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This is a fascinating explotation of how LLMs fall for prompt injection attacks. It turns out that they learn to recognize the style of text in different role/instruction blocks, and not just the tags.

Their conclusion:

Role tags were a formatting trick that became the security architecture and the cognitive scaffolding of modern LLMs. We’ve shown that this architecture doesn’t survive into the model’s actual representations, and that such role confusion is linked to prompt injection.

Unless LLMs achieve genuine role perception, we think injection defense will remain a perpetual whack-a-mole game. And the continuous nature of role boundaries opens the threat of injections designed to subtly shift LLM states through seemingly innocuous text, legally and at scale.

More generally, roles are quietly one of the most important abstractions in the LLM stack, providing the boundaries meant to separate self from other, thought from communication, instruction from data. They’re human-controlled switches in an otherwise continuous system. We think they deserve a lot more study than they’ve gotten.

Full paper: “Prompt Injection as Role Confusion.” Simon Willison comments.

Fable 5 is the supposed safe version of Anthropic’s Mythos Preview, with guardrails to ensure that it can’t be used to create cyberattacks.

Well, that restriction was bypassed within days.

Two men pleaded guilty in the United Kingdom this week to criminal charges stemming from an August 2024 cyberattack that crippled Transport for London, the entity responsible for the public transport network in the Greater London area. The duo were key members of a prolific cybercrime group known as Scattered Spider, and their guilty pleas came on the first day of what was expected to be a six-week trial.

Thalha Jubair, 20, of East London and 18-year-old Owen Flowers of Walsall admitted conspiring to commit unauthorized acts against Transport for London computer systems and causing risk of serious damage to human welfare. According to a report from the BBC, Flowers alone admitted to being part of a conspiracy to hack into U.S. based healthcare providers SSM Health Care Corporation and Sutter Health in September 2024.

Jubair is also wanted by U.S. law enforcement agencies. In September 2025, prosecutors in New Jersey unsealed an indictment alleging Jubair and other Scattered Spider members committed computer fraud, wire fraud, and money laundering in relation to 120 computer network intrusions involving 47 U.S. entities between May 2022 and September 2025, and that the group’s victims paid at least $115 million in ransom payments.

In July 2025, KrebsOnSecurity reported that Flowers and Jubair were arrested in the United Kingdom in connection with Scattered Spider ransom attacks against the retailers Marks & Spencer and Harrods, and the British food retailer Co-op Group. Multiple sources familiar with those investigations said Flowers was the Scattered Spider member who anonymously gave interviews to the media in the days after the group’s September 2023 ransomware attacks disrupted operations at Las Vegas casinos operated by MGM Resorts and Caesars Entertainment.

According to prosecutors, Jubair co-ran a bustling Telegram channel called Star Chat, the home of a SIM-swapping group that used voice- and SMS-based phishing attacks to steal credentials from employees at the major wireless providers in the U.S. and U.K. The group would then use that access to sell a service that could redirect a target’s phone number to a device the attackers controlled and intercept the victim’s calls and text messages (including one-time codes for multi-factor authentication).

New Jersey prosecutors also allege Jubair also was involved in a mass SMS phishing campaign during the summer of 2022 that stole single sign-on credentials from employees at hundreds of companies. That weeks-long SMS phishing campaign led to intrusions and data thefts at more than 130 organizations, including LastPass, DoorDash, Mailchimp, Plex and Signal.

KrebsOnSecurity reported last year that one of Jubair’s alter egos at age 15 was “Everlynn,” a hacker who sold fraudulent “emergency data requests” that used compromised police and government email addresses to demand subscriber data (e.g. username, IP/email address) from major tech companies, claiming the requests concerned urgent matters of life and death and could not wait for a court order.

In April 2026, 24-year-old British national and Scattered Spider member Tyler “Tylerb” Buchanan pleaded guilty to wire fraud conspiracy and aggravated identity theft for participating in the group’s SMS phishing spree in the summer of 2022. The government said Buchanan, Jubair and others used the credentials harvested in that phishing campaign to steal at least $8 million in cryptocurrency from victims throughout the United States. Buchanan is currently scheduled to be sentenced on October 2.

In August 2025, 20-year-old Scattered Spider member from Florida named Noah Michael Urban was sentenced to 10 years in federal prison and ordered to pay $13 million in restitution, after pleading guilty to charges of wire fraud and conspiracy.

The U.S. Department of Justice says three alleged Scattered Spider defendants indicted along with Buchanan still face charges, including Ahmed Hossam Eldin Elbadawy, 24, a.k.a. “AD,” of College Station, Texas; Evans Onyeaka Osiebo, 21, of Dallas, Texas; and Joel Martin Evans, 26, a.k.a. “joeleoli,” of Jacksonville, North Carolina.

Flowers and Jubair are slated to be sentenced in a London court on July 15, 2026.

I haven’t thought about the privacy issues surrounding professional athletes and wearables.

Wearables present serious privacy issues for “Average Joe” consumers, who are entrusting tech companies to safely store and protect their biometric data. Imagine the stakes for a professional athlete, whose entire livelihood could be affected by a single biometric data point. To give one of many realistic hypotheticals: a basketball player has a terrible game, and the coach wonders if they showed up to the gym hungover. The coach has access to the player’s wearable data, and checks to see when they went to sleep, as well as what their heart rate looked like during the night. Should the player have been out partying before a game? No. Should the coach be able to surveil them? Definitely not.

It will not surprise you to learn that there’s an emergent gambling angle here: sports leagues would love to commercialize players’ biometric data, and sharp bettors would love access to data about, say, a hungover player. “We’re going to get to a spot where people are betting not just on the velocity of the puck that was shot by a player in the NHL playoffs, but on what the heart rate of a certain player is going to be running down the field,” said Helen “Nellie” Drew, the director of the University of Buffalo’s Center for the Advancement of Sport, and a professor of practice in sports law.

There are other practical considerations, too. What if wearable data reveals that a player isn’t as speedy as they were before, and a team uses that data against the player during contract negotiations? What if a wearable reveals a player is favoring their leg, or is at greater risk of injury? This information is potentially beneficial to a training staff and an athlete, so long as it’s disclosed and used in a responsible manner—­a critical, mostly unresolved caveat. “Aging and injured players are the most at-risk” of wearable data being used against them, said Michael LeRoy, who researches sports labor laws and AI, and is a professor at the University of Illinois’s School of Labor and Employment Relations.

The bit about gamblers is particularly scary.

I have often said that surveillance tech is generally deployed first against people with diminished rights: children, prisoners, military personnel, the mentally impaired. This is another early use case with different dynamics. The surveilled are wealthy and powerful, and—in many cases—unionized.

Dolphins, sharks, turtles, and human workers are all victims of unregulated squid fishing fleets.

Another news article.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

On June 9th, Anthropic released its Fable generative AI model. Three days later, the US government classified it as a dangerous munition, and used its export-control authority to prohibit any foreign nationals from accessing it. Unable to differentiate between Americans and foreigners, the company shut off access for everyone.

The government’s actions won’t help. The problem isn’t any one particular model; it’s the general trend of increasing AI capabilities. And any real solution requires the sort of collective action that just isn’t possible right now.

Fable is the constrained version of Mythos, the AI model Anthropic announced in April. Anthropic only released it to a few selected organizations, because the company claimed it was so good at finding and exploiting vulnerabilities in computer code that releasing it more generally would be dangerous.

It was an obviously self-serving announcement, and because few were able to verify Anthropic’s claims they were met with some skepticism. Those with access used Mythos to find and patch many vulnerabilities in their own software. But one UK group found the latest, already public, OpenAI model to be just as powerful.

Fable is just another incremental improvement in the years-long climb of AI capabilities. But just as important as the AI model is the “harness.” This is typically not AI. It’s ordinary computer code that interfaces with the user. It stitches together AI models, decides how and for what purposes they can be used, and gives them useful tools such as web search and the ability to run their own computer code.

When Mythos first entered limited release, there was widespread debate whether its power came from the model or the harness. With Mythos demonstrating that it was possible, the open-source community scrambled to build harnesses that could steer other AI models towards similar capabilities. Harness improvements don’t need massive data or data centers.

They largely succeeded. For example, a Prague company was able to replicate Anthropic’s few verifiable cybersecurity capabilities with a much smaller and cheaper model—and a more sophisticated harness. Last week, a group showed that multiple cheaper models harnessed in concert matches Fable’s performance.

The broader community had only a few days with Fable, but that time we learned some about its capabilities. Its difference is less the new model’s raw analytical and problem solving capabilities, and more that the model doesn’t need that sophisticated harness.

Fable requires much less expertise and detailed prompting from the human user. You can give it a difficult goal and it will figure out novel and unexpected ways to satisfy it, finding loopholes in whatever constraints you or the system have imposed on it.

“Relentlessly proactive” is how AI researcher Simon Willison described it. Another descriptor might be “creative.” Experienced AI developers have had that combination of creativity and proactivity since last year, but Fable puts it within easy reach of everyone.

In the hands of someone with a legitimate problem that needs solving, that can be an incredibly useful capability. But in the hands of someone who wants to do harm, it can be equally dangerous. AIs don’t have a moral compass in the same way that people do. They are agents of the wants and desires of the people who prompt them.

That points to the real problem with relentlessly proactive AI. In language, wants and desires are always underspecified. If I ask you to get me some coffee, you would probably pour me a cup from the coffeepot, or buy one from a nearby coffee shop.

You couldn’t buy me a pound of raw beans, or a coffee plantation. You wouldn’t order a cup of coffee for delivery next month. You wouldn’t find a nearby person, rip a cup of coffee out of their hands, and bring it to me. I wouldn’t have to specify any of the million limitations to my request; you would just know.

Human stories are filled with warnings about underspecified desires. King Midas wished that everything he touch turn to gold, forgetting to add “but not my food, drink, and daughter.” And genies are notorious for granting your wish in a way you wish they hadn’t.

The deeper point is that it’s impossible to list all limitations and restrictions, and like a malicious genie, a creative AI will find the ones you forgot. Block a database you don’t want it to have access to, and it might figure out how to bypass your control. Ask it to book a flight, and it might hack the airline because the website says the flight is sold out. Ask it to save money on your cellphone plan, and it might cancel it altogether—or get someone else to pay for it. As far as we know now AI has not done any of this yet, but you get the idea.

Malicious intent is not required. To an AI model, constraints are just things to get around and not general truisms about the world. They are creative problem solvers and natural rule breakers. They “hack” in the sense that they find and exploit loopholes.

Human systems rely on so many norms that we scarcely recognize the existence of until they are broken. AIs naturally think outside the box, because they don’t have any real conception of what the box is or why it’s there in the first place.

There is no foolproof way to prevent people from using AI models to complete harmful tasks. There is no way to prevent the models from incidentally causing harm while completing benign tasks. AI models are no longer isolated from the real world. They browse the internet and answer emails.

They trade stocks and make purchases. They control physical systems. They are, in effect, robots that affect life and property. We have no technical mechanisms to verify the integrity of an AI system. This level of capability and creativity in the hands of us untrustworthy humans will have both great and terrible results.

The problem is not unique to Anthropic. Mythos/Fable might currently be the most capable rules hacker, but more sophisticated harnesses give other models similar capabilities. And we should assume that the other frontier models are no more than a few months behind, and that open-source models are less than a year behind. At best, any ban only serves to delay the problem for a short while.

That delay might be useful if we—as a society, as a planet—would use that time to come together and figure out what to do. This isn’t a US/China arms race problem; this a species-level problem that requires coordinated action at that scale. Unfortunately, we have no mechanism to do that. I first wrote about this problem five years ago, but it was all too futuristic.

Today, when its right in front of us, there is no world government that can impose constraints on the for-profit corporations currently controlling AI models and research. The US has no appetite to effectively and even-handedly regulate those corporations, even as they do catastrophic damage to the environment, democracy, and—in this case—society in general.

This all makes an AI public option all the more necessary, and urgent. Today’s AIs can be fast, smart and secure, but only two of the three are possible for any given system. These safety tradeoffs are tightly held secrets of companies racing to beat one another, and they tell us we have to trust them. Instead, the choices and their consequences need to be brought out into the sunlight.

We should be funding open-source harnesses that balance capability and safety—that achieve useful goals without so much power—and open-source AI models whose provenance and biases are public and well understood. We have opened the AI Pandora’s box. Now we have to make the best of it.

This essay originally appeared in The Guardian.

At least one malware developer is adding text about nuclear and biological weapons to their spyware, in an effort to stop automatic AI analysis.

Details:

The _index.js payload begins with a large JavaScript block comment containing fake system instructions and policy-triggering content. Because it is inside a comment, it does not affect JavaScript execution. The runtime skips it. The real malware begins after the comment with a try{eval(…)} wrapper around a large character-code array and a ROT-style substitution function.

This header appears designed for AI-mediated analysis, not for Node, Bun, or Python. It attempts to derail scanners or analyst copilots that feed the beginning of a file to a language model without clearly isolating the content as untrusted data. In weak pipelines, this can cause refusal behavior, prompt confusion, context pollution, or premature classification before the scanner reaches the actual malware.

This is not a magical bypass against static detection. YARA rules, entropy checks, AST parsing, string extraction, deobfuscation, and behavioral rules still work. But it is a practical anti-analysis trick against naive LLM-first triage systems.

For the past four years, a sprawling Android-based botnet called Popa has forced millions of consumer TV boxes to relay Internet traffic linked to advertising fraud, account takeovers, and mass data-scraping efforts. This week, researchers from multiple security firms concluded that the Popa botnet is linked to NetNut, a “residential proxy” provider operated by the publicly-traded Israeli firm Alarum Technologies Ltd [NASDAQ: ALAR].

Popa is a massive botnet, but by all accounts it is unlike traditional botnets that enlist compromised systems in destructive activities, such as coordinating huge distributed denial-of-service attacks. Rather, Popa appears designed with a singular purpose: Implementing a persistent communications layer capable of registering a device, maintaining long-lived encrypted connections, and opening communication tunnels on demand.

Experts say Popa is a plugin component associated with the Vo1d botnet, a large-scale malware campaign targeting unofficial Android-based TV boxes. These devices, which are marketed under thousands of brand names and model numbers and broadly available for purchase at top e-commerce destinations, all advertise the ability to stream hundreds of subscription video services for an up front one-time fee.

But as the FBI and security industry experts have warned repeatedly, these streaming boxes typically bundle or come pre-installed with software that turns the user’s TV into a “residential proxy” — allowing anyone to route their Internet traffic through that device for as long as it remains plugged into a wall socket and connected to a local network. More concerning, some of these proxy networks do little to stop malicious customers from communicating with and even compromising systems on the local network of the unsuspecting device owner.

The first clues about Popa’s origins came in a 2025 report from the Chinese security company XLAB, which flagged at least nine domain names that were used to register and direct the activities of compromised devices. In a report released today, the security firm Qurium described how it stumbled on some of those same domains while investigating a series of disruptive and expensive data scraping events targeting the company’s hosted organizations in May 2026, in which the scraping activity was scattered evenly across more than 1.4 million Internet addresses.

Qurium said it found several dozen domains used to control Popa that were all hosted in lockstep across multiple Internet addresses over time, including gmslb[.]net, safernetwork[.]io, tera-home[.]com, and ninjatech[.]io. Digging deeper, Qurium discovered gmslb[.]net was referenced in dozens of pirated or modded video content streaming apps, such as CRICFy, DooFlix, Sprozfy, RTS Tv, Flixoid, CyberFlix, Rapid Streamz, TvMob and HD/OceanStreams.

Qurium’s report notes that most of the domains long used to control the Popa botnet were seized or dismantled in July 2025, after Google, HUMAN Security and Trend Micro teamed up to disrupt Badbox 2.0, a botnet that is closely associated with Vo1d. Qurium said that immediately after that disruption, several dozen new domains were registered to serve as controllers for the Popa botnet, but that one of those control domains was not new: ninjatech[.]io.

Ninjatech is a company founded by Moishi Kramer, whose LinkedIn profile says he is vice president of research and development at NetNut. That resume credits Kramer for helping NetNut to build from the “ground up,” “designing the architecture,” and “scaling the NetNut” before the company was acquired by Alarum Technologies. A self-created listing at the job board F6S references Kramer as the sole owner of the Ninjatech domain (a screen capture of it is pictured below).

Responding via email, Mr. Kramer said Ninjatech ceased operations approximately five years ago, when the company sold a software development kit (SDK) called Popa that was designed to use a small portion of a device’s bandwidth and to run only after the host application obtained user consent.

“That code was sold and licensed to third parties including resellers years ago,” Kramer said. “Once software is distributed that way, the original developer has no control over how others later modify, rebrand, or deploy it.”

Kramer said neither he nor NetNut builds, operates or maintains the infrastructure being described as Popa, nor does he control the Ninjatech domain.

“I didn’t register the June 2025 domains you mention, and I don’t know who did,” he continued. “I have no control over, or visibility into, that infrastructure. I can only tell you it isn’t operated by me or by NetNut.”

But in a separate Popa research report released today, the proxy-tracking company Synthient said a recent analysis of the Popa SDK revealed outbound traffic clearly associated with NetNut.

“The research team assesses with high confidence that devices running Popa forward traffic from Netnut clients,” Synthient wrote. “This proves without a shadow of a doubt that Popa actively continues to be used by NetNut as part of their proxy pool.”

Alarum Technologies, NetNut’s Tel Aviv-based parent company, said the reports by Synthient and Qurium contained “demonstrably inaccurate assertions and flawed deductions rather than verified facts.” Alarum shared a statement saying they reject the basic characterization of the SDKs and technologies discussed in the reports as a “botnet.”

“The SDKs at issue are designed to facilitate bandwidth-sharing functionality and do not transform user devices into malware-controlled systems or otherwise compromise the devices on which they operate,” the statement reads. “Netnut operates a commercial proxy network and maintains policies, procedures, and technological measures designed to promote lawful and responsible use of its services.”

Alarum said NetNut places “significant emphasis on appropriate notice and consent mechanisms, conducts customer due diligence, monitors for potential misuse, and takes steps intended to detect and mitigate suspicious or unauthorized activity.”

“This method of operation is supported both by internal procedures and policies, including performing KYC checks and additional due diligence of NetNut’s customers, as well as employing various technological measures, designed to assist in identifying and addressing suspected misuse of the network,” their statement continued.

However, in a report released on June 8, the proxy tracking service Spur asserted that NetNut does not require corporate verification or meaningful “know your customer” procedures before allowing customers to purchase proxy access.

“An individual can sign up, pay, and route traffic through partner address space, including space belonging to institutions whose users never opted in,” Spur wrote. “The ‘verified corporations only’ claim is simply marketing for bandwidth sellers, not an access control on who actually uses the proxies.”

“Nor is NetNut the only front door,” Spur continued. “A number of downstream white labelers and resellers repackage the same ISP proxy pool under their own brands. These outlets typically perform no KYC at all, less scrutiny than NetNut itself, who at the very least might assign an account manager to potential users. Anyone who knows where to look can buy access through a reseller with nothing more than a burner email address and $5 in crypto.”

Synthient found that although the most recent builds of Popa (as of three months ago) have added the ability to ask the user for consent before installing proxy components, not all variants or previous versions of Popa contain this functionality.

“Of the over 20 genuine Popa publishers analyzed, none of them were observed asking for user consent,” Sythient wrote.

THE PREVALENCE OF POPA

Chris Formosa is senior lead information security engineer for Black Lotus Labs, a division of the Internet backbone carrier Lumen Technologies.

“What especially makes Popa dangerous is just how widely used NetNut is for reselling and sharing,” Formosa said, explaining that many other proxy services simply resell NetNut proxies rather than building out their own far-flung proxy networks. “So these Popa IPs appear in tons of different services all over the ecosystem, which makes it one of the most problematic and dangerous proxy botnets on the market currently.”

Formosa said the Popa botnet averages between 1.5 million to 2.5 million distinct IP addresses each day, relying on between 250 and 300 Internet addresses that are used to direct its activities.

“That’s why Popa is so dangerous,” Formosa said. “It may not be the largest botnet we have seen, but it is spread all over the industry, making its power very amplified.”

Formosa said while that makes Popa one of the larger botnets out there today, its numbers pale in comparison to those previously boasted by IPIDEA, a China-based proxy provider that until recently operated a daily pool of nearly 10 million devices that they resold as proxies to anyone. In January 2026, Synthient published research showing that multiple new large DDoS botnets had grown rapidly by tunneling through IPIDEA proxies into the local networks of unsuspecting TV box owners and infecting other Android-based devices behind the user’s firewall.

IPIDEA is based largely on SDKs used to view pirated streaming content on a vast number of TV box devices, but the service’s numbers have dwindled since January, when Google and industry partners took legal action to seize domain names that IPIDEA used to control devices and proxy traffic through them.

Jérôme Meyer, a security researcher at Nokia Deepfield, said the total population of devices participating in the Popa botnet may be far higher than Lumen’s estimates. Meyer told KrebsOnSecurity that Nokia is monitoring 26 of at least 359 known relay nodes for the botnet, and estimates that each relay node handles between 35,000 and 60,000 clients simultaneously.

“On the relay node subset I am looking at (26 of them), 750,000 unique sources in 24 hours,” Meyer wrote in response to questions.

Nokia Deepfield released its own report today on RoboVPN, a VPN app tied to the Vo1d botnet’s Popa plugin that Qurium attributes to NetNut/Alarum Technologies.

THE SYMBIOSIS OF PROXIES AND DATA SCRAPING

Experts say many of the world’s largest proxy providers have updated their public-facing branding to highlight their utility for training AI platforms, implying it is a primary use case for their residential proxies. That’s because AI services tend to rely on constantly mass-scraping the Internet for new text, images and video content that can be used to train large language models (LLMs).

“AI companies depend on web-scraped content: for pre-training, for retrieval, for agent grounding, for search,” reads a report this month from Include Security that examines the prevalence of proxy SDKs in smart TV apps. “But the modern web isn’t scrapeable from a datacenter. Cloudflare, DataDome, HUMAN, among others throttle or block requests from known cloud IPs. The workaround is residential proxies. A scraping job routed through a Comcast or T-Mobile subscriber’s connection arrives at the target site from an IP that belongs to a paying residential customer.”

This non-stop content scraping has spawned more than 70 copyright infringement lawsuits against major tech companies that have acknowledged large-scale data scraping as a major source of the “brains” behind their commercial AI offerings. Ironically, much of that scraping is being aided by proxy services that are intimately tied to unofficial Android TV boxes and associated SDKs whose stated purpose is streaming pirated content.

The scraping activity has become so aggressive that it often overwhelms the targeted websites, preventing them from being reachable by legitimate visitors. In many reported cases, nonprofit organizations, libraries and universities have complained of constantly battling to keep their services online in the face of relentless data-scraping firms hiding behind residential proxy services.

A survey conducted last year by the Confederation of Open Access Repositories (COAR) found while some content scraping bots are rather innocuous, “others are sufficiently aggressive that they are increasingly causing service disruptions in repositories and other scholarly communications infrastructures.” More than 90 percent of survey respondents indicated their repository is encountering aggressive bots, usually more than once a week, and often leading to slow downs and service outages.

“Automated web scraping is nothing new, and has been the key technology underlying search engines such as Google for over 30 years,” wrote Brendan O’Connell, platform manager at the Directory of Open Access Journals (DOAJ), a free, community-curated index of peer-reviewed academic journals. “However, the current investor-fueled AI startup craze means there are now thousands of well-funded companies developing and deploying their own scraping tools to train AI models, alongside existing major players like OpenAI and Google.”

DON’T TOUCH THAT DIAL!

Across the United States, local communities are pushing back against the proliferation of new data centers aimed primarily at improving the capabilities of AI. But security experts say the general public remains largely unaware that using one of these unsanctioned Android TV boxes means their “smart TV” is almost certainly using a significant amount of bandwidth each month to help train modern AI models.

Even households without these sketchy TV boxes can still have their smart TVs turned into residential proxy nodes, just by downloading one of thousands of apps made available on Samsung and LG smart TVs. Spur said it recently scraped the LG and Samsung app stores and found that each had approximately 3,000 apps available for download. Many of these apps are simple games or utilities that state in the fine print that the user’s Internet connection will be used to download data and that they can opt out at any time.

Spur said it found that more than 42 percent of apps available for download via the webOS operating system on LG smart TVs include SDKs that turn one’s television into an always-on residential proxy node. More than a quarter of the apps made for Samsung’s Tizen operating system had similar residential proxy components, Spur found.

Experts say it’s questionable whether TV apps with proxy SDKs can obtain meaningful consent from users for installing an always-on proxy connection, particularly when anyone in a household — including children — can effectively opt the family TV into a residential proxy network just by installing a simple game or app.

“Privacy-policy disclosure is the wrong control surface for a TV,” Include Security wrote. “It is hard to scroll through a legal document navigated by arrow keys on a remote, and the in-app consent dialog doesn’t convey that a paying customer is about to route their scraping traffic through the user’s home internet.”

Spur’s head of research Sean Simmons told KrebsOnSecurity that most people do not have a working mental model for what it means to sell access to their residential IP address, no matter what device they are using.

“And on a TV, the gap is even wider,” Simmons said. “A one-time prompt navigated with a remote can disappear into the setup flow, while the app keeps monetizing the connection long after anyone remembers what they accepted.”

Simmons said LG and Samsung should follow the lead of other TV platforms that have already drawn a line against residential proxy providers, pointing to policies by Amazon that prohibit apps facilitating proxy services for third parties. Likewise the TV streaming device maker Roku reportedly now bars developers from using proxy SDKs and has removed apps that bundled them.

Apps that turn one’s device into a residential proxy node are not limited to smart TVs and no-name streaming boxes, of course. As noted by the security firm Infoblox, mobile app developers can embed SDKs provided by the residential proxy networks into their products to monetize their software, allowing them to receive a small amount of money on each installation.

The result, Infoblox said, is that devices are frequently enrolled without the owner’s knowledge, typically through free applications such as VPNs, streaming apps, screensavers and “productivity” apps such as PDF viewers and break reminders.

All too often, these proxy services are beaconing out from employee devices brought into the workplace, Infoblox found. In a blog post earlier this month, Infoblox said it discovered that fully 65% of its customer base was querying one or more residential proxy related domains.

“We saw steady growth in these queries in 2025, with a 25% increase over the year to over 500 billion per month,” Infoblox wrote. “Over 90% of our pharmaceutical and food & beverage customers have queried residential proxy indicators. Perhaps even more concerning is that over 60% of government and banking customers have as well.”

Infoblox researchers Nick Sundvall and David Brunsdon warned that with residential proxies in the corporate environment, external access is granted to an organization’s IP space.

“If threat actors were to abuse the residential proxy to attack a third party, the third party’s incident response would, correctly, identify your residential proxy as the source,” they wrote. “Untangling that, by proving that you were the conduit and not the threat actor, costs time, creates legal exposure, and can damage your reputation. The stunning prevalence of these services within customer environments warrants attention from both network defenders and policy makers who should consider how the risks posed by residential proxies could be impacting their security posture.”

On 14 April, the Trump administration quietly acknowledged the widespread use of AI to automate government processes. The office of management and budget (OMB) disclosed a staggering 3,611 active or planned use cases for AI across the federal government. The list has ballooned by 70% from the one published in the final year of the Biden administration, and includes many disturbing-seeming plans to hand over sensitive governmental functions to AI.

Scanning this list, many readers may find many causes for alarm. It represents a transfer of decision processes from human to machine on a massive scale over matters of individual freedom, public health and well-being, nuclear reactor safety and more.

Consider these examples. The Health and Human Services’ (HHS) office of administration for children and families hired the world’s “scariest AI company,” Palantir—notorious for its work on behalf of the military, the CIA and ICE—to scan all grant applications to flag those not ideologically aligned with the administration’s dictates. The Federal Bureau of Prisons is developing an AI system to assess the “potential for misconduct for newly admitted inmates,” routing people into high-security confinement before they have actually done anything wrong in their custody. These read like programs fit for a Philip K Dick or George Orwell novel.

Other use cases insert AI into life-and-death decision making. The Department of Veterans Affairs is developing an AI that will listen in on calls to the veterans crisis line, and then gather information from external databases to assess the mental state and suicide risk of the caller.

The Department of Energy is testing the use of AI to control nuclear reactors, targeting a way to autonomously respond to potential nuclear safety incidents. Here’s one that’s disturbing for its retirement, rather than its deployment: the state department has ended a program to use AI to forecast mass civilian killings, which had been intended to aid conflict prevention.

While it’s easy to raise questions about these and similar uses of AI, the reality is that any of these programs could be implemented responsibly. In some cases, like the HHS system, the AI might be enforcing alignment to a policy prescription that opponents abhor. But that concern is more about the policy itself rather than the idea that agencies should comply with executive orders.

In other cases, there may even be bipartisan agreement on the goal, like taking urgent action to help veterans at risk of self-harm. Lots of work and validation is needed to prove AI safe and effective for these use cases and convince the public it is appropriate, but the idea is plausible.

In other cases, a scary-sounding AI use may not even be new. The use of predictive methods and statistics to assign prisoner security classifications goes back decades, even if such systems are often biased and ineffective.

Using autonomous systems for model predictive control (MPC) of nuclear reactors is a well studied, and a widely applied aspect of nuclear plant management. And the recently disclosed addition of AI was initiated under the Biden administration.

But anyone reviewing the 2025 inventory could be forgiven for leaping to severe conclusions. What matters are the details of how the AI system is used, and here the inventory is severely lacking.

The disclosures carry minimal information, and lack the context necessary to understand their purpose and approach. The descriptions are typically just a sentence, and rarely more than a paragraph.

And while the process theoretically involves some form of public consultation, in reality there is generally none. It would take an eagle-eyed citizen to even come across this disclosure. Unless you read FedScoop regularly, or watch the OMB’s federal chief information officer’s GitHub account, you probably missed it.

Only one of the examples cited above (the DoJ) even proposes to involve the public. Under the administration’s policy, it’s not required for the rest because they are not classified as “high impact” use cases—a label that is applied inconsistently across agencies.

We wrote a book surveying applications of AI to democratic processes worldwide, including executive agencies as well as the courts, legislatures and politics. Our conclusion was that, while there are inappropriate applications of AI in governance that should be resisted, an urgent need to reform the economics of AI, and an imperative for renovating the democratic systems it is being unleashed on, there are also valuable and beneficial use cases for AI in government.

Machine translation is a good example. Customs and Border Protection (CBP) has deployed an AI translation system to help officers when human interpreters are not available. The idea that CBP, an agency under heavy scrutiny for reported abuses of human rights, would direct people to talk to a machine instead of a person may strike many as inhumane.

It’s true that human interpreters have very real advantages when it comes to understanding nuance from physical cues and social context. But an officer with a competent AI translator available immediately is better than one who cannot communicate with the person in front of them.

The Trump administration’s AI use case inventory has 70 such translation use cases, up from 58 in the Biden administration’s 2024 disclosure.

Disclosure of AI use cases could be a means to build public confidence and trust, but only if paired with consistent, meaningful public consultation. Washington DC and California are actively engaging the public to determine where and how it’s appropriate to use AI in government processes, or for government to regulate AI use in society.

Both have held public deliberations on this topic at a wide scale, using AI platforms. These examples demonstrate the potential for capturing broad-based public input to steer AI policy.

The international gold standard was arguably set by the French in 2016, via their Digital Republic Act. The law, itself informed by an online citizen consultation, requires all algorithms used to automate government administrative decisions to be subject to public records requests, to be appealable to a human reviewer, and to have mandatory notification of the use of automation to those affected by the decisions.

Canada offers another example of what more rigorous and participatory disclosure might look like. In 2025, they launched an AI use case registry, not unlike the US inventory. However, Canada also has a federal directive mandating a transparent risk-scoring and impact assessment process for automated systems that make administrative decisions about citizens.

That longstanding directive requires a detailed explanation of risks and benefits as well as consultation with certain stakeholders from the conception of the AI use case. The Canadian system could be improved; it could require a public comment period and an obligation for agencies to respond substantively to feedback before engaging in sensitive uses of AI.

AI offers real potential to improve the efficacy, efficiency and accessibility of government. But, equally, there is legitimate reason for public concern and distrust that can only be addressed through transparency and dialog. The US should adopt, at the federal and state level, algorithmic impact risk assessment procedures and public comment processes to facilitate a safe, trusted, equitable transformation of government agencies to take advantage of modern technology.

This essay was written with Nathan E. Sanders, and originally appeared in The Guardian.

There are over a dozen cases around the country where police officers are using the Flock surveillance camera system to obsessively and illegally stalk people.

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