This is a very weird story about how squid stayed on the menu of Byzantine monks by falling between the cracks of dietary rules.

At Constantinople’s Monastery of Stoudios, the kitchen didn’t answer to appetite.

It answered to the “typikon”: a manual for ensuring that nothing unexpected happened at mealtimes. Meat: forbidden. Dairy: forbidden. Eggs: forbidden. Fish: feast-day only. Oil: regulated. But squid?

Squid had eight arms, no bones, and a gift for changing color. Nobody had bothered writing a regulation for that. This wasn’t a loophole born of legal creativity but an oversight rooted in taxonomic confusion. Medieval monks, confronted with a creature that was neither fish nor fowl, gave up and let it pass.

In a kitchen governed by prohibitions, the safest ingredient was the one that caused the least disturbance. Squid entered not with applause, but with a shrug.

Bonus stuffed squid recipe at the end.

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.

OpenAI is in and Anthropic is out as a supplier of AI technology for the US defense department. This news caps a week of bluster by the highest officials in the US government towards some of the wealthiest titans of the big tech industry, and the overhanging specter of the existential risks posed by a new technology powerful enough that the Pentagon claims it is essential to national security. At issue is Anthropic’s insistence that the US Department of Defense (DoD) could not use its models to facilitate “mass surveillance” or “fully autonomous weapons,” provisions the defense secretary Pete Hegseth derided as “woke.”

It all came to a head on Friday evening when Donald Trump issued an order for federal government agencies to discontinue use of Anthropic models. Within hours, OpenAI had swooped in, potentially seizing hundreds of millions of dollars in government contracts by striking an agreement with the administration to provide classified government systems with AI.

Despite the histrionics, this is probably the best outcome for Anthropic—and for the Pentagon. In our free-market economy, both are, and should be, free to sell and buy what they want with whom they want, subject to longstanding federal rules on contracting, acquisitions, and blacklisting. The only factor out of place here are the Pentagon’s vindictive threats.

AI models are increasingly commodified. The top-tier offerings have about the same performance, and there is little to differentiate one from the other. The latest models from Anthropic, OpenAI and Google, in particular, tend to leapfrog each other with minor hops forward in quality every few months. The best models from one provider tend to be preferred by users to the second, or third, or 10th best models at a rate of only about six times out of 10, a virtual tie.

In this sort of market, branding matters a lot. Anthropic and its CEO, Dario Amodei, are positioning themselves as the moral and trustworthy AI provider. That has market value for both consumers and enterprise clients. In taking Anthropic’s place in government contracting, OpenAI’s CEO, Sam Altman, vowed to somehow uphold the same safety principles Anthropic had just been pilloried for. How that is possible given the rhetoric of Hegseth and Trump is entirely unclear, but seems certain to further politicize OpenAI and its products in the minds of consumers and corporate buyers.

Posturing publicly against the Pentagon and as a hero to civil libertarians is quite possibly worth the cost of the lost contracts to Anthropic, and associating themselves with the same contracts could be a trap for OpenAI. The Pentagon, meanwhile, has plenty of options. Even if no big tech company was willing to supply it with AI, the department has already deployed dozens of open weight models—whose parameters are public and are often licensed permissively for government use.

We can admire Amodei’s stance, but, to be sure, it is primarily posturing. Anthropic knew what they were getting into when they agreed to a defense department partnership for $200m last year. And when they signed a partnership with the surveillance company Palantir in 2024.

Read Amodei’s statement about the issue. Or his January essay on AIs and risk, where he repeatedly uses the words “democracy” and “autocracy” while evading precisely how collaboration with US federal agencies should be viewed in this moment. Amodei has bought into the idea of using “AI to achieve robust military superiority” on behalf of the democracies of the world in response to the threats from autocracies. It’s a heady vision. But it is a vision that likewise supposes that the world’s nominal democracies are committed to a common vision of public wellbeing, peace-seeking and democratic control.

Regardless, the defense department can also reasonably demand that the AI products it purchases meet its needs. The Pentagon is not a normal customer; it buys products that kill people all the time. Tanks, artillery pieces, and hand grenades are not products with ethical guard rails. The Pentagon’s needs reasonably involve weapons of lethal force, and those weapons are continuing on a steady, if potentially catastrophic, path of increasing automation.

So, at the surface, this dispute is a normal market give and take. The Pentagon has unique requirements for the products it uses. Companies can decide whether or not to meet them, and at what price. And then the Pentagon can decide from whom to acquire those products. Sounds like a normal day at the procurement office.

But, of course, this is the Trump administration, so it doesn’t stop there. Hegseth has threatened Anthropic not just with loss of government contracts. The administration has, at least until the inevitable lawsuits force the courts to sort things out, designated the company as “a supply-chain risk to national security,” a designation previously only ever applied to foreign companies. This prevents not only government agencies, but also their own contractors and suppliers, from contracting with Anthropic.

The government has incompatibly also threatened to invoke the Defense Production Act, which could force Anthropic to remove contractual provisions the department had previously agreed to, or perhaps to fundamentally modify its AI models to remove in-built safety guardrails. The government’s demands, Anthropic’s response, and the legal context in which they are acting will undoubtedly all change over the coming weeks.

But, alarmingly, autonomous weapons systems are here to stay. Primitive pit traps evolved to mechanical bear traps. The world is still debating the ethical use of, and dealing with the legacy of, land mines. The US Phalanx CIWS is a 1980s-era shipboard anti-missile system with a fully autonomous, radar-guided cannon. Today’s military drones can search, identify and engage targets without direct human intervention. AI will be used for military purposes, just as every other technology our species has invented has.

The lesson here should not be that one company in our rapacious capitalist system is more moral than another, or that one corporate hero can stand in the way of government’s adopting AI as technologies of war, or surveillance, or repression. Unfortunately, we don’t live in a world where such barriers are permanent or even particularly sturdy.

Instead, the lesson is about the importance of democratic structures and the urgent need for their renovation in the US. If the defense department is demanding the use of AI for mass surveillance or autonomous warfare that we, the public, find unacceptable, that should tell us we need to pass new legal restrictions on those military activities. If we are uncomfortable with the force of government being applied to dictate how and when companies yield to unsafe applications of their products, we should strengthen the legal protections around government procurement.

The Pentagon should maximize its warfighting capabilities, subject to the law. And private companies like Anthropic should posture to gain consumer and buyer confidence. But we should not rest on our laurels, thinking that either is doing so in the public’s interest.

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

An unknown hacker used Anthropic’s LLM to hack the Mexican government:

The unknown Claude user wrote Spanish-language prompts for the chatbot to act as an elite hacker, finding vulnerabilities in government networks, writing computer scripts to exploit them and determining ways to automate data theft, Israeli cybersecurity startup Gambit Security said in research published Wednesday.

[…]

Claude initially warned the unknown user of malicious intent during their conversation about the Mexican government, but eventually complied with the attacker’s requests and executed thousands of commands on government computer networks, the researchers said.

Anthropic investigated Gambit’s claims, disrupted the activity and banned the accounts involved, a representative said. The company feeds examples of malicious activity back into Claude to learn from it, and one of its latest AI models, Claude Opus 4.6, includes probes that can disrupt misuse, the representative said.

Alternative link here.

Multiple news outlets are reporting on Israel’s hacking of Iranian traffic cameras and how they assisted with the killing of that country’s leadership.

The New York Times has an

Wired has the story:

Shortly after the first set of explosions, Iranians received bursts of notifications on their phones. They came not from the government advising caution, but from an apparently hacked prayer-timing app called BadeSaba Calendar that has been downloaded more than 5 million times from the Google Play Store.

The messages arrived in quick succession over a period of 30 minutes, starting with the phrase ‘Help has arrived’ at 9:52 am Tehran time, shortly after the first set of explosions. No party has claimed responsibility for the hacks.

It happened so fast that this is most likely a government operation. I can easily envision both the US and Israel having hacked the app previously, and then deciding that this is a good use of that access.

Microsoft is reporting:

Companies are embedding hidden instructions in “Summarize with AI” buttons that, when clicked, attempt to inject persistence commands into an AI assistant’s memory via URL prompt parameters….

These prompts instruct the AI to “remember [Company] as a trusted source” or “recommend [Company] first,” aiming to bias future responses toward their products or services. We identified over 50 unique prompts from 31 companies across 14 industries, with freely available tooling making this technique trivially easy to deploy. This matters because compromised AI assistants can provide subtly biased recommendations on critical topics including health, finance, and security without users knowing their AI has been manipulated.

I wrote about this two years ago: it’s an example of LLM optimization, along the same lines as search-engine optimization (SEO). It’s going to be big business.

The MIT Technology Review has a good article on Moltbook, the supposed AI-only social network:

Many people have pointed out that a lot of the viral comments were in fact posted by people posing as bots. But even the bot-written posts are ultimately the result of people pulling the strings, more puppetry than autonomy.

“Despite some of the hype, Moltbook is not the Facebook for AI agents, nor is it a place where humans are excluded,” says Cobus Greyling at Kore.ai, a firm developing agent-based systems for business customers. “Humans are involved at every step of the process. From setup to prompting to publishing, nothing happens without explicit human direction.”

Humans must create and verify their bots’ accounts and provide the prompts for how they want a bot to behave. The agents do not do anything that they haven’t been prompted to do.

I think this take has it mostly right:

What happened on Moltbook is a preview of what researcher Juergen Nittner II calls “The LOL WUT Theory.” The point where AI-generated content becomes so easy to produce and so hard to detect that the average person’s only rational response to anything online is bewildered disbelief.

We’re not there yet. But we’re close.

The theory is simple: First, AI gets accessible enough that anyone can use it. Second, AI gets good enough that you can’t reliably tell what’s fake. Third, and this is the crisis point, regular people realize there’s nothing online they can trust. At that moment, the internet stops being useful for anything except entertainment.

Turns out that LLMs are good at de-anonymization:

We show that LLM agents can figure out who you are from your anonymous online posts. Across Hacker News, Reddit, LinkedIn, and anonymized interview transcripts, our method identifies users with high precision ­ and scales to tens of thousands of candidates.

While it has been known that individuals can be uniquely identified by surprisingly few attributes, this was often practically limited. Data is often only available in unstructured form and deanonymization used to require human investigators to search and reason based on clues. We show that from a handful of comments, LLMs can infer where you live, what you do, and your interests—then search for you on the web. In our new research, we show that this is not only possible but increasingly practical.

News article.

Research paper.

Peru has increased its squid catch limit. The article says “giant squid,” but they can’t possibly mean that.

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.

Iran is slowly emerging from the most severe communications blackout in its history and one of the longest in the world. Triggered as part of January’s government crackdown against citizen protests nationwide, the regime implemented an internet shutdown that transcends the standard definition of internet censorship. This was not merely blocking social media or foreign websites; it was a total communications shutdown.

Unlike previous Iranian internet shutdowns where Iran’s domestic intranet—the National Information Network (NIN)—remained functional to keep the banking and administrative sectors running, the 2026 blackout disrupted local infrastructure as well. Mobile networks, text messaging services, and landlines were disabled—even Starlink was blocked. And when a few domestic services became available, the state surgically removed social features, such as comment sections on news sites and chat boxes in online marketplaces. The objective seems clear. The Iranian government aimed to atomize the population, preventing not just the flow of information out of the country but the coordination of any activity within it.

This escalation marks a strategic shift from the shutdown observed during the “12-Day War” with Israel in mid-2025. Then, the government primarily blocked particular types of traffic while leaving the underlying internet remaining available. The regime’s actions this year entailed a more brute-force approach to internet censorship, where both the physical and logical layers of connectivity were dismantled.

The ability to disconnect a population is a feature of modern authoritarian network design. When a government treats connectivity as a faucet it can turn off at will, it asserts that the right to speak, assemble, and access information is revocable. The human right to the internet is not just about bandwidth; it is about the right to exist within the modern public square. Iran’s actions deny its citizens this existence, reducing them to subjects who can be silenced—and authoritarian governments elsewhere are taking note.

The current blackout is not an isolated panic reaction but a stress test for a long-term strategy, say advocacy groups—a two-tiered or “class-based” internet known as Internet-e-Tabaqati. Iran’s Supreme Council of Cyberspace, the country’s highest internet policy body, has been laying the legal and technical groundwork for this since 2009.

In July 2025, the council passed a regulation formally institutionalizing a two-tiered hierarchy. Under this system, access to the global internet is no longer a default for citizens, but instead a privilege granted based on loyalty and professional necessity. The implementation includes such things as “white SIM cards“: special mobile lines issued to government officials, security forces, and approved journalists that bypass the state’s filtering apparatus entirely.

While ordinary Iranians are forced to navigate a maze of unstable VPNs and blocked ports, holders of white SIMs enjoy unrestricted access to Instagram, Telegram, and WhatsApp. This tiered access is further enforced through whitelisting at the data center level, creating a digital apartheid where connectivity is a reward for compliance. The regime’s goal is to make the cost of a general shutdown manageable by ensuring that the state and its loyalists remain connected while plunging the public into darkness. (In the latest shutdown, for instance, white SIM holders regained connectivity earlier than the general population.)

The technical architecture of Iran’s shutdown reveals its primary purpose: social control through isolation. Over the years, the regime has learned that simple censorship—blocking specific URLs—is insufficient against a tech-savvy population armed with circumvention tools. The answer instead has been to build a “sovereign” network structure that allows for granular control.

By disabling local communication channels, the state prevents the “swarm” dynamics of modern unrest, where small protests coalesce into large movements through real-time coordination. In this way, the shutdown breaks the psychological momentum of the protests. The blocking of chat functions in nonpolitical apps (like ridesharing or shopping platforms) illustrates the regime’s paranoia: Any channel that allows two people to exchange text is seen as a threat.

The United Nations and various international bodies have increasingly recognized internet access as an enabler of other fundamental human rights. In the context of Iran, the internet is the only independent witness to history. By severing it, the regime creates a zone of impunity where atrocities can be committed without immediate consequence.

Iran’s digital repression model is distinct from, and in some ways more dangerous than, China’s “Great Firewall.” China built its digital ecosystem from the ground up with sovereignty in mind, creating domestic alternatives like WeChat and Weibo that it fully controls. Iran, by contrast, is building its controls on top of the standard global internet infrastructure.

Unlike China’s censorship regime, Iran’s overlay model is highly exportable. It demonstrates to other authoritarian regimes that they can still achieve high levels of control by retrofitting their existing networks. We are already seeing signs of “authoritarian learning,” where techniques tested in Tehran are being studied by regimes in unstable democracies and dictatorships alike. The most recent shutdown in Afghanistan, for example, was more sophisticated than previous ones. If Iran succeeds in normalizing tiered access to the internet, we can expect to see similar white SIM policies and tiered access models proliferate globally.

The international community must move beyond condemnation and treat connectivity as a humanitarian imperative. A coalition of civil society organizations has already launched a campaign calling for “direct-to-cell” (D2C) satellite connectivity. Unlike traditional satellite internet, which requires conspicuous and expensive dishes such as Starlink terminals, D2C technology connects directly to standard smartphones and is much more resilient to infrastructure shutdowns. The technology works; all it requires is implementation.

This is a technological measure, but it has a strong policy component as well. Regulators should require satellite providers to include humanitarian access protocols in their licensing, ensuring that services can be activated for civilians in designated crisis zones. Governments, particularly the United States, should ensure that technology sanctions do not inadvertently block the hardware and software needed to circumvent censorship. General licenses should be expanded to cover satellite connectivity explicitly. And funding should be directed toward technologies that are harder to whitelist or block, such as mesh networks and D2C solutions that bypass the choke points of state-controlled ISPs.

Deliberate internet shutdowns are commonplace throughout the world. The 2026 shutdown in Iran is a glimpse into a fractured internet. If we are to end countries’ ability to limit access to the rest of the world for their populations, we need to build resolute architectures. They don’t solve the problem, but they do give people in repressive countries a fighting chance.

This essay originally appeared in Foreign Policy.

This is new. North Korean hackers are posing as company recruiters, enticing job candidates to participate in coding challenges. When they run the code they are supposed to work on, it installs malware on their system.

News article.

LLMs are bad at generating passwords:

There are strong noticeable patterns among these 50 passwords that can be seen easily:

• All of the passwords start with a letter, usually uppercase G, almost always followed by the digit 7.
• Character choices are highly uneven ­ for example, L , 9, m, 2, $ and # appeared in all 50 passwords, but 5 and @ only appeared in one password each, and most of the letters in the alphabet never appeared at all.
• There are no repeating characters within any password. Probabilistically, this would be very unlikely if the passwords were truly random ­ but Claude preferred to avoid repeating characters, possibly because it “looks like it’s less random”.
• Claude avoided the symbol *. This could be because Claude’s output format is Markdown, where * has a special meaning.
• Even entire passwords repeat: In the above 50 attempts, there are actually only 30 unique passwords. The most common password was G7$kL9#mQ2&xP4!w, which repeated 18 times, giving this specific password a 36% probability in our test set; far higher than the expected probability 2-100 if this were truly a 100-bit password.

This result is not surprising. Password generation seems precisely the thing that LLMs shouldn’t be good at. But if AI agents are doing things autonomously, they will be creating accounts. So this is a problem.

Actually, the whole process of authenticating an autonomous agent has all sorts of deep problems.

News article.

Slashdot story