Bruce Schneier:

NSO Group Hacking WhatsApp Despite Court Order
WhatsApp has caught the NSO Group phishing its users, in violation of a court order.

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Bruce Schneier:

Enhanced License Plate Tracking
The surveillance company Leonardo wants more data:A surveillance company plans to add sensors to automatic license plate readers (ALPRs) that would mean the devices, as well as capture the license plate of passing vehicles, would also sweep up unique identifiers of mobile phones, wearables, and other Bluetooth-enabled devices in those cars, potentially letting law enforcement identify specific drivers or passengers.The technology, called SignalTrace, would turn ALPR cameras from devices focused on tracking cars to ones that can more readily track the location of particular people. ALPR cameras have become a commonly deployed technology all across the U.S.; SignalTrace would make some of those cameras capable of collecting much more data.Yes, it’s bad that more companies are collecting this level of surveillance data. But all of this pales in comparison to the type and quantity of data our smartphones already collect about us.Alternate link.

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Bruce Schneier:

Bernie Sanders’ AI Sovereign Wealth Fund Plan
Let no one accuse Bernie Sanders of ducking the big questions. Writing in the New York Times last week, the senator asked: “Will the future of humanity be determined by a handful of billionaires who have promoted and developed AI, with virtually no democratic input, who stand to become even richer and more powerful than they are today?”We agree entirely that this is one of the most potent questions facing global democracy today. Our book, Rewiring Democracy, surveys the emerging uses for and impacts of AI in democracy around the world and reaches the same conclusion: that the most urgent risk posed by AI is the concentration of power, wealth and control among tech oligarchs.And yet we reached a vastly different conclusion than Sanders on what to do about it.The senator points to a once radical but increasingly popular solution: creating a US sovereign wealth fund by taking 50% stock in AI companies such as Anthropic, OpenAI and xAI. The argument in favor of this is twofold. One: it would establish democratic control over the AI companies, giving the government “the power, through its voting shares and an equal representation on each company’s board, to block decisions that hurt our citizens and to push for policies that help them”. Two: it would return a big chunk of the economic rewards of soaring AI valuations to the public, ensuring “trillions of dollars potentially generated by AI are used to improve the lives of all of us”.We laud both these goals unreservedly.We wholeheartedly agree that there must be public influence over the development and use of AI, just as we demand the government intervene to ensure that automakers, drugmakers, airlines and other industries balance profitability with public safety and the public interest. And we credit the senator with recognizing that there are more levers for the government to pull beyond the promulgation of regulation to achieve this.And we also agree that the obscene, dangerous accumulation of wealth among AI companies needs to be disrupted. As OpenAI and Anthropic race to be minted as the world’s latest trillion-dollar AI companies, we should recognize that—whether or not it constitutes a bubble—these staggering market capitalizations represent a transfer of wealth. The flow of money goes from the smaller businesses and actual people using AI, and being subjected to it, to the owners of these tech companies.That includes the world’s 86 AI billionaires “seeking to maximize their power and profit” aiming to decide the “fate of humanity … behind closed doors in Silicon Valley”, as Sanders said.And yet, while we do not outright oppose the taking of AI company stock, or of a US sovereign wealth fund, there are better ways to achieve Sanders’ stated goals.Public ownership of these companies entangles corporate profit and valuation with the public interest. It would incentivize the government to clear regulations, permit the exploitation of workers and users, suppress competition, encourage AI adoption regardless of the responsibleness of the implementation or appropriateness of the use case, and otherwise act on behalf of corporate interests.After all, if growing, say, Nvidia from its first $5tn in value to its next $5tn also represents a doubling in value of this segment of the sovereign wealth fund, then you can expect the fund managers to support chip sales, foreign and domestic, with the same zeal as the company’s private investors.This is not an effective way to influence corporations to act in the public interest. In fact, it makes corporate influence on the government more likely.We should be wary of this possibility because we’ve seen it before. Ownership of substantial stakes in oil companies by the Norwegian sovereign wealth fund, the world’s largest, does not seem to have steered those corporations to pro-environmental policies. Instead, the Norwegian government’s dependence on those companies has inhibited them from taking climate action. Here in the US…

Bruce Schneier:

Friday Squid Blogging: Squid-Inspired Fluid Pump
This fluid pump was inspired by the way squids propel themselves through the water.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.

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B. Schneier:

Upcoming Speaking Engagements
This is a current list of where and when I am scheduled to speak:I’m giving a keynote at Cybernation 2026 in Berlin, Germany, on June 24, 2026.I’m speaking at the Potsdam Conference on National Cybersecurity at the Hasso Plattner Institut in Potsdam, Germany. The event runs June 24–25, 2026, and my talk will be the evening of June 24.I’m participating in a panel discussion at the Austrian Institute for International Affairs in Vienna on Thursday, June 25, 2026.I’m speaking at the Digital Humanism Conference in Vienna, Austria, on Friday, June 26, 2026.I’m giving a fireside chat for Epicenter Works, to be held at Kaffee Alt Wien in Vienna, Austria, on Friday, June 26, 2026.I’m participating (via Zoom) in a panel discussion at Quantum.Tech World in Boston, Massachusetts, USA, on Friday, June 26, 2026. The topic is “Q-Day’s Shortening Deadline: Immediate Solutions.”I’m speaking at Czech Technical University in Prague, Czechia, on Monday, June 29, 2026.I’m speaking at the Nuremberg Digital Festival in Nuremburg, Germany, on Wednesday, July 1, 2026.I’m speaking at CanSecWest 2026 in Vancouver, Canada. The conference runs September 30–October 1, 2026; the time of my talk is TBD.The list is maintained on this page.

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Bruce Schneier:

The FCC Wants to Eliminate Burner Phones
A proposed FCC rule would kill burner phones: phones whose accounts are not attached to a particular person.The FCC plans to do this by legally forcing the country’s telecoms to store a wealth of personal information about essentially all phone customers, including a government issued identification number and their physical address, alarming privacy advocates and civil rights activists who compare the measures to those from authoritarian countries where it can be difficult to buy a mobile phone plan without giving up your identity.The proposed change would drastically shake up how people obtain phone plans in the U.S., and have all sorts of privacy and cybersecurity knock-on effects. The FCC is proposing the data collection partly as a way to combat scammers, with telecoms being required to collect other information on business and foreign customers like the intended use case of their bulk phone plan purchase and their IP address. But the changes would mean telecoms collect data on all new and renewing customers, and the FCC provides a long list of other things that the collected data could help authorities with.Alternate link.

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Bruce Schneier:

Flock Cameras Are Being Used for Stalking
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.Alternate link.

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Bruce Schneier:

AI Use by the US Government
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…

Bruce Schneier:

Embedding Forbidden Text in Spyware to Discourage AI Analysis
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.

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Bruce Schneier:

Anthropic’s Fable and the State of AI
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…

Bruce Schneier:

Friday Squid Blogging: Victims of Unregulated Squid Fishing
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.

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Bruce Schneier:

Professional Athletes and Wearables
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.

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Bruce Schneier:

Anthropic’s Fable 5 Model Jailbroken Within Days
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.

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Bruce Schneier:

Embedding Forbidden Text in Spyware to Discourage AI Analysis
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.

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Bruce Schneier:

Interesting Paper Exploring Prompt Injection
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.

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Bruce Schneier:

AI and Liability
Earlier this month, a German court ruled that Google is liable for its AI search summaries. Rejecting defenses like “users can check for themselves,” and that they generally know “that information generated with AI should not be blindly trusted,” the court held that the AI’s summaries are reflections of the company and “above all an expression of Google’s business activities.”This is the latest skirmish in a decades-old battle over internet publishing. Historically, there were two different types of information distributors: carriers and publishers. A phone company is a carrier. It’ll transmit whatever you say, even discussions about committing a crime. Words are words, and the phone company does not know—nor is it liable for—the words you choose to speak. A newspaper, on the other hand, is a publisher. It decides the words it publishes, and what quotes to include in its articles. If those words or quotes are defamatory or otherwise illegal, it’s liable.Internet companies have long tried to play both ends of this distinction. They claim to be a carrier when it suits them, and also to be a publisher when that is advantageous. Section 230 of the 1996 Communication Decency Act enshrined this straddling when it shielded internet providers from liability for the speech of others on their platforms: “No provider or user of an interactive computer service shall be treated as the publisher or speaker of any information provided by another information content provider.”For years, a debate has continued about how to apply this law to social media platforms. When platforms merely displayed people’s posts and comments in reverse-chronological order, they behaved largely like carriers, relaying people’s words without regard to their contents. But the next generation of platforms, like Facebook, curated feeds with algorithms and thereby acted more like publishers, making editorial decisions about who sees what. Some experts think section 230 has gone too far and needs reform; others think that it’s what holds the modern internet together.Google’s AI overviews are far less nuanced. They work differently from traditional search, which courts have held involves archiving and facilitating access to the editorial content of third parties. AI overviews don’t just quote and republish words from different websites. With overviews, the AI rewrites other people’s words, exercising editorial discretion like a newspaper article or an original essay on a topic.It’s not only Google’s AI that falls into this category. Imagine a restaurant review site that provides AI summaries, or a site summarizing laws and government procedures. Or a traditional publisher that uses AI to summarize its own publication. Accuracy matters, and liability is one of the most important ways we as a public can demand accuracy and hold companies accountable when they cause harm.Two years ago, Air Canada learned this lesson. Its AI chatbot promised a discount the company later rescinded, arguing in court that the airline wasn’t responsible for the promises the bot made because it was a “separate legal entity that is responsible for its own actions.” The court sided with the flyer, saying that the airline was just as responsible for what its chatbot says as what’s on its website. The potential precedent here is that corporations have a duty of care for the performance of the AI chatbots they employ.AI agents are agents of the person or organization that deploys them—and should be treated by the law as such. If a company hired human writers to write its summaries, that company would be liable for inaccuracies in those summaries. If a company’s human agent signed contracts in the company’s name, that company would be bound by those contracts. And if a doctor gave dangerously wrong medical advice, they would be liable for malpractice.To allow businesses to hide behind the excuse of faulty AI in those same circumstances would be a massive handout to companies, and would introduce disastrous incentives…

Bruce Schneier:

One Million Passports Leaked Online
A database of almost a million passports from around the world was leaked online.Note what happened. A high-value credential—a passport—was used in an ancillary low-value authentication system: ID verification for cannabis dispensaries. And it’s the low-value system that got hacked, putting the high-value credential at risk.

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Bruce Schneier:

Meta Is Testing Facial Recognition for Police and Military
We know that ICE wants to deploy eyeglasses with facial recognition that can identify people in real time.Turns out Meta is prototyping the feature with a Pentagon supplier. (Alternate news story.)

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Bruce Schneier:

The Chinese Control the Majority of Argentina’s Squid Fleet
Chinese companies control nearly two-thirds of Argentina’s own squid fleet.

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Bruce Schneier:

Robot Police Officers
We’ve taken one small step towards robot police officers: a drone capable of disarming a suspect:In a June 22 video posted on the Sacramento County Sheriff’s Office’s Instagram page, an officer wearing goggles can be seen operating a drone to retrieve a knife from an armed suspect hiding inside a cluttered house. “After not responding to negotiators, a drone was deployed inside the residence,” the post says. “Drone pilots located the suspect hiding in a corner of a garage” and then used a high-powered magnet attached to the drone to grab the knife out of the suspect’s hand. In the video ­ which is soundtracked by the “Mission: Impossible” theme song—the intercepted knife can be seen spinning around in the air as the drone carries it back to the deputies.Slashdot thread.

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Bruce Schneier:

Factoring RSA Keys with Many Zeros
Interesting research on a new class of weak RSA keys: keys with lots of zeros. It turns out that these keys are out in the wild.The badkeys project is an open-source service that checks public keys for known vulnerabilities. While developing this tool, Hanno collected a massive number of real-world keys from public sources, including Certificate Transparency logs, internet-wide TLS and SSH scans, PGP keys, and many others. By searching this dataset for unexpectedly sparse RSA moduli, we uncovered a large number of keys in the wild with the patterns in Figure 1.Both patterns include several regularly spaced blocks of all zeros interleaved with seemingly random data. Pattern 1 appears in CT logs for certificates issued to several large organizations, including Yahoo and Verizon, and on some devices running NetApp software. Fortunately, these certificates have already expired, but we still shared our findings with these companies. We wanted to learn more about which product could be responsible for generating these keys, but we did not hear back. Pattern 2 appears on SSH hosts running the CompleteFTP software from EnterpriseDT. The underlying vulnerability affects RSA keys generated using versions 10.0.0­12.0.0 (Dec 2016­Mar 2019) and DSA keys generated with v10.0.0­23.0.4 (Dec 2016­Dec 2023).These vulnerabilities affect a small minority of hosts on the internet, but the more interesting takeaway is that independent cryptographic implementations failed in similar ways. More implementations may include the same bugs, and so it’s worth tailoring cryptanalytic algorithms for this particular type of failure.The article doesn’t speculate, but I will. This could be a deliberately designed backdoor, of the sort I wrote about back in 2013. I could imagine some government agency figuring out how to break this class of RSA keys, and then convincing different providers to hand them out to users.

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