming. Thankfully, there are simple steps you can take to streamline your digital experience.

Take care of your passwords

Reusing the same password (or slight variations of it) is common — I’ve definitely done it and then struggled to remember which tiny symbol I changed.

A password manager can help by generating and storing strong, unique passwords, so you only need to remember one master password. There’s no need to keep track of every tiny change you made.

Clear your browser regularly

Another simple thing you can do right away is clear your browser’s cache and cookies. Doing this on a regular basis limits tracking and makes your browser run more smoothly. Just use a shortcut Ctrl+Shift+Delete on Windows/Linux or Cmd+Shift+Delete on Mac.

If you want to block tracking cookies but keep your login sessions, AdGuard can help. Just click Delete third-party cookies in Tracking protection.

Delete apps you don’t use

Unused apps take up space and can become security risks if they’re outdated. Make sure to delete your account if you no longer use the app. Then delete the app from your phone altogether — and breathe a little easier.

Block notifications you don’t need

Most phones are filled with notifications you don’t really need — you may receive dozens of push messages urging you to subscribe to a new service, order fast food, or keep playing a game that’s been taking up too much of your time.

Take a few minutes to turn off non-essential notifications. While you’re at it, check app permissions in Settings. Some apps may be tracking your location or accessing more data than necessary in the background.

Get rid of ads

Imagine trying to close an ad before a video, but it opens another popup, then another. Suddenly, you’re on the verge of a mental breakdown in the middle of your lunch break.

An ad blocker like AdGuard can help clean up your browsing experience by removing intrusive ads and banners and making it much harder for companies to track your activity.

Use a VPN to protect your privacy

If you’re worried about websites and advertisers tracking your activity, a VPN can help hide your IP address and encrypt your connection.

Try AdGuard VPN — it is designed to protect your connection without logging your activity or selling your data.

Set up DNS filtering

Some devices, like smart TVs, don’t support ad blockers. This is where DNS filtering comes in handy — it can protect all devices on your home network from ads and trackers.

You can also set up security features to block harmful content or reduce the risk of phishing.

Special discounts for AdGuard digital self-care week are valid through April 28.

And remember: small steps are enough. It’s impossible to block every single ad or tracker, or protect yourself from every single password leak. But what matters is knowing you are making your online experience safer.

At AdGuard, we develop a whole range of privacy tools: an ad blocker, a VPN, a DNS service, an email service, and soon — a crypto wallet. These products do a lot of complex things under the hood — but they need to stay simple and friendly on the outside.

Our users come from all over the world. Many of them don’t speak English natively, many aren’t tech-savvy. And yet we want all of them to feel supported while using our tools.

In this post, I want to show you how we at AdGuard approach UX design and copy:

• What our writing and design are built on
• How we handle different levels of user experience
• How feedback and real-world constraints shape the final result
• And how you can help us make it better

Let’s begin with the foundation.

What we build on: Our principles

At AdGuard, we like to say that we’re on the light side of the Internet. This idea runs through everything we do.

• We care about transparency and openness
• We actively show it — help users, explain things, or just get out of their way when needed
• We give the control to the user. We believe the product should adapt to the user — not the other way around

Here’s what it looks like in practice.

Transparency → Clean UI, clear copy

We try to keep interfaces as clean as possible, with just enough visual and textual information to help you complete the task.

One button to enable protection

Active care → Help that’s always there

In our apps, you’ll often find small hints, tooltips, and summaries. Where needed, we add short explanations or links to longer articles — so you can dive deeper when you want to.

What users see when adding a custom filter

User control → Deep functionality, optional complexity

Most of our apps are layered. If you want to just turn on protection and move on, that’s great. If you want to dig deeper, you’ll find detailed settings, expert modes, and customizations. Your control is not necessary, but if you seek it, we’ll do our best to provide it to you.

Additional settings are shown when you choose to customize further

Our audience: How we approach different needs

Not everyone uses AdGuard the same way.

Some want to set it and forget it. Others enjoy tweaking settings and learning more about how privacy works. We try to support both.

Here’s how we approach it.

Layered experience

You can think of our apps as layered systems:

• Simple on the surface — for those who want quick protection
• Customizable in the middle — for those who are curious to learn and tweak things a bit
• Deeply configurable — for power users who want full control without hand-holding

This approach helps us keep the UI clean, while still offering depth for those who need it.

One example is our User rules feature — a tool that allows users to create custom filtering rules.

Our previous approach was designed for advanced users only. There was a blank input field and a link to the documentation.

Old User rules unterface

Now, in AdGuard Mini for Mac, we’re trying something new: a guided interface that walks you through the process.

Creating a user rule in AdGuard Mini for Mac

We explain what the rule does, where it applies, and how it works. We hope this makes it easier to get started, even if you’ve never written a rule before.

Let us know what you think: is it clear? Easy to follow? Missing something? Your feedback helps us decide whether to bring this approach to other platforms too.

Vocabulary

To meet users where they are, we adjust the vocabulary based on how familiar they might be with the topic and what they’re trying to achieve.

• AdGuard protects you from ads and trackers — for those who just want peace of mind online. This works well when the user’s goal is simple: “I want the ads gone,” or “I want to feel safer.”
• AdGuard blocks known analytics and phishing domains — for users who already understand the basics of online threats and want more transparency: “I know different types of tracking and risks exist, and I want to manage them more consciously.”
• AdGuard lets you apply advanced cosmetic rules and scriptlets from custom lists — for experienced users who want full control, less explanations and more just tools: “I know what I’m doing; just let me configure it.”

Feedback: Where we learn what works

We’re constantly learning from you — and trying to reflect that in the product.

Sometimes the insights come from anonymous usage data. For example, we noticed that some users were ignoring the “card” about HTTPS filtering on the main screen. Without HTTPS filtering, however, most ads cannot be blocked. So we changed the wording on that “card” — and the result was immediate: the number of users who enabled the feature increased by about one and a half times.

New wording (right) drives more taps than the old one (left)

Sometimes feedback comes from support requests. One user once asked where they could find their VPN license key. The thing is, AdGuard VPN doesn’t use license keys — the subscription is simply tied to the email address used for purchase. So we added a short explanation to the confirmation email that users receive after buying a license. After that, questions about VPN license keys practically disappeared.

Post-purchase email for AdGuard VPN users

Constraints

We can’t implement every idea we come up with — and that’s not a bad thing. Constraints shape our work and help us better understand how and what to communicate to users. Here are a few examples of these constraints — and how we deal with them.

Legal text

We’d love for all our texts to be friendly and concise, but in some cases we need to be legally precise to avoid any ambiguity. That’s why you may come across legal wording in our products — but whenever possible, we also provide a shorter, clearer summary to help you understand it quickly.

Excerpt from AdGuard’s Privacy policy

Localization

We build products for a global audience, and they are translated into 20+ languages. Even though we try to cover as many languages as possible, there will always be users who interact with our apps in a language that isn’t their native one. That means when we write in English, we have to keep in mind that not only native speakers will read it:

• Vocabulary and syntax shouldn’t be too complex
• We avoid local jokes, idioms, and wordplay that only native speakers would understand

Another constraint is how translation works in practice. We have several in-house translators and a group of dedicated volunteers who help us a lot (you can join too — read how). But in some cases, we have to rely on machine translation — and it’s much harder to provide context there.

This leads to a few additional rules:

• Limit the amount of information in one message — English words are often shorter than their equivalents in other languages
• Whenever possible, write in full sentences and avoid ending strings with prepositions — this can make translation more difficult

Let’s take another look at the User rules section.

When we were designing it, our initial idea was to make it feel like natural English: “Block request to ads.com.”

But in many languages, that structure wouldn’t work:

• Slavic languages use grammatical cases
• Turkic languages use postpositions, not prepositions
• Asian languages often require more context

So we rewrote it as “Block requests to this domain.” It’s longer, but clearer for most of the users — and translators.

UI element for creating new rules in different languages

What’s next — and how you can help

Here’s a funny paradox: we’re a privacy company, so we collect as little data as possible. But that makes it harder to know which parts of our apps are most useful and which are confusing. We’re working on privacy-friendly ways to learn more about how you use AdGuard — so we can make things simpler, clearer, and more useful.

One small thing you can do to help: turn on the setting called Send anonymized app usage data. You’ll find it in AdGuard Ad Blocker, AdGuard VPN, and AdGuard Mail.

This option doesn’t send anything personal. We don’t collect identifiers, track you, or share your data with anyone. But it does help us see which screens people interact with — and which features need improvement.

Our next step is to rely more on insights like these when making product decisions. They help us see what really matters to users — and guide us in making AdGuard apps even clearer, more intuitive, and more helpful for you.

If you see something confusing — or something that worked well for you on our apps and websites — please let us know by clicking the button below.

We use every opportunity to make our apps clearer, more helpful, and closer to what you actually need!

What doesn’t feel like part of the deal, though, is a dating platform sharing your sensitive information, photos, and location data with some AI company you’ve never even heard of, and doing it without your consent. That crosses a line and constitutes a breach of trust. But that’s exactly what OkCupid, a dating app owned by Match Group (which also owns Tinder, Hinge, and Plenty of Fish), did.

And what’s worse, when it was found out, the punishment it received was little more than a slap on the wrist.

When user data is seen as the company’s property

In a proposed settlement that OkCupid and its parent company, Match Group, reached with the US Federal Trade Commission (FTC) in March this year, the government alleged that the app was “deceiving” users by sharing their personal information — including photos and location data — with an unrelated third party. This was done without users’ knowledge or consent, and in violation of OkCupid’s own privacy promises.

At the time of the violation — back in 2014 — OkCupid’s privacy policy stated that it could share user data either with “service providers,” business partners, or affiliated companies, or otherwise only after explicitly informing users and giving them a chance to opt out. But that’s not what happened. The FTC found that OkCupid shared the information of potentially millions of users, including up to 3 million user photos, with an AI company called Clarifai. That company was none of those things — not a service provider, not a partner, not an affiliate — and OkCupid never asked users for consent, nor gave them any chance to opt out. In practice, that left millions of people completely unaware that their data was being repurposed behind the scenes.

How and why did that happen? The explanation is fairly mundane. OkCupid’s founders had a vested interest in Clarifai, which later used those 3 million photos and other user data to develop facial recognition and image-processing tools. Namely, they had invested in the company and treated OkCupid as a convenient source of data. As Ars Technica reported, Clarifai’s CEO acknowledged that the data helped them build a system that could “identify the age, sex and race of detected faces,” meaning users’ photos were turned into training material for the tool they never agreed to support. The FTC noted that, for years, OkCupid tried to deny having any relationship with the AI firm.

On paper, this might have been framed as acceptable under vaguely worded policies. But in practice, OkCupid was treating user data as if it simply belonged to them. That runs against the spirit of the privacy promises they made. Because what the policy suggested and what the users reasonably believed was that their data would only be used in the ways explicitly described. And training AI models was never part of that.

To see how problematic that behavior is, consider a simple thought experiment: imagine the founders hadn’t invested in an AI company, but in something like a car insurance broker or a health insurance firm, and then casually gave that completely unrelated business access to sensitive user data collected through OkCupid. The data could then be used, for example, to infer people’s lifestyles, sexual orientation, or health risks and then influence their insurance rates or eligibility — in other words lead to negative real-world consequences for users based on the data they never knowingly shared for that purpose.

Slap on the wrist

You might think such egregious mishandling of user data would come with serious penalties. But that wasn’t the case. As part of the settlement, OkCupid was essentially just barred from misrepresenting its data collection practices and privacy controls going forward. No steep fines — in fact, no fines at all — and no real long-term consequences beyond the obligation to comply. In theory, people affected could still try to sue in civil court, but that’s a long shot, especially since Match did not admit any wrongdoing.

Source

This type of punishment is hard to take seriously. In effect, that's not a penalty, it’s a mere restatement of the rules. What this basically amounts to is being told not to do something they weren’t supposed to be doing in the first place. That makes the whole thing feel less like enforcement and more like a pinky promise. And that’s a hard sell, coming from a company that already showed it was willing to stretch or rather ignore its own promises when it suited it.

Sharing user data without consent: the rule, not the exception

OkCupid’s case is only the most recent example of this kind of possessive attitude toward user data. But while some argue — Match Group among them — that times have changed and such permissive practices are long behind us, that couldn’t be further from the truth. Cases of companies mishandling user data often by quietly sharing or outright selling it without clear consent have been piling up in recent years.

Take Grindr. In recent years, the app faced major penalties across Europe after it was found to be sharing highly sensitive data, including sexual orientation, precise location, and advertising identifiers, with hundreds of advertising partners without valid consent, leading to a $6.1 million fine in Norway and ongoing mass legal action in the UK over the alleged sharing of HIV-related data with advertising firms.

Or another dating app, Raw, where in 2025 a security lapse exposed users’ exact, street-level locations along with personal details like sexual preferences and birth dates. This kind of exposure doesn’t just create online risks — it can translate into real-world vulnerability. Adding a more dystopian edge, the incident came at a time when the company was exploring making a wearable device meant to monitor partners’ physiological signals, raising obvious concerns about surveillance layered on top of already shaky data practices.

And it’s not just dating apps. In 2024–2025, General Motors and its OnStar unit were found to have quietly collected detailed driving behavior. This included data on braking, speed, and location, which was later sold to data brokers, and then used by insurers to raise premiums, in some cases dramatically. Again, there were real-world financial consequences for users. The FTC ultimately banned the practice for five years following an investigation into it.
Similar patterns have shown up elsewhere too — from networking platforms like LinkedIn to data brokers and even security software. In all of these cases — and there are more waiting to be discovered — user data was quietly repurposed, shared, or sold without people ever really knowing. If anything, they show that the idea that privacy promises are little more than hot air hasn’t really gone away.

What this actually means for users

It’s easy to treat these cases as abstract violations or regulatory issues, but the consequences are anything but abstract. When this kind of data is shared, leaked, or repurposed, it can expose deeply personal information: from sexual orientation and health status to precise location history, and often to parties users never even knew existed.

That can lead to anything from targeted manipulation and profiling to real-world risks, like harassment, discrimination, or financial penalties, as seen with insurance data. And once that data is out there, there’s no real way to take it back or control how it’s used next. And as more systems start relying on collecting this kind of data, the stakes only get higher.

This is becoming especially clear with newer practices like age verification, which is seeing growing adoption around the world and often requires users to hand over highly sensitive information, such as facial scans or government IDs.

The higher the stakes, the bigger the problem

So, while the risks and concerns aren’t new, the situation is getting progressively more precarious. Take firms like British age verification leader Yoti which was recently found to be collecting and retaining biometric data without valid consent — or Discord, which introduced ID-based age verification and then landed in hot water after that data was exposed in a breach. In both cases, users were asked to hand over highly sensitive data, only for it to be mishandled or exposed.

The world as a whole is moving towards more data collection for the sake of convenience. We’re increasingly surrounded by technologies built on the same premise — from home surveillance systems like Ring to city-wide tracking networks like Flock, which use AI-powered cameras to log license plates and vehicle details into searchable databases.

But even though these innovations are touted as a boon to security, these are all part of the same underlying problem. You’re expected to trust that these systems won’t be hacked, and at the same time trust that companies won’t misuse your data. But we’ve already seen both happen, often without users even knowing. Even when policies sound reassuring, there are always people inside organizations with access, and it only takes one misuse or one bad apple.

Which is why things like mass data collection, behavioral tracking, or always-on monitoring — whether it’s framed as safety, personalization, or innovation — feel progressively less like features and more like liabilities. Because when something goes wrong, it’s the users who deal with the fallout, not the companies collecting the data. We are expected to trust the companies to do the right thing, and rely on someone to catch it when they don’t (if you’re lucky). Maybe that’s always been the case. But as long as there are no real consequences — as the OkCupid case has shown — there’s very little incentive for them to do anything differently next time.