Google’s big Android sideloading crackdown has a 24-hour catch – how the new limits work


Unlocking Android Developer Mode.

Adrian Kingsley-Hughes/ZDNET

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ZDNET’s key takeaways

  • Google claims the move will make Android safer.
  • Sideloading from unverified developers will involve a five-step process.
  • There will also be a mandatory 24-hour cooling-off period.

For years, one of the clearest differences between Android and iOS has revolved around who has ultimate control over the hardware. Apple has always maintained that a closed ecosystem is the only way to keep users safe. Coincidentally, that closed ecosystem has also been good for Apple’s bottom line because it’s easy to grab a chunk of most digital sales on the platform. Buy a movie or pay for an app subscription, and Apple gets a commission of between 15% to 30%.

Google chose a different approach. Yes, Google has the Play Store, and yes, Google gets commission from app subscriptions and in-software add-ons. And while, for most users, the Google Play Store is where they get their apps, there are alternatives. And one of those alternatives is sideloading, the ability to install apps from unverified developers, bypassing Google’s Play Store.

Also: 3 unofficial Android Auto apps I installed to make my car screen more useful – and how

But Google is planning to make some big changes to sideloading, all in the name of security.

Changes are coming.

Adrian Kingsley-Hughes/ZDNET

Last year, Google began to outline how this approach would work. And the company was eager to emphasize that sideloading wasn’t going away

Also: This silent Android feature scans your photos for ‘sensitive content’ – how to uninstall it

But the more I read about Google’s plan to change how sideloading works, the more I feel that the process is essentially dead.

Don’t ever sideload anything onto your Android device? Then none of this affects you in any way whatsoever.

Why limit Android app sideloading?

According to Google, sideloading is a security risk. In fact, the company’s analysis found sideloading is responsible for “50 times more malware from internet-sideloaded sources than on apps available through Google Play.”

Also: I found a free Android app that makes deleting photos as easy as swiping left

That’s a pretty compelling statistic. I mean, we know from platforms like Windows (and Mac OS) that people will download and install all sorts of stuff onto their systems in exchange for the promise of some benefit (usually something free that would otherwise cost money). 

But Google is also aware that some users need a way to sideload apps, so it’s developed a way to allow the practice to continue, while making it harder for bad guys to exploit the mechanism. 

And this shift means some big changes.

What are the new changes?

Matthew Forsythe, Google’s director of product management for Android app safety, has outlined the new process that power users will need to navigate to bypass the security mechanisms and sideload apps from unverified developers.

5-step process to sideload apps from unverified developers.

The process to sideload apps from unverified developers.

Adrian Kingsley-Hughes/ZDNET

  1. Enable developer mode: Open the Settings app, scroll down to About Phone, and tap the build number seven times. You’ll be prompted to enter your passcode, after which, you’re in.
  2. Confirm that there’s no coaching going on: Is someone trying to get you to turn off your security? That’s a red flag, and Google wants to highlight that risk.
  3. Restart the phone and reauthenticate: This step acts as a firebreak if a third party is involved in sideloading.
  4. 24-hour cooling-off period: Google will enforce a 24-hour cooling-off period before allowing sideloads. The approach will also require biometric authentication (fingerprint or face unlock) or device PIN to continue.
  5. Install: Now the user is ready to install apps from unverified developers, and they’ll also have the option to enable the approach for seven days or allow it indefinitely. 

This mechanism, which Google calls Advanced Flow, won’t be part of the open-source element of Android, but will instead form part of the closed-source, proprietary Google Play Services platform.

Also: How to clear your Android phone cache in 30 seconds

Sideloading apps from verified developers and developers with limited distribution assets won’t change (here, limited distribution is very limited, and restricted to only 20 devices). These changes would apply to developers who use an outlet, such as F-Droid, and who have nothing to do with the Play Store. 

Google is planning to roll out these changes for “apps in select regions” starting September 2026.

The argument for sideloading

The biggest argument is freedom. It’s your hardware, and you should be able to do anything you want with it, up to and including installing junk and malware. 

The best overview I’ve seen as to why sideloading is important, and that any changes to the way it works will ultimately be harmful, is on Reddit. The discussion covers everything from device freedom to developer privacy and safety to having the ability to adapt and fork open-source programs. 

Reading between the lines

There’s no doubt that sideloading is a route for malware onto Android devices, and Google has the receipts for its “50 times more malware” claim. On the flip side, there’s no doubt that sideloading is a feature many Android users are passionate about.

It’s hard not to overlook how deliberately cumbersome the solution that Google has come up with here is, and I can’t see anyone outside of the more hardened power users bothering to jump through all the hoops. And given that this mechanism is baked into the proprietary bit of Android, Google could decide to change it or pull the plug on it entirely down the line. 

It’s also important to keep in mind that while it’s easy to get focused on apps from unverified developers (especially legitimate or scammy tools), ultimately, encouraging developers to become verified can have repercussions, because Google has the power to block apps from any developer. 

Also: Your Android phone is getting agentic powers with Gemini Intelligence

What kind of apps might Google want to pull the plug on in the future? It’s not hard to think of some. The tech giant might be put under pressure by companies to pull the plug on things such as emulators (a class of app that requires developer ID checks on the Play Store for some time now), or it might want to stop tools, such as ReVanced, an app that can, among other things, enable YouTube Premium features without a subscription. I can definitely see Google wanting to protect app revenues, and blocking these kinds of apps would help it do that. 

Can anyone save sideloading?

Probably not. 

The fuss kicked up so far might have elicited some concessions from Google, but I’m fairly sure the company had similar plans all along. Users could switch to iPhone, but that’s a tighter ecosystem. Those with compatible handsets could install custom operating systems, such as LineageOS and GrapheneOS, but this path is not for the faint of heart.

What about legislative pressures on Google from outside the US, from places such as the EU? After all, the European Commission — a body that is no fan of any of the big tech corporations — forced Apple to allow third-party app stores, sideloading, and alternative payment systems, killed off the Lightning port, and might ensure the iPhone has a user-replaceable battery. 

Could the EU save sideloading as we know it? I wouldn’t hold my breath, and that’s because Google hasn’t blocked sideloading. Instead, the company has just put a whole bunch of hurdles in the way. 





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Another day, another politically motivated attack in the United States.

This morning’s shooting at a Dallas ICE detention facility – where a sniper killed two detainees and wounded another before taking his own life prompted me to revisit a question that’s been troubling me: Is political violence actually increasing in America, or does it just feel that way?

To explore this, I’ve conducted what I’ll call a methodological experiment.

Rather than relying on traditional datasets, I’ve used ChatGPT and Claude to construct a synthetic index of political violence in the US since 1945. Let me be absolutely clear: this isn’t conventional data. It’s data generated through language models, with all the limitations that implies.

The Methodology (and Its Limitations)

Here’s what I did: I asked both ChatGPT and Claude to generate lists of politically motivated violent incidents since 1945, then had them score each incident’s severity on a scale where 50 represents a “normal” level.

The models assessed both casualties and symbolic significance, and I used them to cross-check each other’s work. I then quality-checked the output myself and categorised perpetrators by political affiliation where this was clearly established.

This approach is, admittedly, unorthodox. Language models are trained on existing texts and may reflect biases in their training data. They might overweight highly publicised events or recent incidents that featured prominently in their training corpus.

The “data” we’re looking at is essentially a structured synthesis of what these models have absorbed about American political violence.

Yet there’s something intriguing here. These models have processed vast amounts of information about political violence – news reports, academic studies, government documents. Their output might capture patterns that traditional datasets miss, though it might also amplify certain narratives or blind spots.

What the Synthetic Data Reveal

With those caveats firmly in mind, the patterns that emerge from this exercise are concerning. The model-generated index shows a clear upward trend in political violence over the past decade.

Looking at the breakdown by perpetrator ideology (where clearly established), the data suggest that right-wing extremist groups have been responsible for the majority of incidents in recent years, though we cannot draw conclusions about today’s attack whilst investigations are ongoing.

The synthetic data align with some empirical observations. Princeton’s Bridging Divides Initiative recorded over 600 incidents of threats and harassment against local officials in 2024 – a 74% increase from 2022. The University of Maryland found that in the first half of 2025, 35% of violent events targeted U.S. government personnel or facilities – more than twice the rate in 2024.

The Charlie Kirk Assassination and Recent Patterns

The September assassination of conservative activist Charlie Kirk marked a particularly dark moment.

The incident followed numerous recent acts of political violence, including the murder of Minnesota Democratic state Rep. Melissa Hortman and her husband, and two assassination attempts on President Trump in 2024.

What the synthetic data reveal is not just increased frequency but a shift in patterns. While overall levels of physical political violence remained low in 2024 compared to years prior, acts of vigilante violence grew as a proportion of all reported incidents.

We’re seeing less organised group violence and more lone-wolf attacks – a pattern that’s harder to predict and prevent.

The Epistemological Challenge

When we use language models to generate “data” about social phenomena, what exactly are we measuring? We’re essentially extracting structured information from the collective corpus of human writing about these events. It’s aggregating distributed information, but through an AI intermediary rather than traditional data collection methods.

This raises fascinating questions.

The models suggest that right-wing extremist violence has been responsible for a fairly large majority of U.S. domestic terrorism deaths since 2001. But how much of this reflects actual patterns versus the way these events are covered and discussed in the sources the models were trained on?

The synthetic data are, in a sense, a mirror of our collective discourse about political violence. They reflect not just what happened, but how we’ve talked about what happened. That’s both a limitation and, potentially, a feature – understanding the narrative landscape around political violence might be as important as counting incidents.

An Experimental Tool

I’ve built an interactive app (using the AI coding tool Lovable) based on this language model-generated violence index.

Users can explore the synthetic data, examine patterns across different time periods and perpetrator groups, and understand the methodology behind it. Think of it as an experiment in using AI to structure historical information rather than a definitive dataset.

The value isn’t in treating this as gospel truth, but in what it reveals about how these events are recorded, remembered, and synthesised in our collective digital memory.

When language models trained on our civilisation’s text output show rising political violence, it tells us something – even if that something is as much about narrative as about underlying reality.

This morning’s tragedy in Dallas reminds us that behind every data point – whether traditionally collected or AI-generated – there are real victims and real consequences. Understanding the patterns, however imperfectly, is the first step toward addressing them.

Try the tool here.





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