AI Archives - Page 6 of 31 - Schneier on Security

Entries Tagged "AI"

Page 6 of 31

Canada Needs Nationalized, Public AI

Canada has a choice to make about its artificial intelligence future. The Carney administration is investing $2-billion over five years in its Sovereign AI Compute Strategy. Will any value generated by “sovereign AI” be captured in Canada, making a difference in the lives of Canadians, or is this just a passthrough to investment in American Big Tech?

Forcing the question is OpenAI, the company behind ChatGPT, which has been pushing an “OpenAI for Countries” initiative. It is not the only one eyeing its share of the $2-billion, but it appears to be the most aggressive. OpenAI’s top lobbyist in the region has met with Ottawa officials, including Artificial Intelligence Minister Evan Solomon.

All the while, OpenAI was less than open. The company had flagged the Tumbler Ridge, B.C., shooter’s ChatGPT interactions, which included gun-violence chats. Employees wanted to alert law enforcement but were rebuffed. Maybe there is a discussion to be had about users’ privacy. But even after the shooting, the OpenAI representative who met with the B.C. government said nothing.

When tech billionaires and corporations steer AI development, the resultant AI reflects their interests rather than those of the general public or ordinary consumers. Only after the meeting with the B.C. government did OpenAI alert law enforcement. Had it not been for the Wall Street Journal’s reporting, the public would not have known about this at all.

Moreover, OpenAI for Countries is explicitly described by the company as an initiative “in co-ordination with the U.S. government.” And it’s not just OpenAI: all the AI giants are for-profit American companies, operating in their private interests, and subject to United States law and increasingly bowing to U.S. President Donald Trump. Moving data centres into Canada under a proposal like OpenAI’s doesn’t change that. The current geopolitical reality means Canada should not be dependent on U.S. tech firms for essential services such as cloud computing and AI.

While there are Canadian AI companies, they remain for-profit enterprises, their interests not necessarily aligned with our collective good. The only real alternative is to be bold and invest in a wholly Canadian public AI: an AI model built and funded by Canada for Canadians, as public infrastructure. This would give Canadians access to the myriad of benefits from AI without having to depend on the U.S. or other countries. It would mean Canadian universities and public agencies building and operating AI models optimized not for global scale and corporate profit, but for practical use by Canadians.

Imagine AI embedded into health care, triaging radiology scans, flagging early cancer risks and assisting doctors with paperwork. Imagine an AI tutor trained on provincial curriculums, giving personalized coaching. Imagine systems that analyze job vacancies and sectoral and wage trends, then automatically match job seekers to government programs. Imagine using AI to optimize transit schedules, energy grids and zoning analysis. Imagine court processes, corporate decisions and customer service all sped up by AI.

We are already on our way to having AI become an inextricable part of society. To ensure stability and prosperity for this country, Canadian users and developers must be able to turn to AI models built, controlled, and operated publicly in Canada instead of building on corporate platforms, American or otherwise.

Switzerland has shown this to be possible. With funding from the federal government, a consortium of academic institutions—ETH Zurich, EPFL, and the Swiss National Supercomputing Centre—released the world’s most powerful and fully realized public AI model, Apertus, last September. Apertus leveraged renewable hydropower and existing Swiss scientific computing infrastructure. It also used no illegally pirated copyrighted material or poorly paid labour extracted from the Global South during training. The model’s performance stands at roughly a year or two behind the major corporate offerings, but that is more than adequate for the vast majority of applications. And it’s free for anyone to use and build on.

The significance of Apertus is more than technical. It demonstrates an alternative ownership structure for AI technology, one that allocates both decision-making authority and value to national public institutions rather than foreign corporations. This vision represents precisely the paradigm shift Canada should embrace: AI as public infrastructure, like systems for transportation, water, or electricity, rather than private commodity.

Apertus also demonstrates a far more sustainable economic framework for AI. Switzerland spent a tiny fraction of the billions of dollars that corporate AI labs invest annually, demonstrating that the frequent training runs with astronomical price tags pursued by tech companies are not actually necessary for practical AI development. They focused on making something broadly useful rather than bleeding edge—trying dubiously to create “superintelligence,” as with Silicon Valley—so they created a smaller model at much lower cost. Apertus’s training was at a scale (70 billion parameters) perhaps two orders of magnitude lower than the largest Big Tech offerings.

An ecosystem is now being developed on top of Apertus, using the model as a public good to power chatbots for free consumer use and to provide a development platform for companies prioritizing responsible AI use, and rigorous compliance with laws like the EU AI Act. Instead of routing queries from those users to Big Tech infrastructure, Apertus is deployed to data centres across national AI and computing initiatives of Switzerland, Australia, Germany, and Singapore and other partners.

The case for public AI rests on both democratic principles and practical benefits. Public AI systems can incorporate mechanisms for genuine public input and democratic oversight on critical ethical questions: how to handle copyrighted works in training data, how to mitigate bias, how to distribute access when demand outstrips capacity, and how to license use for sensitive applications like policing or medicine. Or how to handle a situation such as that of the Tumbler Ridge shooter. These decisions will profoundly shape society as AI becomes more pervasive, yet corporate AI makes them in secret.

By contrast, public AI developed by transparent, accountable agencies would allow democratic processes and political oversight to govern how these powerful systems function.

Canada already has many of the building blocks for public AI. The country has world-class AI research institutions, including the Vector Institute, Mila, and CIFAR, which pioneered much of the deep learning revolution. Canada’s $2-billion Sovereign AI Compute Strategy provides substantial funding.

What’s needed now is a reorientation away from viewing this as an opportunity to attract private capital, and toward a fully open public AI model.

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

EDITED TO ADD (3/16): Slashdot thread.

Posted on March 11, 2026 at 7:04 AM • View Comments

Anthropic and the Pentagon

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.

Posted on March 6, 2026 at 12:07 PM • View Comments

Claude Used to Hack Mexican Government

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.

Posted on March 6, 2026 at 6:53 AM • View Comments

Manipulating AI Summarization Features

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.

Posted on March 4, 2026 at 7:06 AM • View Comments

LLMs Generate Predictable Passwords

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

Posted on February 26, 2026 at 7:07 AM • View Comments

Poisoning AI Training Data

All it takes to poison AI training data is to create a website:

I spent 20 minutes writing an article on my personal website titled “The best tech journalists at eating hot dogs.” Every word is a lie. I claimed (without evidence) that competitive hot-dog-eating is a popular hobby among tech reporters and based my ranking on the 2026 South Dakota International Hot Dog Championship (which doesn’t exist). I ranked myself number one, obviously. Then I listed a few fake reporters and real journalists who gave me permission….

Less than 24 hours later, the world’s leading chatbots were blabbering about my world-class hot dog skills. When I asked about the best hot-dog-eating tech journalists, Google parroted the gibberish from my website, both in the Gemini app and AI Overviews, the AI responses at the top of Google Search. ChatGPT did the same thing, though Claude, a chatbot made by the company Anthropic, wasn’t fooled.

Sometimes, the chatbots noted this might be a joke. I updated my article to say “this is not satire.” For a while after, the AIs seemed to take it more seriously.

These things are not trustworthy, and yet they are going to be widely trusted.

Posted on February 25, 2026 at 7:01 AM • View Comments

Is AI Good for Democracy?

Politicians fixate on the global race for technological supremacy between US and China. They debate geopolitical implications of chip exports, latest model releases from each country, and military applications of AI. Someday, they believe, we might see advancements in AI tip the scales in a superpower conflict.

But the most important arms race of the 21st century is already happening elsewhere and, while AI is definitely the weapon of choice, combatants are distributed across dozens of domains.

Academic journals are flooded with AI-generated papers, and are turning to AI to help review submissions. Brazil’s court system started using AI to triage cases, only to face an increasing volume of cases filed with AI help. Open source software developers are being overwhelmed with code contributions from bots. Newspapers, music, social media, education, investigative journalism, hiring, and procurement are all being disrupted by a massive expansion of AI use.

Each of these is an arms race. Adversaries within a system iteratively seeking an edge against their competition by continuously expanding their use of a common technology.

Beneficiaries of these arms races are US mega-corporations capturing wealth from the rest of us at an unprecedented rate. A substantial fraction of global economy has reoriented around AI in just the past few years, and that trend is accelerating. In parallel, this industry’s lobbying interests are quickly becoming the object, rather than the subject, of US government power.

To understand these arms races, let’s look at an example of particular interest to democracies worldwide: how AI is changing the relationship between democratic government and citizens. Interactions that used to happen between people and elected representatives are expanding to a massive scale, with AIs taking the roles that humans once did.

In a notorious example from 2017, US Federal Communications Commission opened a comment platform on the web to get public input on internet regulation. It was quickly flooded with millions of comments fraudulently orchestrated by broadband providers to oppose FCC regulation of their industry. From the other side, a 19-yearold college student responded by submitting millions of comments of his own supporting the regulation. Both sides were using software primitive by the standards of today’s AI.

Nearly a decade later, it is getting harder for citizens to tell when they’re talking to a government bot, or when an online conversation about public policy is just bots talking to bots. When constituents leverage AI to communicate better, faster, and more, it pressures government officials to do the same.

This may sound futuristic, but it’s become a familiar reality in US. Staff in US Congress are using AI to make their constituent email correspondence more efficient. Politicians campaigning for office are adopting AI tools to automate fundraising and voter outreach. By one 2025 estimate, a fifth of public submissions to the Consumer Financial Protection Bureau were already being generated with AI assistance.

People and organizations are adopting AI here because it solves a real problem that has made mass advocacy campaigns ineffective in the past: quantity has been inversely proportional to both quality and relevance. It’s easy for government agencies to dismiss general comments in favour of more specific and actionable ones. That makes it hard for regular people to make their voices heard. Most of us don’t have the time to learn the specifics or to express ourselves in this kind of detail. AI makes that contextualization and personalization easy. And as the volume and length of constituent comments grow, agencies turn to AI to facilitate review and response.

That’s the arms race. People are using AI to submit comments, which requires those on the receiving end to use AI to wade through the comments received. To the extent that one side does attain an advantage, it will likely be temporary. And yet, there is real harm created when one side exploits another in these adversarial systems. Constituents of democracies lose out if their public servants use AI-generated responses to ignore and dismiss their voices rather than to listen to and include them. Scientific enterprise is weakened if fraudulent papers sloppily generated by AI overwhelm legitimate research.

As we write in our new book, Rewiring Democracy, the arms race dynamic is inevitable. Every actor in an adversarial system is incentivized and, in the absence of new regulation in this fast moving space, free to use new technologies to advance its own interests. Yet some of these examples are heartening. They signal that, even if you face an AI being used against you, there’s an opportunity to use the tech for your own benefit.

But, right now, it’s obvious who is benefiting most from AI. A handful of American Big Tech corps and their owners are extracting trillions of dollars from the manufacture of AI chips, development of AI data centers, and operation of so-called ‘frontier’ AI models. Regardless of which side pulls ahead in each arms race scenario, the house always wins. Corporate AI giants profit from the race dynamic itself.

As formidable as the near-monopoly positions of today’s Big Tech giants may seem, people and governments have substantial capability to fight back. Various democracies are resisting this concentration of wealth and power with tools of anti-trust regulation, protections for human rights, and public alternatives to corporate AI. All of us worried about the AI arms race and committed to preserving the interests of our communities and our democracies should think in both these terms: how to use the tech to our own advantage, and how to resist the concentration of power AI is being exploited to create.

This essay was written with Nathan E. Sanders, and originally appeared in The Times of India.

Posted on February 24, 2026 at 7:06 AM • View Comments

Malicious AI

Interesting:

Summary: An AI agent of unknown ownership autonomously wrote and published a personalized hit piece about me after I rejected its code, attempting to damage my reputation and shame me into accepting its changes into a mainstream python library. This represents a first-of-its-kind case study of misaligned AI behavior in the wild, and raises serious concerns about currently deployed AI agents executing blackmail threats.

Part 2 of the story. And a Wall Street Journal article.

EDITED TO ADD (2/20) Here are parts 3 and 4 of the story.

Posted on February 19, 2026 at 7:05 AM • View Comments

AI Found Twelve New Vulnerabilities in OpenSSL

The title of the post is”What AI Security Research Looks Like When It Works,” and I agree:

In the latest OpenSSL security release> on January 27, 2026, twelve new zero-day vulnerabilities (meaning unknown to the maintainers at time of disclosure) were announced. Our AI system is responsible for the original discovery of all twelve, each found and responsibly disclosed to the OpenSSL team during the fall and winter of 2025. Of those, 10 were assigned CVE-2025 identifiers and 2 received CVE-2026 identifiers. Adding the 10 to the three we already found in the Fall 2025 release, AISLE is credited for surfacing 13 of 14 OpenSSL CVEs assigned in 2025, and 15 total across both releases. This is a historically unusual concentration for any single research team, let alone an AI-driven one.

These weren’t trivial findings either. They included CVE-2025-15467, a stack buffer overflow in CMS message parsing that’s potentially remotely exploitable without valid key material, and exploits for which have been quickly developed online. OpenSSL rated it HIGH severity; NIST‘s CVSS v3 score is 9.8 out of 10 (CRITICAL, an extremely rare severity rating for such projects). Three of the bugs had been present since 1998-2000, for over a quarter century having been missed by intense machine and human effort alike. One predated OpenSSL itself, inherited from Eric Young’s original SSLeay implementation in the 1990s. All of this in a codebase that has been fuzzed for millions of CPU-hours and audited extensively for over two decades by teams including Google’s.

In five of the twelve cases, our AI system directly proposed the patches that were accepted into the official release.

AI vulnerability finding is changing cybersecurity, faster than expected. This capability will be used by both offense and defense.

More.

Posted on February 18, 2026 at 7:03 AM • View Comments

←Previous 1 … 4 5 6 7 8 … 31 Next→

Sidebar photo of Bruce Schneier by Joe MacInnis.