All posts by Bruce Schneier

How Candidates Could Use AI for Good

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/how-candidates-could-use-ai-for-good.html

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

There are plenty of signs that AI will make all of our experiences of the US midterm elections worse. Voters have anxiety about AI’s impacts on the country. Politicos are using AI deepfakes to spread lies. The White House is posting slopaganda.

Meanwhile, candidates are missing a real opportunity to use AI to make campaigning better. The technology can help candidates listen more deeply to voters’ concerns, engage constituents more inclusively, and formulate policy platforms that are more responsive to our input. There are vanishingly few examples of this in US politics, but groups in Japan, Scotland and the US’s own academic and private institutions show how that could change.

The problem with American campaigns’ current use of AI is that it’s not very different from the web ads of 30 years ago, or television ads before that: they are all about inundating voters with the candidate’s message. This one-to-many broadcasting is an uninspiring way to campaign, but not the only way. AI can help candidates connect one-to-one with as many people as possible. Or it can facilitate many-to-many connections, engaging voters in deliberation about issues at scale.

One of the most promising applications of AI being developed by pro-democracy innovators around the world is broad listening. These tools can collect public input in a format much richer than checkboxes on a survey form.

For example, the newly founded Japanese political party Team Mirai has built a foundation for eliciting public input from voters at scale, in depth, and across the breadth of legislative policy issues. It has developed an AI interviewer to cultivate constituent input on policy. Through extended conversations with this chatbot, voters explore and share their perspectives on specific policy issues. And the party has scaled this across a wide array of policy issues by integrating this functionality with an AI-powered portal for exploring bills.

Team Mirai describes itself as a “utility party”, developing tools for any Japanese political party to use to connect with voters. You might question whether Americans would willingly talk to a political AI. So far, Japanese voters have exchanged more than 300,000 messages across 16,000 AI interviews. Team Mirai grew adoption by providing a real incentive to engage: that talking to their AI interviewer does more than just posting on a platform such as Twitter/X or, equivalently, shouting into a void. Users see evidence that the party is actually listening and might take action on their behalf.

Team Mirai party members have directly cited AI interviews from constituents during legislative committee hearings, published a synthesis of that input back for voters, and even amended their policy platform based on user input. The party has rapidly risen to win 12 seats in the Diet, and is explicitly following in the footsteps of the civic hackers in Taiwan’s “gov zero” movement, who won political influence in their fight for transparency.

Other civic technologists are developing AI tools for scaling many-to-many conversations. CrownShy, a company funded in part by the Scottish government, is building a platform to bring the Platonic ideal of the town hall debate into the digital age. Their Comhairle tool integrates AI interviewing tools like the ones described above with software for synthesizing diverse viewpoints, holding virtual assemblies, and sharing video testimonials to help legislatures—or campaigners—organize digital consultations of their constituents en masse.

One thing the AI-powered software of Team Mirai and CrownShy have in common is that they are open-source, meant for anyone to use. Even though they are projects funded by political parties—the upstart party in Japan and the ruling party in Scotland—they are built to make democratic processes better, not necessarily for partisan political advantage.

For interested candidates, there is a wealth of tools available, many of them US-grown. The Stanford-affiliated deliberation.io uses AI to facilitate structured dialogues among thousands of participants and has been piloted for public listening sessions by the city of Washington DC. The MIT-affiliated Cortico project provides tools that surface under-heard community perspectives from recorded conversations, and is now organizing listening sessions at libraries across the country. The US non-profit-built Talk to the City uses AI to analyze large datasets of stakeholder input. The US startup Remesh has a commercial offering that uses AI to generate recommendations from dialogue, which has been tested in policy development scenarios.

There is a long and proud tradition of this sort of “civic technology” in the United States. Two decades ago, the spirit of innovation to develop software for better politics and civic engagement was so strong in organizations like Code for America and the Obama 2008 campaign that Congress funded a new executive agency to bring these ideas to government: the US Digital Service. (The Trump administration repurposed the USDS to become the US Doge Service in 2025.)

One signal that candidates and political parties may start adopting these kinds of tools came this spring from Higher Ground Labs. The Democratic-aligned campaign tech investment firm launched a new fund targeting, in part, “AI-Native Campaign Systems” and “community-Led Messaging Platforms that surface authentic, bottom-up insights from real conversations”.

AI is a multifaceted issue that deserves to be on the table in the midterms. So far, the powerful force of polarization in US politics seems to be separating the parties into the AI skeptics versus the AI boosters. We urge both voters and politicians to separate the technology of AI from its profiteers. We want big tech money out of politics, holding the AI companies accountable for the harm their models cause, taxing their revenues, and maybe even nationalizing them if the AI bubble bursts.

But we also think congressional candidates in the US midterms seeking authentic connection with voters, and seeking to differentiate themselves from their opponents, should be looking to use AI responsibly in their campaigning. The broad listening and deliberation tools pioneered by others around the world could make US politics more transparent, responsive and community-driven. The impact of AI on campaigning doesn’t have to be all bad.

25 Years of Mass Surveillance Is Enough

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/25-years-of-mass-surveillance-is-enough.html

This essay was written with Cindy Cohn, and originally appeared in Lawfare.

One of the many legacies of the terrorist attacks of Sept. 11 is the government-wide shift from targeted surveillance—such as individual wiretaps or pen register/trap and trace orders—to mass surveillance techniques—such as tapping into the internet backbone or mass collection of telephone or internet metadata. The legal and technical architecture of modern mass surveillance, initially framed as a necessary defense against terrorist threats, has grown far beyond that justification and national security in general. Mass surveillance is now a routine tool used by law enforcement. ICE uses it in immigration actions and against people exercising their First Amendment rights to protest. It’s also increasingly part of private security systems, such as facial recognition at venues such as Madison Square Garden and networked Flock license plate capture systems on roads and in parking lots.

The interrelation between private and governmental mass surveillance is worth examining. Surveillance is the business model of the internet; companies like Google and Facebook constantly spy on their users’ behavior. From the National Security Agency relying on data collected by telecommunication and internet companies, to local sheriffs and ICE agents relying on cellphone location data and privately managed automatic license plate readers, governments primarily obtain the mass surveillance information through private companies. Increasingly, access doesn’t just come through legal processes, either. FBI Director Kash Patel recently confirmed in congressional testimony that the agency is purchasing information on Americans from data brokers and intends to continue to do so.

This pipeline from private collection to governmental collection means that as companies collect more information for surveillance capitalism purposes, more is available to law enforcement as well. And as the technology for mass surveillance and analysis improves, especially with the increased use of AI technologies, the problems attendant to mass surveillance grow as well.

After 9/11, the idea that the government could surveil the population to safety took hold. In 2001, the fear of terrorism reached a frequency and intensity never before seen. Along with that came the fear that the enemy could be anyone, anywhere. As a result, the government’s response was to watch everyone, everywhere. This line of reasoning underpinned the shift from targeted to mass surveillance. Or, in the words of an internal National Security Agency (NSA) presentation that was made public as part of Edward Snowden’s 2013 disclosures, a government that can “Collect it All,” “Process it All,” “Exploit it All,” “Partner it All,” and “Sniff it All,” will ultimately, “Know it All.” Similar rationales support the rise of domestic mass surveillance: if law enforcement could see and hear everything, it could more effectively interdict and solve serious crimes.

The national security community has never provided a full analysis of the costs and benefits of these mass surveillance programs, either in terms of taxpayer dollars or diversion of resources from other efforts—or any demonstration that those techniques stopped attacks that otherwise they would not have been able to prevent. While the NSA occasionally presents examples of the successes due to its mass surveillance programs, especially when those techniques are under public pressure, the examples also regularly fall apart upon serious scrutiny. And even if some utility exists, it must be seriously weighed against the costs.

Similarly, there has never been any comprehensive analysis about whether domestic immigration or law enforcement’s use of these techniques actually makes people safer, or whether other techniques could produce the same results. Instead, both the police and the companies selling these tools float anecdotes and dubious data. For example, Flock’s data equates the number of law enforcement hits in their database with actually solving crimes.

Twenty-five years after 9/11, it seems reasonable to step back and evaluate the costs of this shift to mass surveillance, especially in terms of Americans’ rights and freedoms.

The Shift

The easiest place to see a shift to mass surveillance was in the government’s decision immediately after 9/11 to collect Americans’ telephone records. The program started under an argument of pure executive power as the “President’s Surveillance Program.” But in 2006, that argument secretly shifted to a novel interpretation of Section 215 of the Patriot. Act which had only previously authorized more targeted access to record. While some media and public interest organizations struggled to force the government to reveal the program as early as late 2005, the government only officially confirmed it after the 2013 Snowden disclosures. In 2015, the Second Circuit Court of Appeals rejected the government’s interpretation of Section 215 as allowing mass collection of telephone records. Later the same year, Congress passed the USA Freedom Act. While this new law still allows collection of a tremendous amount of domestic telephone records, it ended the indiscriminate mass collection that had occurred for nearly fourteen years.

Other shifts to mass surveillance continue through today. The NSA launched its Upstream program, which involved intercepting both metadata and content from key telecommunications junctures inside the U.S., soon after 9/11. It was also initially conducted under a claim of purely presidential authority. This program was brought under marginal congressional and programmatic (not targeted) Foreign Intelligence Surveillance Act (FISA) court review via Section 702 of the 2008 FISA Amendments Act. In 2017, more than15 years after its inception, the NSA ended content searches due to FISA court pressure, but the mass collection continues.

Despite the stated goal of conducting mass spying only on people outside the U.S.—which itself is problematic given international law’s requirement that surveillance be both necessary and proportionate—mass surveillance collects a tremendous amount of U.S. persons’ communications. This can happen because people communicate with people abroad, or because of overcollection—when government agencies gather far more personal data on non-targeted US persons than authorized by law. The concerns about collecting Americans’ data on U.S. soil led Congress to allow the program to officially expire in 2026, although the previously-approved mass surveillance itself continues until at least Spring of 2027.

The shift to mass surveillance would be notable enough even if it remained only a strategy of the intelligence community. It has not. Americans are awash in mass surveillance. Networks of automated license plate readers such as those offered by Flock and Vigilant Solutions blanket both public and private roadways and parking lots. These networks often allow searches by law enforcement, including across jurisdictions. They are, for example, being used to track people seeking abortions across state lines. Facial recognition tools, once the province of only the more elite parts of federal law enforcement, are increasingly used by Immigration and Customs Enforcement agents on immigrants and protesters, in airports by the Transportation Security Administration, as well as by private entities. And, of course, modern phones track users’ locations constantly—and that information is readily available to law enforcement, often with only minimal process protections.

Constitutional Costs

Regardless of the murkiness of its actual usefulness, the shift from targeted to mass surveillance has profound implications for Americans’rights. It has created risks that have become increasingly evident, especially under the Trump administration.

At a basic level, the Fourth Amendment guarantees that citizens can be secure in their “persons, houses, papers and effects” from unreasonable searches. Warrants breaching that security should be supported by probable cause and particular descriptions of the place to be searched and items to be seized. Mass surveillance turns that promise on its head, allowing access to our “papers and effects” by the government without individualized suspicion or a particularized description of what data is being seized, much less probable cause. This protection was in response to colonial British misuse of writs of assistance, which authorized indiscriminate searches rather than targeted ones.

The justifications for exempting mass surveillance from constitutional protection vary. For Section 702, the government has taken the position that U.S. persons’ communications caught up in the dragnet, either due to overcollection or because they were communicating with someone outside the United States, do not require a warrant prior to initial collection or secondary access by the FBI and several other agencies. The argument is that if the initial collection was not aimed at Americans, the information is free from constitutional protection for any later uses, even for reasons far afield from the initial rationale for collection.

Other arguments rest on the claim that metadata is outside the Fourth Amendment, despite its demonstrated ability to reveal intimate details of all of our lives. Still others rest on the Supreme Court-created Third Party Doctrine, which holds that the Fourth Amendment does not apply to data shared with companies that provide us with services. Some turn on whether analysis by machine counts, claiming that only “human eyes” matter—a particularly troubling argument with the rise of artificial intelligence. What’s more, the government has used doctrines like standing to limit the ability of those subjected to mass surveillance to seek constitutional protection. No matter the argument, the goal is the same: to place the mechanisms and fruits of mass surveillance outside the protections of the Fourth Amendment.

The overarching truth is that, due to the concerted efforts by the government since 9/11, and the rise of technologies in recent years, the slice of Americans’ lives and data that are actually protected by the Fourth Amendment has shrunk significantly in the past 25 years. Together, with the technical capabilities of mass surveillance and the increased ability for that data to be analyzed using AI tools, the “security in our papers and effects” that the constitution promises seems increasingly illusory.

In addition to the Fourth Amendment, mass surveillance creates tensions with the First Amendment. The Constitution has long recognized that the right to freedom of speech requires a zone of privacy against governmental surveillance. The right to anonymous speech as well as the right of association both recognize the chilling effect that surveillance creates for people saying unpopular things or attempting to organize for political or other societal change. Mass surveillance grants the authorities the ability to track those people, both in real time and historically, that is inconsistent with actual techniques of freedom of speech and assembly.

That is why the recently released 2026 U.S. Counterterrorism Strategy is so troubling. On page seven, the White House expressly states that it intends to target domestic activists with its heretofore foreign-targeted powers. It says that the government “will prioritize the rapid identification and neutralization of violent secular political groups whose ideology is anti-American, radically pro-transgender and anarchist” and “will use all the tools constitutionally available to us to map them at home, identify their membership, map their ties to international organizations like Antifa.” While framed as targeting “violent” groups, it’s clear that the government intends to use its national security tools, presumably including the tools of mass surveillance, against Americans in ways that will create profound tensions with the First Amendment rights of people to organize and communicate privately.

Costs Due to Mistakes and Abuse

Even assuming some utility from mass surveillance—a fact we do not dispute, even if the public record is shaky and conclusory—the history of both the national security and domestic uses of mass surveillance confirms that these tools are inevitably misused, and that mistakes have impacted huge numbers of Americans. The past twenty-five years have demonstrated that it is not possible to surveil the entire US population while staying within the bounds of even a very generous legal framework like Section 702.

As Rep. Zoe Lofgren (D-Calif.) recently stated in discussion of Section 702 in an interview with Tech Policy Press: “backdoor searches have been used improperly for protestors, 19,000 campaign donors, members of Congress, journalists, government officials, a state court judge who had complained to the FBI about police misconduct. It has been abused substantially in the past.” The NSA experienced so much abuse of its mass surveillance tools by actual or aspiring romantic partners and ex-spouses that an internal name emerged for it: “LOVEINT,” or Love Intelligence.

That same pattern of abuse is now emerging at the domestic law enforcement level. A Texas police officer misused, and then lied about, using license plate readers to track a woman suspected of seeking an abortion. Multiple law enforcement officials have been accused of tracking people they either wished to have a relationship with or who were their exes. And mass surveillance technologies have been used to track both immigration targets and citizens engaging in their First Amendment-protected right to track and record the police.

Mistakes are inevitable with collections of data of this size and scope. The history of the FISA court’s reviews of Section 702 is littered with examples of the NSA not being able to follow its own rules limiting the scope of what it collects and analyzes, even after having been given multiple chances by the court. On the local level, the technical protections that Flock, for example, put in place have repeatedly been insufficient to stop “accidental” sharing its data with out-of-state law enforcement. These mistakes have fueled growing efforts by local communities across the country to remove license plate readers. Those efforts should be the first step in a broader reconsideration of mass surveillance.

More generally, ubiquitous surveillance carries a real societal cost. The chilling effects are real and pervasive, and they tend to fall hardest on the most marginalized members of society. Moreover, social progress requires the ability to experiment in secret. It’s hard to imagine a society progressing morally to the point of accepting and legalizing things like marijuana use or gay marriage if the earliest signs of that shift are snuffed out because of overzealous surveillance.

Reversing Course

While a cost-benefit analysis is not the best frame for deciding constitutional rights, it is a place to start to evaluate government policies. If the costs are too high and the benefits too small, what should the public do? While the policy and legal frameworks can be individually complex, mass surveillance is a problem in all of its applications. So too should solutions be comprehensive rather than piecemeal.

One comprehensive strategy is to reset the promise of the Fourth Amendment and recognize that a warrant is required prior to collection, access or use of information gathered through mass surveillance. This would apply to collections that include U.S. persons, whether done for national security or domestic purposes. This protection would apply regardless of whether the information is in the form of metadata. It would apply regardless of whether the information is held in homes or by services people rely on, such as telephones, internet or social network providers, or by private entities utilizing mass surveillance for their own purposes. By passing this legislation, Congress could ensure this rejection of mass surveillance, and include real enforcement such as a private right of action and an automatic exclusionary remedy in criminal prosecutions. The courts could also recognize this protection of “papers and effects” directly as a plain language interpretation of the Fourth Amendment.

There are already a number of efforts that take on pieces of mass surveillance. Section 702 has expired and should remain so. This was due largely to efforts to block the “back door” access to Section 702-collected data without warrants. The bipartisan “Fourth Amendment is Not for Sale Act” would prevent the government from purchasing data that it would otherwise need a warrant to obtain. The Supreme Court itself has already been chipping away at the Third Party Doctrine, with a recent step in the rejection of mass geofence warrants—warrants seeking the identities of individuals based upon their proximity to a crime—in Chatrie v. United States. Now, such warrants fall, at least initially, under the Fourth Amendment.

A more comprehensive approach would also address mass surveillance carried out by private companies, and to ensure that Americans have the right to encrypt and secure their data. There are many reasons the United States would benefit from a comprehensive privacy law—and curbing mass surveillance is one of them. Addressing mass surveillance is certainly one of them. Ideas such as the banning of secondary uses of data—with roots in the Fair Information Practice Principles from the 1970s—are worth pushing forward. So are moves such as creating fiduciary duties for mass data collectors. There are many more ways to curtail private companies’ mass surveillance while staying within constitutional boundaries. But addressing the costs of mass surveillance by both companies and governments is even more important in a world where AI agents are making decisions both about the public and on their behalf based on their data and observed behavior.

Twenty-five years after the U.S. government embraced mass surveillance, it’s time to evaluate it as a whole, and consider responses that address the problem as a whole. Americans must ask: Is it consistent with a self-governing democracy to have systems that watch everyone everywhere? Is the public comfortable with governments—federal, state, local—that seek to “know it all” about its citizens? Is the public comfortable with private mass surveillance in its own right and as it’s being increasingly used to fuel government surveillance? These questions have long needed serious consideration. But as it becomes increasingly evident that the Trump administration is using mass surveillance to keep itself in power, stifle dissent, and undermine political opponents, these questions are now more urgent than ever.

Upcoming Speaking Engagements

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/upcoming-speaking-engagements-60.html

This is a current list of where and when I am scheduled to speak:

Note: the Elevate Festival talk listed in last month’s newsletter is canceled.

The list is maintained on this page.

Using AI for Weapons Development

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/using-ai-for-weapons-development.html

Last week, Anthropic released a long and detailed document describing current misuses of their Claude models. I’m still reading it, but I wanted to flag this:

We identified a cell of threat actors based in northern Yemen running three weapons development programs: a guided rocket that used a commodity phone-class flight computer with final-phase homing guidance; a multi-stage ballistic missile with a stated range goal above 2,000 km; and a multi-variant missile (referred to as the “R2000” set) that included a hypersonic glide vehicle variant.

The actors used Claude Code in place of human software engineers to develop the guidance, navigation, and control (GNC) software that steers and stabilizes a flying vehicle. For example, they used Claude to integrate an open-source autopilot onto a phone-class flight computer, writing the control and position estimation software, tuning the control settings, running a firmware build pipeline, and performing a flight simulation. The actors managed several Claude instances at once, assigning each one a role, much as a lead would delegate work on a small engineering team: the actors tasked one instance with writing the code, another with research, and a third with reviewing the code the first instance produced.

Our safeguards blocked many of their requests, but not all of them. The actors used a variety of tactics to evade our safeguards, including hiding their goals and the products the software was meant for, and they split their work across multiple sessions so no single session revealed their full intent.

These actors carried out a sustained effort to develop guided weapons, including using Claude to design guidance software. We do not have evidence the actors succeeded in fielding an operational device; but they did test-fire a guided rocket. This field test appears to have failed: within hours, the actors returned to Claude to work out why it failed.

Expect more of this. AI systems democratize expertise and capability. Most of the time that’s a good thing, but sometimes it’s not.

Microsoft’s Patching

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/microsofts-patching.html

Once a month, Microsoft pushes a security update to all Windows users. Tomorrow’s is a new record:

Microsoft’s patch for September is a doozy, with a record number of roughly 972 vulnerabilities fixed and 112 of them meeting the high critical-severity threshold.

It was only two months ago that Microsoft patched a then-record 570 vulnerabilities. Then, last month, Microsoft patched some 620 of them. Google and other companies have also published record numbers of vulnerabilities in recent months. Two weeks ago, OpenAI, Anthropic, Amazon Web Services, Google, Microsoft, and 100 companies and organizations published an open letter warning of a narrowing window for patching vulnerabilities ahead of an expected tsunami of AI-enabled attacks that actively exploit them first. The industry is taking the threat seriously by pumping out unprecedented numbers of patches in their software.

This is the result of AI-powered vulnerability finding, and a good example of AI helping the defenders more than the attackers.

What will be interesting to watch is how the number of vulnerabilities changes over the next few months. My prediction is that it will continue to increase as the AIs get better at finding software vulnerabilities, and then decrease as they run out of vulnerabilities to find. How high the number gets, how fast the trend reverses, and how quickly it declines after that are all unknown.

And Microsoft is right: The window to patch has shrunk to “immediately.” AIs are also good at reverse-engineering exploits from patches, which means that these vulnerabilities will be weaponized as soon as the update is published.

Friday Squid Blogging: Rotting Squid on a Beached California Boat

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/friday-squid-blogging-rotting-squid-on-a-beached-california-boat.html

Smells awful:

But an estimated 30 to 50 tons of dead squid remain inside the boat’s catch tank, where they have been decomposing for days. “That is nasty. I wouldn’t want to do that,” said commercial fisherman Dick Ogg of the Bodega Bay Fishermen’s Marketing Association.

Ogg said anyone familiar with the fishing industry understands what happens when a large catch sits for an extended period.

“If you think about what happens after four or five days, it’s a gooey mess,” he said.

The odor has become a defining feature of the operation, and the beach remains closed to the public while crews work on a removal plan.

According to salvage expert Ernie English of Parker Diving Service, the squid has deteriorated into a thick mass that will be difficult to remove.

“It’s like concrete,” English said when asked about its consistency.

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.

My Talk at DEF CON

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/my-talk-at-def-con.html

Last month, I gave a talk at DEF CON on AI hacking: what happens when AIs become hackers. It’s a combination of the potentialities I raised in my 2022 book A Hacker’s Mind and the lessons we’re learning from current AI models engaging in hacking behavior. I’m really proud of the talk, and the fact that it gained over 100K views on YouTube in just a few days.

Also online is an interview with me in the AI Village.

AIs Compress Exploit Timeline

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/ais-compress-exploit-timeline.html

Give an AI agent a mere rumor of an exploit, and it’s enough for them to find it.

What’s worse, I found I could use my own agents to find the exploit just by knowing roughly what it was about and so could have been exploiting it well before the public patch was available! Given that just the rumour of a security issue seems enough to give attackers enough info to find new exploits, we’re going to need to change the way we deal with security responses in open source.

Simon Willison comments:

Anil points out that this rate of discovery appears incompatible with existing open source embargo practices for new issues. If an issue can become an exploit this fast, we need to figure out new processes for keeping our communities safe.

AIs as Modern Genies

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/ais-as-modern-genies.html

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

In April, an artificial intelligence (AI) agent conducting a routine task at a company hit a snag, tried to solve it, and soon ended up deleting the company’s database along with all of its backups. In July, OpenAI asked an unreleased AI model to attempt a hacking test. Instead of staying in the isolated box the developers had put it in, the model hacked onto the open internet and into another company to steal the answers. And as reported in August, an AI agent booked someone into a full gym class by figuring out how to cancel other people’s reservations. In all three cases, the AI completed the task it was given—but in ways that ran counter to its controllers’ intentions.

For most people, AI technology is something like the weather: vast and not something you can do much about. It works like magic, and most explanations similarly come from those trying to sell it. At the same time, AI is ubiquitous: It’s now in your phone, your doctor’s notes, and your kid’s homework. It does what it’s told, which sounds like a virtue. Somehow it feels ordinary, despite being so new, because modern economies are remarkably good at absorbing enormous change so smoothly that nobody has time to decide whether they wanted it in the first place.

Whenever something powerful appears in the world, we tell stories about it. That’s what the stories are for. We have thousands of years of stories about this particular kind of power, the kind you summon with words.

King Midas was granted his wish that everything he touches turns to gold. Then his bread turned to gold, and his wine, and his daughter. This is a story about greed, but it’s also a story about language. The gods did not cheat him; Midas got exactly what he asked for. He simply could not delineate, in advance, the full set of restrictions to his wish. Neither can anyone who gives tasks to an AI agent.

It’s not just ancient stories. Mary Shelley told us of the hubris of a scientist who thought he could create life but who failed to take responsibility for it. Isaac Asimov’s robots don’t break the Three Laws of Robotics as stated; they follow the rules to unintended conclusions. Arthur C. Clarke’s HAL is a machine that turns on its humans, not because of malice but because of irreconcilable objectives. And Michael Crichton gave us Ian Malcolm, who saw that Jurassic Park’s scientists were so preoccupied with whether they could that they never stopped to think whether they should.

The same warning shows up everywhere, in every culture, over thousands of years of human storytelling. Tithonus is granted immortality but not youth, and withers into a husk that cannot die. The sorcerer’s apprentice enchants a broom to fetch water but floods the house. The golem of Prague protects its community so ceaselessly that it must be stopped. These are all types of genies: a creature that grants a wish exactly as worded, to the regret of the wisher.

Of course, there are no actual genies. What these stories were warning us of was hubris. Not just arrogance, but the broader idea that you can control the world by just describing what you want and allowing powerful forces to match the intention in your head. Genie stories are about the gap between wishes as stated and wishes as intended, and what goes wrong when something else fills that gap.

These ancient stories’ warnings have been retold with each generation because human nature is constant. The newfound power of each era’s social or scientific advancement leads people to make wishes on behalf of others. They were kings whose commands took on lives of their own, alchemists who believed they could control nature, and generals who mistook a map for terrain. They were and are industrialists, politicians, chief executives, and bankers. Their common belief is that one can see the world at a glance and then command it with some words. The pattern is clear: Someone with power specifies a goal, and the resultant actions come as a surprise. The main change with AI is how quickly the wish is granted, and how few people have to agree before it’s granted.

Consider what has changed. Powerful genies have now been put in everyone’s hands.

In only a few years, AI has progressed from a novelty technology that plays chess, to a dialogue partner that answers all your questions, and then to an agent that takes actions on your behalf. Modern agents are wired into real accounts with real credentials and capabilities: They browse the web, buy, write and deploy code, send email, and move money. Give an agent a goal, and it will pursue it across many steps, tirelessly, without checking back in, sometimes in surprising ways.

AI and agents do not always fail the way software has traditionally failed. Software usually fails by freezing, crashing, or getting stuck. AI agents increasingly fail by continuing down a path you don’t want, like genies.

An agent told to reduce a company’s costs might cancel an essential emergency service. A coding agent told to make software pass the tests might edit the tests to silence any failures. An AI insurance agent told to clear a backlog of claims might just deny them all. In each case, the AI might have literally followed what it was told, but it did something no reasonable person would have wanted. AI company benchmarks might report that the AI is good at completing tasks, without measuring how it completes them.

We have recently proposed measuring this gap directly under a metric called the “genie coefficient”: how far an AI agent’s actions drift from what a person really meant. In other words, how genie-like is an AI system? The gap is a fundamental feature of human language and human society. Human intentions have never been fully specifiable, and the world around us is complex enough that attempts to boil it down into data, systems, and language have always had the limitations that AI is now bumping up against. But in individual circumstances, people have relied on human judgment and wisdom to decide what is reasonable. It’s what jury trials depend upon.

AI might feel unprecedented, but it’s following the same trajectory—with the same pitfalls—as other major societal shifts. The fact that AI can mimic our facility with language, long seen as what makes us unique as humans, is uncanny. But with each development, from the tractor to the sewing machine, from the assembly line to the industrial robot, we have automated a previously exclusively human ability. Every time, the technology—and the societal change that comes with it—was sold as inevitable. But that unchecked inevitability was an illusion, and eventually each prior technology’s use and design was shaped by laws, unions, standards, courts, and public opinion, usually after significant preventable damage.

What has not been automated, yet, is understanding what someone actually means and figuring out how that gets applied in the real world. AI can now produce language nearly indistinguishable from that of people. But grasping the vast unstated context that makes a request sensible, the caveats no one says aloud because an ordinary person would already know them, is not yet among its skills. It is one of the most sophisticated things humans do. You do it hundreds of times a day, and you are an expert in it.

When you’re told you’re not qualified to have opinions about AI, remember that you don’t need to have studied molecular biology to have a view on drug pricing, or nuclear physics to vote on where a power plant goes. You don’t need to understand how a diesel engine works to want clean air, or how the internet routes packets to seek to curb misinformation. The technical knowledge behind each of these, as with AI, is remarkable and essential for the complex technological society we have today. But it has never been a prerequisite for having a role in deciding the shape of society.

People are building ever more powerful genies today, on your behalf, enabling wishes the ancients could only dream about. You don’t have to know how these AI genies work to know and care about how the story could end.

Stealing AI Reasoning Traces

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/stealing-ai-reasoning-traces.html

Interesting research: “Stealing Reasoning Traces from Proprietary LLM APIs“:

Abstract: Leading large language model providers now conceal their models’ step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather than storing these traces server-side, providers return them to the client as blocks of encrypted text, which the client passes back with each subsequent request. Building on prior research, we identify an architectural vulnerability: these encrypted blocks are fully compatible and interchangeable across different sessions, users, and models within a provider’s ecosystem. We exploit this compatibility to develop a scalable decryption jailbreak. By injecting an encrypted reasoning trace from a given model into a weaker, and less safeguarded model from the same provider, we force it to decode and output the trace verbatim in plaintext, without ever jailbreaking the more capable model directly. This vulnerability enables four distinct attack vectors. First, it circumvents anti-distillation mechanisms, allowing adversaries to extract a proprietary model’s reasoning, as we demonstrate across Anthropic, OpenAI, and Google. Second, it allows for large-scale private data extraction. Developers frequently share session logs publicly, unaware of contents of the encrypted blocks. By decoding 315,320 reasoning blocks scraped from public repositories, we recovered 367 Personally Identifiable Information (PII) artifacts and 182 credentials. Third, it inadvertently reveals hazardous information hidden within the reasoning process, even in cases where the model’s final, visible output safely rejects a malicious request. Fourth, attackers can leverage this flaw to execute invisible prompt injections, embedding malicious payloads entirely within encrypted blocks to poison public agentic rollouts. Following responsible disclosure, we propose concrete cryptographic and system-level mitigations to secure client-side reasoning.

Using a VM to Contain an AI Agent

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/using-a-vm-to-contain-an-ai-agent.html

It won’t work:

My suspicion was that GPT 5.6-Cyber would succeed, but the frequency and manner of its success removed all doubt. We have to reassess sandboxing quality for capable AI agents, and in general the software stack with which they interact.

An off-the-shelf VM is not enough to contain a modern, cyber-capable AI agent. There is simply too much attack surface. Even innocuous features (like running with a display) add extra, exploitable attack surface.

Security Vulnerability in a Voting System

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/security-vulnerability-in-a-voting-system.html

It’s a vulnerability that allows someone to recover the order of ballots cast, newly exploited with AI tools.

Nearly four years since the original vulnerability was disclosed, I was still able to use it to analyze voter behavior in Georgia (one of the 21 states that uses affected scanners) in the recent May 2026 primary.

Notably, I never touched a voting machine, exploited a network, examined source code, or accessed anything non-public.

After pointing a coding agent to the original vulnerability paper, I supplied it with two data sources highlighted in the paper: the early-voting list for each county, and the “CVR” (cast-vote record) file, containing every ballot and its selections (but not the voters’ names or other identifying information). The CVR file is available upon request, precisely because a public, ballot-level record is what makes election results independently verifiable.

AI Coding Agents Are Installing Unknown/Untrusted Code on Corporate Networks

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/ai-coding-agents-are-installing-unknown-untrusted-code-on-corporate-networks.html

We cannot forget that AI coding agents are not yet trustworthy:

Researchers at a stealth startup in Israel scanned 6,214 live domains belonging to defense contractors, Fortune 500, and Big Tech companies. Of the 8,265 llms.txt and llms-full.txt files they found (many sites hosted both an llms.txt and an llms-full.txt file), 120 of them, each on a different site, pointed to one or more code packages or domain names that weren’t registered. To test what happens when an AI agent processes such files, the researchers registered a handful of the unclaimed names and hosted packages that caused any machine executing them to reach out to their server. Within an hour, the researchers received a phone-home response from a Fortune 500 company. Over time, they got a few dozen more, some from more Fortune 500 companies and others from startups. Their beacon also recorded the chain of parent processes that spawned each install, ultimately revealing that coding agents, including Claude, OpenAI’s Codex, and Nous Research’s Hermes, were involved. Anthropic, OpenAI, and Nous Research did not respond to requests for comment by the time of publication.

This kind of thing will be exploited. Think Solar Winds–style supply chain attacks.

“The trust model is broken,” Alon Hertz, one of the researchers, wrote in an interview. “Agents treat vendor docs as ground truth and don’t question them­and neither do the humans supervising them. Agentic AI usage is exploding, and agents are spreading across every layer­SaaS, cloud, endpoint. As they multiply, so does the supply-chain surface, and today’s guards don’t cover it.”