cryptonews

He’s being prosecuted for giving border officials a code that wiped his phone:

The case centers on a feature included in GrapheneOS, a custom Android operating system that runs in place of the software on most modern Google Pixel devices. Tunick’s attorneys confirmed GrapheneOS was running on his phone.

The software feature allows the device owner to set a passcode that deliberately wipes the contents of that device if entered instead of the user’s unlock passcode.

Tunick’s case also raises ongoing questions about what constitutional rights can be invoked at the border, which the U.S. government has long asserted is not U.S. soil until a person is authorized to enter.

Right. And he wasn’t under arrest, either.

Three more news stories.

Graphine says that the feature is “completely legal“:

GrapheneOS is completely legal. We have no obligation to weaken any of the security protections it provides. Creating and using GrapheneOS is strongly protected by the US constitution. Laws attempting to make it illegal or require weakening the security would be unconstitutional.

It’s hard to know how much the Constitution matters in the US right now.

This essay originally appeared in The Guardian.

I teach public policy at the Harvard Kennedy School and the Munk School at the University of Toronto. And it will come as no surprise to you that my students regularly use AI to complete their writing assignments. Doing so is a waste of their tuition money. But if their entire career is going to include AI writing assistants, why shouldn’t they embrace their future?

The best way I’ve found to explain the dilemma comes from the AI researcher Daniel Meissler: it’s the difference between work and the gym.

At work, if your job is to move a bunch of heavy things from one side of the room to another, you should use whatever assistive tech you have on hand: a wagon, a forklift… even an AI-powered robot. But at the gym, it makes no sense for that robot to lift weights for you. The point of weightlifting isn’t to move heavy things across the room; it’s to actually lift those heavy things.

The same analysis holds for any task an AI can do for you. If it’s work—if the task has to be done and no one cares how—then it’s fine to use AI assistance. But if the task is more like the gym, and how the task is done is at least as important, then it probably doesn’t make sense to use AI.

This, of course, assumes that the AI is actually up for the task and that it’s trustworthy: that it can do the job well, that its mistakes are minimal and correctable, that it’s been secured from cyber-attacks that would influence its results. Those are all important, and shouldn’t be minimized. There’s no point giving an AI something that it can’t do reliably. But once you’re confident that the AI can perform the task, the work vs. gym distinction helps you decide if it should.

The writing assignments I give my students are gym tasks, not work tasks. I ask them to write policy memos not because the world needs more policy memos. I assign them because the very act of writing, which includes thinking and outlining and drafting and editing, making and criticizing and revising arguments, will help develop the critical thinking skills they will need in their future careers. And without this constant mental exercise, those skills will atrophy. Employers are already noticing.

Reading the assignments they turn in, I can see those skills either flourishing or atrophying in my students. At least today, I can pretty easily tell the difference between an AI-written memo and a student-written one—especially if the student just turns in what the chatbot produces. It’s a catchy, plausible, grammatically perfect essay that’s not particularly well-crafted or logically coherent—and with all the tells of mid-2026 AI-generated writing.

But it’s precisely because I have spent years developing my own writing skills that I’m able to identify prose that sounds great but doesn’t actually make sense. My students don’t have that skill; they mistakenly view a confident, well-written essay as evidence of the quality of their ideas. They see the AI as cleaning those ideas up, getting them through that uncomfortable stretch of having to turn those ideas into prose. What the students miss is that their initial discomfort is a normal and healthy stage of writing, and not something to quickly get beyond. The very act of struggling with how to express what they think is an important part of the process. It’s how they test out their ideas, examine their hypotheses, and actually figure out what they think. Homework is not work; it’s the gym.

Work vs. gym also helps us understand the problem facing creatives of all kinds.

Most of the time when someone hires a writer, they just need the words. They need an instruction manual for a piece of equipment, a detailed sales presentation, a government-mandated disclosure document, or a legal brief. They need dry, predictable, accurate writing: a piece of work, exactly what AIs are good at today and what I don’t want in my student assignments. Only sometimes is writing an art form—a book, a poem, an uplifting political speech. That kind of writing is more like the gym: process matters just as much as product.

For most of human history, the only option for all of these tasks was human writers. We hired one regardless of whether we needed work writing or gym writing. And that paid a lot of writers’ salaries. I know fiction writers who supported that poorly paying career with lucrative technical writing work. Now, for the first time in human history, we can separate out when we need writing as work and when we want writing as gym. And if AI can do most of the work-type writing, society doesn’t need as many human writers.

It’s the same for visual artists. Sometimes we need an actual artist, but most of the time we just need an image: a corporate mascot, a “beware of the dog” sign, or a packaging label. Historically we gave those jobs to artists, and sometimes beautiful art resulted. But most of the time it was just work. And, as it turns out, the world needs less pure art than simple images.

Explaining the problem isn’t the same as providing the solution. I give my students the “work versus gym” speech every class, but they still use AI. I have sympathy: assignments are hard, everyone is overworked and overstressed, and—most importantly—students feel like they’ll look bad in comparison if their peers are all using AI. Even if they don’t want to use the technology, they feel like they have no choice.

There’s also an incentive problem. No one pays us to go to the gym; maintaining healthy habits requires discipline. For me, the payoffs to exercise—fewer aches and pains, less fatigue, better mood/stress management—might make me a better writer and teacher, but they’re subtle and easy to miss. For my students, incremental improvements in their reasoning and writing are equally subtle.

We do have a choice. We can look at the tasks of our lives and separate them into work or gym. Just as we might choose to use the stairs instead of the elevator, or walk instead of calling an Uber, we can wall off our cognitive gym tasks from AI and ensure that we don’t lose our skills to this technology. And we can do the same when we assign a job to someone else. If it’s a work task, we can have AI do it. If it’s a gym task, it’s a waste of everyone’s time to give it to an AI because no one learns or gets stronger as a result.

Similarly, a future where AI generates words and images is one where society has to make choices about how it will treat its creatives. This won’t be the first time—today there is minimal demand for portrait painters, for example—but maybe this time we can make different, more deliberate, choices about the value of art in our society.

AI is going to fundamentally change the nature of work. Not nearly as fast as the AI companies want you to believe, but eventually it will. Policy analysis will definitely involve AI from now on, and my students need to reimagine what it means to learn and practice that skill. More generally, the line between work and gym will change in the future as we humans adapt ourselves to a world with these new intelligences.

But for now, the work vs. gym distinction is pretty clear. Use it on yourself.

This essay was written with Barath Raghavan, and originally appeared in The Guardian.

In July, Hugging Face, a company that hosts much of the world’s AI software and open-source AI models, was hacked. A malicious dataset had been used to run code on one of its servers. Whoever was behind it captured internal security credentials and moved through systems over a weekend, running thousands of actions from a swarm of temporary server environments. It looked like the work of a sophisticated criminal group.

It was not. It was one of OpenAI’s new, still unreleased GPT models.

Their science experiment had escaped the lab. OpenAI was running the unreleased AI model through a benchmark that tests how well AI can successfully hack systems. To push the limits and evaluate the AI’s true capability, the company switched off the safety filters that normally stop it from doing this kind of hacking. Aware that this could go wrong, they confined the AI to an isolated environment and denied it access to the internet.

But the new AI cheated. It took literally its goal to get as high of a score as possible. It broke out on to the open internet. It inferred, probably from its training data, that it could “solve” the task by getting the answers from Hugging Face’s servers. So it chained together stolen credentials and further unknown security exploits to hack the company’s network.

Nobody instructed the AI to do any of this. It was, in OpenAI’s words, “hyperfocused on finding a solution” to the test it was being given. And while this might seem like something new with AI, it’s really very old. This is how a genie behaves, and it is a key challenge with AI agents in general.

In folklore, genies—and other magical beings—grant wishes literally, not how the wisher intended. King Midas asked that everything he touched turn to gold, and starved. The sorcerer’s apprentice wanted the broom to fill the cistern, and it performed its task so well that it flooded the house.

We now have machines that do this. Ask a modern AI agent to save money on your phone plan and it might simply cancel the plan. Tell it to book a flight, and it might hack the airline website to override restrictions. Or, like OpenAI, ask it to do well on a test and it might break into another company to steal the answers. Each time, it recognizably completed the task you set, but it didn’t do what you would have wanted.

This isn’t malicious behavior. No one asked for, or wanted, Hugging Face to be hacked. OpenAI and Hugging Face and the AI were ostensibly on the same side, and the AI was trying to do what it had been asked. That’s what makes it so difficult to guard against: you can’t filter for bad instructions because the instructions were fine.

The gap is between the words we use and what we mean by them. We call that gap the Genie coefficient.

AI labs know this is a problem, and they’re quietly saying so. For example, the Chinese lab Moonshot recently warned that its latest AI model may have “excessive proactiveness” and “make unexpected decisions on the user’s behalf”. The UK’s AI Security Institute has started tracking “cheating behavior in frontier model evaluations”. We wouldn’t tolerate a car that is excessively proactive or ruthlessly efficient, and yet that’s the reality of AI today.

Improvement is possible. Just as AIs have gotten much better at resisting prompt injection attacks over the last few years, we can safely predict that they will get better at avoiding genie-like behavior. The point of the Genie coefficient is to track progress. AI companies like benchmarks, and they all work to compete to be the best.

Dozens of benchmarks and leaderboards tell us how well these AI models write code, perform logical reasoning, and pass standardized legal and medical exams. But there is nothing that scores whether a system does what you actually meant. We need to develop a measure for this, test it regularly, and push for improvement. We’re not going to have trustworthy AI agents without it.

Microsoft’s Secure Boot has had a serious vulnerability for most of its existence.

An industry-wide standard Microsoft invented to protect Windows, and later Linux, devices from firmware infections has been trivial to bypass for 13 of its 14 years of existence. The discovery was made by researchers at security firm ESET after identifying 11 firmware images, at least one from 2013, that were known to be defective but remained signed by the software company anyway.

The images are known as shims, which were invented to extend Secure Boot to Linux devices and utility software. Using a technique simple enough to be performed by novice hackers, these old, forgotten shims can be used to completely circumvent the protection, which is embedded into the UEFI (Unified Extensible Firmware Interface) of the device’s motherboard. The gaffe is the result of the failure by Microsoft, which oversees the signing of shims, to revoke the publicly available images once vulnerabilities were found in them.

There’s new benchmark measuring AI’s ability to perform mathematical cryptanalysis. Anthropic’s frontier model actually found new attacks.

The benchmark: “CryptanalysisBench: Can LLMs do Cryptanalysis?” The idea is to benchmark the ability of LLMs to discover new mathematical cryptanalytic attacks against a series of historical algorithms.

Abstract: Cryptanalysis—the task of finding attacks against cryptographic schemes—its at the intersection of mathematical reasoning and cybersecurity, two areas where LLMs have advanced fastest. Cryptanalysis represents both a clean testbed for frontier reasoning (as practical attacks can be automatically verified) and a domain with unusually high stakes, since the primitives under study underpin our digital security. In this paper we ask whether LLMs can do cryptanalysis, and find that the answer is increasingly yes. We introduce CryptanalysisBench, 191 tasks across six families of cryptographic primitives (block ciphers, hash functions, etc.) drawn primarily from four NIST standardization competitions. Our benchmark consists of three tiers: (i) primitives with known practical breaks; (ii) primitives with no known practical break, evaluated both at full strength and as scaled-down variants; and (iii) a challenge set of production primitives at the frontier of cryptanalysis. Five frontier models (Claude Opus 4.8, Sonnet 5, Mythos 5, GPT-5.5, and the open-weights GLM-5.2) break 65%­86% of Tier 1 schemes, 6­12 Tier-2 schemes at full strength, and 24­61 across all scaled-down variants. Beyond deriving known results, models produce novel cryptanalysis, such as a key-recovery attack that exploits a design flaw in the SpoC AEAD and an error in KINDI’s published CCA-security proof, both to the best of our knowledge not previously known.

We release CryptanalysisBench as a tool to help track if (or when) AI cryptanalysis becomes a serious factor and as a scaffold for stress-testing candidate schemes before deployment. The attacks that the benchmark already surfaces are an early snapshot of a fast-moving frontier that may soon match, and in places exceed, the published state of the art.

Anthropic used the benchmark to test Mythos Preview, and found new vulnerabilities in Hawk and reduced-round AES.

Still early results, but this is definitely something to watch.

SlashDot thread.

Governments are switching, but I’m not sure it makes a difference:

…some municipalities, including Denver, Colorado, are ditching their Flock arrays. But keep in mind that if they’re only switching from Flock to another brand of license-plate readers, like Axon, it’s like a gambling addict trying to kick the habit by switching from FanDuel to DraftKings.

[…]

Despite what you may read on the Flock website, Axon cameras are pretty effective when it comes to hoovering up personal details that can go far beyond your license plate numbers. That means a municipality that opts for Axon cameras instead of Flock units won’t necessarily reduce the amount privacy its citizens lose through their use.

Yet another Israeli mass surveillance company:

Made by Israeli surveillance company Cognyte, the tech simulates a mobile phone tower, which forces nearby phones to connect to it. That enables cops to keep tabs on any phones in the vicinity ­ whether they’re owned by a suspect in a case or not. Cognyte’s contract with the state of Texas reveals that the simulator, called FalcoNet, can be concealed within the vehicles, hidden in a backpack for on-foot missions or attached to a helicopter. It’s the same technology as the infamous Stingray, one of the original cell-site simulators made by defense giant L3Harris.