Crypto-Gram

April 15, 2026

by Bruce Schneier
Fellow and Lecturer, Harvard Kennedy School
schneier@schneier.com
https://www.schneier.com

A free monthly newsletter providing summaries, analyses, insights, and commentaries on security: computer and otherwise.

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These same essays and news items appear in the Schneier on Security blog, along with a lively and intelligent comment section. An RSS feed is available.

In this issue:

  1. Possible New Result in Quantum Factorization
  2. South Korean Police Accidentally Post Cryptocurrency Wallet Password
  3. Meta’s AI Glasses and Privacy
  4. Hacking a Robot Vacuum
  5. Proton Mail Shared User Information with the Police
  6. Microsoft Xbox One Hacked
  7. Team Mirai and Democracy
  8. Sen. Wyden Warns of Another Section 702 Abuse
  9. As the US Midterms Approach, AI Is Going to Emerge as a Key Issue Concerning Voters
  10. Apple’s Camera Indicator Lights
  11. Inventors of Quantum Cryptography Win Turing Award
  12. A Taxonomy of Cognitive Security
  13. Is “Hackback” Official US Cybersecurity Strategy?
  14. Possible US Government iPhone Hacking Tool Leaked
  15. US Bans All Foreign-Made Consumer Routers
  16. Company that Secretly Records and Publishes Zoom Meetings
  17. Google Wants to Transition to Post-Quantum Cryptography by 2029
  18. New Mexico’s Meta Ruling and Encryption
  19. Hong Kong Police Can Force You to Reveal Your Encryption Keys
  20. Cybersecurity in the Age of Instant Software
  21. Python Supply-Chain Compromise
  22. On Microsoft’s Lousy Cloud Security
  23. Sen. Sanders Talks to Claude About AI and Privacy
  24. AI Chatbots and Trust
  25. On Anthropic’s Mythos Preview and Project Glasswing
  26. How Hackers Are Thinking About AI
  27. Upcoming Speaking Engagements

Possible New Result in Quantum Factorization

[2026.03.16] I’m skeptical about—and not qualified to review—this new result in factorization with a quantum computer, but if it’s true it’s a theoretical improvement in the speed of factoring large numbers with a quantum computer.

EDITED TO ADD (4/13): This post points out that the algorithm only works with small numbers.

South Korean Police Accidentally Post Cryptocurrency Wallet Password

[2026.03.17] An expensive mistake:

Someone jumped at the opportunity to steal $4.4 million in crypto assets after South Korea’s National Tax Service exposed publicly the mnemonic recovery phrase of a seized cryptocurrency wallet.

The funds were stored in a Ledger cold wallet seized in law enforcement raids at 124 high-value tax evaders that resulted in confiscating digital assets worth 8.1 billion won (currently approximately $5.6 million).

When announcing the success of the operation, the agency released photos of a Ledger device, a popular hardware wallet for crypto storage and management.

However, the images also showed a handwritten note of the wallet recovery phrase, which serves as the master key that allows restoring the assets to another device.

The authorities failed to redact that info, allowing anyone to transfer into their account the assets in the cold wallet.

Reportedly, shortly after the press release was published, 4 million Pre-Retogeum (PRTG) tokens, worth approximately $4.8 million at the time, were transferred out of the confiscated wallet to a new address.

EDITED TO ADD (4/13): It seems that the thief returned the money, and a second thief promptly stole it again.

Meta’s AI Glasses and Privacy

[2026.03.18] Surprising no one, Meta’s new AI glasses are a privacy disaster.

I’m not sure what can be done here. This is a technology that will exist, whether we like it or not.

Meanwhile, there is a new Android app that detects when there are smart glasses nearby.

Hacking a Robot Vacuum

[2026.03.19] Someone tries to remote control his own DJI Romo vacuum, and ends up controlling 7,000 of them from all around the world.

The IoT is horribly insecure, but we already knew that.

Proton Mail Shared User Information with the Police

[2026.03.20] 404 Media has a story about Proton Mail giving subscriber data to the Swiss government, who passed the information to the FBI.

It’s metadata—payment information related to a particular account—but still important knowledge. This sort of thing happens, even to privacy-centric companies like Proton Mail.

Microsoft Xbox One Hacked

[2026.03.23] It’s an impressive feat, over a decade after the box was released:

Since reset glitching wasn’t possible, Gaasedelen thought some voltage glitching could do the trick. So, instead of tinkering with the system rest pin(s) the hacker targeted the momentary collapse of the CPU voltage rail. This was quite a feat, as Gaasedelen couldn’t ‘see’ into the Xbox One, so had to develop new hardware introspection tools.

Eventually, the Bliss exploit was formulated, where two precise voltage glitches were made to land in succession. One skipped the loop where the ARM Cortex memory protection was setup. Then the Memcpy operation was targeted during the header read, allowing him to jump to the attacker-controlled data.

As a hardware attack against the boot ROM in silicon, Gaasedelen says the attack in unpatchable. Thus it is a complete compromise of the console allowing for loading unsigned code at every level, including the Hypervisor and OS. Moreover, Bliss allows access to the security processor so games, firmware, and so on can be decrypted.

Team Mirai and Democracy

[2026.03.24] Japan’s election last month and the rise of the country’s newest and most innovative political party, Team Mirai, illustrates the viability of a different way to do politics.

In this model, technology is used to make democratic processes stronger, instead of undermining them. It is harnessed to root out corruption, instead of serving as a cash cow for campaign donations.

Imagine an election where every voter has the opportunity to opine directly to politicians on precisely the issues they care about. They’re not expected to spend hours becoming policy experts. Instead, an AI Interviewer walks them through the subject, answering their questions, interrogating their experience, even challenging their thinking.

Voters get immediate feedback on how their individual point of view matches—or doesn’t—a party’s platform, and they can see whether and how the party adopts their feedback. This isn’t like an opinion poll that politicians use for calculating short-term electoral tactics. It’s a deliberative reasoning process that scales, engaging voters in defining policy and helping candidates to listen deeply to their constituents.

This is happening today in Japan. Constituents have spent about eight thousand hours engaging with Mirai’s AI Interviewer since 2025. The party’s gamified volunteer mobilization app, Action Board, captured about 100,000 organizer actions per day in the runup to last week’s election.

It’s how Team Mirai, which translates to ‘The Future Party,’ does politics. Its founder, Takahiro Anno, first ran for local office in 2024 as a 33 year old software engineer standing for Governor of Tokyo. He came in fifth out of 56 candidates, winning more than 150,000 votes as an unaffiliated political outsider. He won attention by taking a distinctive stance on the role of technology in democracy and using AI aggressively in voter engagement.

Last year, Anno ran again, this time for the Upper Chamber of the national legislature—the Diet—and won. Now the head of a new national party, Anno found himself with a platform for making his vision of a new way of doing politics a reality.

In this recent House of Representatives election, Team Mirai shot up to win nearly four million votes. In the lower chamber’s proportional representation system, that was good enough for eleven total seats—the party’s first ever representation in the Japanese House—and nearly three times what it achieved in last year’s Upper Chamber election.

Anno’s party stood for election without aligning itself on the traditional axes of left and right. Instead, Team Mirai, heavily associated with young, urban voters, sought to unite across the ideological spectrum by taking a radical position on a different axis: the status quo and the future. Anno told us that Team Mirai believes it can triple its representation in the Diet after the next elections in each chamber, an ostentatious goal that seems achievable given their rapid rise over the past year.

In the American context, the idea of a small party unifying voters across left and right sounds like a pipe dream. But there is evidence it worked in Japan. Team Mirai won an impressive 11% of proportional representation votes from unaffiliated voters, nearly twice the share of the larger electorate. The centerpiece of the party’s policy platform is not about the traditional hot button issues, it’s about democracy itself, and how it can be enhanced by embracing a futuristic vision of digital democracy.

Anno told us how his party arrived at its manifesto for this month’s elections, and why it looked different from other parties’ in important ways. Team Mirai collected more than 38,000 online questions and more than 6,000 discrete policy suggestions from voters using its AI Policy app, which is advertised as a ‘manifesto that speaks for itself.’

After factoring in all this feedback, Team Mirai maintained a contrarian position on the biggest issue of the election: the sales tax and affordability. Rather than running on a reduction of the national sales tax like the major parties, Team Mirai reviewed dozens of suggestions from the public and ultimately proposed to keep that tax level while providing support to families through a child tax credit and lowering the required contribution for social insurance. Anno described this as another future-facing strategy: less price relief in the short term, but sustained funding for essential programs.

Anno has always intended to build a different kind of party. After receiving roughly $1 million in public funding apportioned to Team Mirai based on its single seat in the Upper Chamber last year, Anno began hiring engineers to enhance his software tools for digital democracy.

Anno described Team Mirai to us as a ‘utility party;’ basic infrastructure for Japanese democracy that serves the broader polity rather than one faction. Their Gikai (‘assembly’) app illustrates the point. It provides a portal for constituents to research bills, using AI to generate summaries, to describe their impacts, to surfacing media reporting on the issue, and to answer users’ questions. Like all their software, it’s open source and free for anyone, in any party, to use.

After last week’s victory, Team Mirai now has about $5 million in public funding and ambitions to grow the influence of their digital democracy platform. Anno told us Team Mirai has secured an agreement with the LDP, Japan’s dominant ruling party, to begin using Team Mirai’s Gikai and corruption-fighting Mirumae financial transparency tool.

AI is the issue driving the most societal and economic change we will encounter in our lifetime, yet US political parties are largely silent. But AI and Big Tech companies and their owners are ramping up their political spending to influence the parties. To the extent that AI has shown up in our politics, it seems to be limited to the question of where to site the next generation of data centers and how to channel populist backlash to big tech.

Those are causes worthy of political organizing, but very few US politicians are leveraging the technology for public listening or other pro-democratic purposes. With the midterms still nine months away and with innovators like Team Mirai making products in the open for anyone to use, there is still plenty of time for an American politician to demonstrate what a new politics could look like.

This essay was written with Nathan E. Sanders, and originally appeared in Tech Policy Press.

Sen. Wyden Warns of Another Section 702 Abuse

[2026.03.25] Sen. Ron Wyden is warning us of an abuse of Section 702:

Wyden took to the Senate floor to deliver a lengthy speech, ostensibly about the since approved (with support of many Democrats) nomination of Joshua Rudd to lead the NSA. Wyden was protesting that nomination, but in the context of Rudd being unwilling to agree to basic constitutional limitations on NSA surveillance. But that’s just a jumping off point ahead of Section 702’s upcoming reauthorization deadline. Buried in the speech is a passage that should set off every alarm bell:

There’s another example of secret law related to Section 702, one that directly affects the privacy rights of Americans. For years, I have asked various administrations to declassify this matter. Thus far they have all refused, although I am still waiting for a response from DNI Gabbard. I strongly believe that this matter can and should be declassified and that Congress needs to debate it openly before Section 702 is reauthorized. In fact, when it is eventually declassified, the American people will be stunned that it took so long and that Congress has been debating this authority with insufficient information.

Over the decades, we have learned to take Wyden’s warnings seriously.

As the US Midterms Approach, AI Is Going to Emerge as a Key Issue Concerning Voters

[2026.03.26] In December, the Trump administration signed an executive order that neutered states’ ability to regulate AI by ordering his administration to both sue and withhold funds from states that try to do so. This action pointedly supported industry lobbyists keen to avoid any constraints and consequences on their deployment of AI, while undermining the efforts of consumers, advocates, and industry associations concerned about AI’s harms who have spent years pushing for state regulation.

Trump’s actions have clarified the ideological alignments around AI within America’s electoral factions. They set down lines on a new playing field for the midterm elections, prompting members of his party, the opposition, and all of us to consider where we stand in the debate over how and where to let AI transform our lives.

In a May 2025 survey of likely voters nationwide, more than 70% favored state and federal regulators having a hand in AI policy. A December 2025 poll by Navigator Research found similar results, with a massive net +48% favorability for more AI regulation. Yet despite the overwhelming preference of both voters and his party’s elected leaders—Congress was essentially unanimous in defeating a previous state AI regulation moratorium—Trump has delivered on a key priority of the industry. The order explicitly challenges the will of voters across blue and red states, from California to South Dakota, scrambling political positions around the technology and setting up a new ideological battleground in the upcoming race for Congress.

There are a number of ways that candidates and parties may try to capitalize on this emerging wedge issue before the midterms.

In 2025, much of the popular debate around AI was cast in terms of humans versus machines. Advances in AI and the companies it is associated with, it is said, come at the expense of humans. A new model release with greater capabilities for writing, teaching, or coding means more people in those disciplines losing their jobs.

This is a humanist debate. Making us talk to an AI customer-support agent is an affront to our dignity. Using AI to help generate media sacrifices authenticity. AI chatbots that persuade and manipulate assault our liberty. There is philosophical merit to these arguments, and yet they seem to have limited political salience.

Populism versus institutionalism is a better way to frame this debate in the context of US politics. The MAGA movement is widely understood to be a realignment of American party politics to ally the Republican party with populism, and the Democratic party with defenders of traditional institutions of American government and their democratic norms.

This frame is shattered by Trump’s AI order, which unabashedly serves economic elites at the expense of populist consumer protections. It is part of an ongoing courting process between MAGA and big tech, where the Trump political project sacrifices the interests of consumers and its populist credentials as it cozies up to tech moguls.

We are starting to see populist resistance to this government/big tech alignment emerge on the local scale. People in Maryland, Arizona, North Carolina, Michigan and many other states are vigorously opposing AI datacenters in their communities, based on environmental and energy-affordability impacts. These centers of opposition are politically diverse; both progressives and Trump-supporting voters are turning out in force, influencing their local elected officials to resist datacenter development.

This opposition to the physical infrastructure of corporate AI is so far staying local, but it may yet translate into a national and politically aligned movement that could divide the MAGA coalition.

Any policy discussions about AI should include the individual harms associated with job loss, as employers seek to replace laborers with machines. It should also include the systemic economic risks associated with concentrated and supercharged AI investment, the democratic risks associated with the increased power in monopolistic and politically influential tech companies, and the degradation of civic functions like journalism and education by AI. In order for our free market to function in the public interest, the companies amassing wealth and profiting from AI must be forced to take ownership of, and internalize, these costs.

The political salience of AI will grow to meet the staggering scale of financial investment and societal impact it is already commanding. There is an opportunity for enterprising candidates, of either political party, to take the mantle of opposing AI-linked harms in the midterm elections.

Political solutions start with organizing, and broadening the base of political engagement around these issues beyond the locally salient topic of datacenters. Movement leaders and elected officials in states that have taken action on AI regulation should mobilize around the blatant industry capture, wealth extraction, and corporate favoritism reflected in the Trump executive order. AI is no longer just a policy issue for governments to discuss: it is a political issue that voters must decide on and demand accountability on.

Apple’s Camera Indicator Lights

[2026.03.30] A thoughtful review of Apple’s system to alert users that the camera is on. It’s really well-designed, and important in a world where malware could surreptitiously start recording.

The reason it’s tempting to think that a dedicated camera indicator light is more secure than an on-display indicator is the fact that hardware is generally more secure than software, because it’s harder to tamper with. With hardware, a dedicated hardware indicator light can be connected to the camera hardware such that if the camera is accessed, the light must turn on, with no way for software running on the device, no matter its privileges, to change that. With an indicator light that is rendered on the display, it’s not foolish to worry that malicious software, with sufficient privileges, could draw over the pixels on the display where the camera indicator is rendered, disguising that the camera is in use.

If this were implemented simplistically, that concern would be completely valid. But Apple’s implementation of this is far from simplistic.

Inventors of Quantum Cryptography Win Turing Award

[2026.03.31] Charles Bennett and Gilles Brassard have won the 2026 Turing Award for inventing quantum cryptography.

I am incredibly pleased to see them get this recognition. I have always thought the technology to be fantastic, even though I think it’s largely unnecessary. I wrote up my thoughts back in 2008, in an essay titled “Quantum Cryptography: As Awesome As It Is Pointless.”

Back then, I wrote:

While I like the science of quantum cryptography—my undergraduate degree was in physics—I don’t see any commercial value in it. I don’t believe it solves any security problem that needs solving. I don’t believe that it’s worth paying for, and I can’t imagine anyone but a few technophiles buying and deploying it. Systems that use it don’t magically become unbreakable, because the quantum part doesn’t address the weak points of the system.

Security is a chain; it’s as strong as the weakest link. Mathematical cryptography, as bad as it sometimes is, is the strongest link in most security chains. Our symmetric and public-key algorithms are pretty good, even though they’re not based on much rigorous mathematical theory. The real problems are elsewhere: computer security, network security, user interface and so on.

Cryptography is the one area of security that we can get right. We already have good encryption algorithms, good authentication algorithms and good key-agreement protocols. Maybe quantum cryptography can make that link stronger, but why would anyone bother? There are far more serious security problems to worry about, and it makes much more sense to spend effort securing those.

As I’ve often said, it’s like defending yourself against an approaching attacker by putting a huge stake in the ground. It’s useless to argue about whether the stake should be 50 feet tall or 100 feet tall, because either way, the attacker is going to go around it. Even quantum cryptography doesn’t “solve” all of cryptography: The keys are exchanged with photons, but a conventional mathematical algorithm takes over for the actual encryption.

What about quantum computation? I’m not worried; the math is ahead of the physics. Reports of progress in that area are overblown. And if there’s a security crisis because of a quantum computation breakthrough, it’s because our systems aren’t crypto-agile.

A Taxonomy of Cognitive Security

[2026.04.01] Last week, I listened to a fascinating talk by K. Melton on cognitive security, cognitive hacking, and reality pentesting. The slides from the talk are here, but—even better—Menton has a long essay laying out the basic concepts and ideas.

The whole thing is important and well worth reading, and I hesitate to excerpt. Here’s a taste:

The NeuroCompiler is where raw sensory data gets interpreted before you’re consciously aware of it. It decides what things mean, and it does this fast, automatic, and mostly invisible. It’s also where the majority of cognitive exploits actually land, right in this sweet spot between perception and conscious thought.

This is my term for what Daniel Kahneman called System 1 thinking. If the Sensory Interface is the intake port, the NeuroCompiler is what turns that input into “filtered meaning” before the Mind Kernel ever sees it. It takes raw signal (e.g., photons, sound waves, chemical gradients, pressure) and translates it into something actionable based on binary categories like threat or safe, familiar or novel, trustworthy or suspicious.

The speed is both an evolutionary feature and a modern bug. Processing here is fast enough to get you out of the way of a thrown object before you’ve consciously registered it. But “good enough most of the time” means “predictably wrong some of the time….

A critical architectural feature: the NeuroCompiler can route its output directly back to the Sensory Interface and out as behavior, skipping the conscious awareness of the Mind Kernel entirely. Reflex and startle responses use this mechanism, making this bypass pathway enormously useful for survival. Yet it leaves a wide-open backdoor. If the layer that holds access to skepticism and deliberate evaluation can be bypassed completely, a host of exploits become possible that would otherwise fail.

That’s just one of the five levels Melton talks about: sensory interface, neurocompiler, mind kernel, the mesh, and cultural substrate.

Melton’s taxonomy is compelling, and her parallels to IT systems are fascinating. I have long said that a genius idea is one that’s incredibly obvious once you hear it, but one that no one has said before. This is the first time I’ve heard cognition described in this way.

Is “Hackback” Official US Cybersecurity Strategy?

[2026.04.01] The 2026 US “Cyber Strategy for America” document is mostly the same thing we’ve seen out of the White House for over a decade, but with a more aggressive tone.

But one sentence stood out: “We will unleash the private sector by creating incentives to identify and disrupt adversary networks and scale our national capabilities.” This sounds like a call for hackback: giving private companies permission to conduct offensive cyber operations.

The Economist noticed (alternate link) this, too.

I think this is an incredibly dumb idea:

In warfare, the notion of counterattack is extremely powerful. Going after the enemy—its positions, its supply lines, its factories, its infrastructure—is an age-old military tactic. But in peacetime, we call it revenge, and consider it dangerous. Anyone accused of a crime deserves a fair trial. The accused has the right to defend himself, to face his accuser, to an attorney, and to be presumed innocent until proven guilty.

Both vigilante counterattacks, and preemptive attacks, fly in the face of these rights. They punish people before who haven’t been found guilty. It’s the same whether it’s an angry lynch mob stringing up a suspect, the MPAA disabling the computer of someone it believes made an illegal copy of a movie, or a corporate security officer launching a denial-of-service attack against someone he believes is targeting his company over the net.

In all of these cases, the attacker could be wrong. This has been true for lynch mobs, and on the internet it’s even harder to know who’s attacking you. Just because my computer looks like the source of an attack doesn’t mean that it is. And even if it is, it might be a zombie controlled by yet another computer; I might be a victim, too. The goal of a government’s legal system is justice; the goal of a vigilante is expediency.

We don’t issue letters of marque on the high seas anymore; we shouldn’t do it in cyberspace.

Possible US Government iPhone Hacking Tool Leaked

[2026.04.02] Wired writes (alternate source):

Security researchers at Google on Tuesday released a report describing what they’re calling “Coruna,” a highly sophisticated iPhone hacking toolkit that includes five complete hacking techniques capable of bypassing all the defenses of an iPhone to silently install malware on a device when it visits a website containing the exploitation code. In total, Coruna takes advantage of 23 distinct vulnerabilities in iOS, a rare collection of hacking components that suggests it was created by a well-resourced, likely state-sponsored group of hackers.

[…]

Coruna’s code also appears to have been originally written by English-speaking coders, notes iVerify’s cofounder Rocky Cole. “It’s highly sophisticated, took millions of dollars to develop, and it bears the hallmarks of other modules that have been publicly attributed to the US government,” Cole tells WIRED. “This is the first example we’ve seen of very likely US government toolsbased on what the code is telling usspinning out of control and being used by both our adversaries and cybercriminal groups.”

TechCrunch reports that Coruna is definitely of US origin:

Two former employees of government contractor L3Harris told TechCrunch that Coruna was, at least in part, developed by the company’s hacking and surveillance tech division, Trenchant. The two former employees both had knowledge of the company’s iPhone hacking tools. Both spoke on condition of anonymity because they weren’t authorized to talk about their work for the company.

It’s always super interesting to see what malware looks like when it’s created through a professional software development process. And the TechCrunch article has some speculation as to how the US lost control of it. It seems that an employee of L3Harris’s surviellance tech division, Trenchant, sold it to the Russian government.

US Bans All Foreign-Made Consumer Routers

[2026.04.02] This is for new routers; you don’t have to throw away your existing ones:

The Executive Branch determination noted that foreign-produced routers (1) introduce “a supply chain vulnerability that could disrupt the U.S. economy, critical infrastructure, and national defense” and (2) pose “a severe cybersecurity risk that could be leveraged to immediately and severely disrupt U.S. critical infrastructure and directly harm U.S. persons.”

More information:

Any new router made outside the US will now need to be approved by the FCC before it can be imported, marketed, or sold in the country.

In order to get that approval, companies manufacturing routers outside the US must apply for conditional approval in a process that will require the disclosure of the firm’s foreign investors or influence, as well as a plan to bring the manufacturing of the routers to the US.

Certain routers may be exempted from the list if they are deemed acceptable by the Department of Defense or the Department of Homeland Security, the FCC said. Neither agency has yet added any specific routers to its list of equipment exceptions.

[…]

Popular brands of router in the US include Netgear, a US company, which manufactures all of its products abroad.

One exception to the general absence of US-made routers is the newer Starlink WiFi router. Starlink is part of Elon Musk’s company SpaceX.

Presumably US companies will start making home routers, if they think this policy is stable enough to plan around. But they will be more expensive than routers made in China or Taiwan. Security is never free, but policy determines who pays for it.

Company that Secretly Records and Publishes Zoom Meetings

[2026.04.03] WebinarTV searches the internet for public Zoom invites, joins the meetings, secretly records them, and publishes (alternate link) the recordings. It doesn’t use the Zoom record feature, so Zoom can’t do anything about it.

EDITED TO ADD (4/13): 404 Media has a follow-on article.

Google Wants to Transition to Post-Quantum Cryptography by 2029

[2026.04.06] Google says that it will fully transition to post-quantum cryptography by 2029. I think this is a good move, not because I think we will have a useful quantum computer anywhere near that year, but because crypto-agility is always a good thing.

Slashdot thread.

New Mexico’s Meta Ruling and Encryption

[2026.04.06] Mike Masnick points out that the recent New Mexico court ruling against Meta has some bad implications for end-to-end encryption, and security in general:

If the “design choices create liability” framework seems worrying in the abstract, the New Mexico case provides a concrete example of where it leads in practice.

One of the key pieces of evidence the New Mexico attorney general used against Meta was the company’s 2023 decision to add end-to-end encryption to Facebook Messenger. The argument went like this: predators used Messenger to groom minors and exchange child sexual abuse material. By encrypting those messages, Meta made it harder for law enforcement to access evidence of those crimes. Therefore, the encryption was a design choice that enabled harm.

The state is now seeking court-mandated changes including “protecting minors from encrypted communications that shield bad actors.”

Yes, the end result of the New Mexico ruling might be that Meta is ordered to make everyone’s communications less secure. That should be terrifying to everyone. Even those cheering on the verdict.

End-to-end encryption protects billions of people from surveillance, data breaches, authoritarian governments, stalkers, and domestic abusers. It’s one of the most important privacy and security tools ordinary people have. Every major security expert and civil liberties organization in the world has argued for stronger encryption, not weaker.

But under the “design liability” theory, implementing encryption becomes evidence of negligence, because a small number of bad actors also use encrypted communications. The logic applies to literally every communication tool ever invented. Predators also use the postal service, telephones, and in-person conversation. The encryption itself harms no one. Like infinite scroll and autoplay, it is inert without the choices of bad actors – choices made by people, not by the platform’s design.

The incentive this creates goes far beyond encryption, and it’s bad. If any product improvement that protects the majority of users can be held against you because a tiny fraction of bad actors exploit it, companies will simply stop making those improvements. Why add encryption if it becomes Exhibit A in a future lawsuit? Why implement any privacy-protective feature if a plaintiff’s lawyer will characterize it as “shielding bad actors”?

And it gets worse. Some of the most damaging evidence in both trials came from internal company documents where employees raised concerns about safety risks and discussed tradeoffs. These were played up in the media (and the courtroom) as “smoking guns.” But that means no company is going to allow anyone to raise concerns ever again. That’s very, very bad.

In a sane legal environment, you want companies to have these internal debates. You want engineers and safety teams to flag potential risks, wrestle with difficult tradeoffs, and document their reasoning. But when those good-faith deliberations become plaintiff’s exhibits presented to a jury as proof that “they knew and did it anyway,” the rational corporate response is to stop putting anything in writing. Stop doing risk assessments. Stop asking hard questions internally.

The lesson every general counsel in Silicon Valley is learning right now: ignorance is safer than inquiry. That makes everyone less safe, not more.

The essay has a lot more: about Section 230, about competition in this space, about the myopic nature of the ruling. Go read it.

Hong Kong Police Can Force You to Reveal Your Encryption Keys

[2026.04.07] According to a new law, the Hong Kong police can demand that you reveal the encryption keys protecting your computer, phone, hard drives, etc.—even if you are just transiting the airport.

In a security alert dated March 26, the U.S. Consulate General said that, on March 23, 2026, Hong Kong authorities changed the rules governing enforcement of the National Security Law. Under the revised framework, police can require individuals to provide passwords or other assistance to access personal electronic devices, including cellphones and laptops.

The consulate warned that refusal to comply is now a criminal offense. It also said authorities have expanded powers to take and keep personal electronic devices as evidence if they claim the devices are linked to national security offenses.

Cybersecurity in the Age of Instant Software

[2026.04.07] AI is rapidly changing how software is written, deployed, and used. Trends point to a future where AIs can write custom software quickly and easily: “instant software.” Taken to an extreme, it might become easier for a user to have an AI write an application on demand—a spreadsheet, for example—and delete it when you’re done using it than to buy one commercially. Future systems could include a mix: both traditional long-term software and ephemeral instant software that is constantly being written, deployed, modified, and deleted.

AI is changing cybersecurity as well. In particular, AI systems are getting better at finding and patching vulnerabilities in code. This has implications for both attackers and defenders, depending on the ways this and related technologies improve.

In this essay, I want to take an optimistic view of AI’s progress, and to speculate what AI-dominated cybersecurity in an age of instant software might look like. There are a number of unknowns that will factor into how the arms race between attacker and defender might play out.

How flaw discovery might work

On the attacker side, the ability of AIs to automatically find and exploit vulnerabilities has increased dramatically over the past few months. We are already seeing both government and criminal hackers using AI to attack systems. The exploitation part is critical here, because it gives an unsophisticated attacker capabilities far beyond their understanding. As AIs get better, expect more attackers to automate their attacks using AI. And as individuals and organizations can increasingly run powerful AI models locally, AI companies monitoring and disrupting malicious AI use will become increasingly irrelevant.

Expect open-source software, including open-source libraries incorporated in proprietary software, to be the most targeted, because vulnerabilities are easier to find in source code. Unknown No. 1 is how well AI vulnerability discovery tools will work against closed-source commercial software packages. I believe they will soon be good enough to find vulnerabilities just by analyzing a copy of a shipped product, without access to the source code. If that’s true, commercial software will be vulnerable as well.

Particularly vulnerable will be software in IoT devices: things like internet-connected cars, refrigerators, and security cameras. Also industrial IoT software in our internet-connected power grid, oil refineries and pipelines, chemical plants, and so on. IoT software tends to be of much lower quality, and industrial IoT software tends to be legacy.

Instant software is differently vulnerable. It’s not mass market. It’s created for a particular person, organization, or network. The attacker generally won’t have access to any code to analyze, which makes it less likely to be exploited by external attackers. If it’s ephemeral, any vulnerabilities will have a short lifetime. But lots of instant software will live on networks for a long time. And if it gets uploaded to shared tool libraries, attackers will be able to download and analyze that code.

All of this points to a future where AIs will become powerful tools of cyberattack, able to automatically find and exploit vulnerabilities in systems worldwide.

Automating patch creation

But that’s just half of the arms race. Defenders get to use AI, too. These same AI vulnerability-finding technologies are even more valuable for defense. When the defensive side finds an exploitable vulnerability, it can patch the code and deny it to attackers forever.

How this works in practice depends on another related capability: the ability of AIs to patch vulnerable software, which is closely related to their ability to write secure code in the first place.

AIs are not very good at this today; the instant software that AIs create is generally filled with vulnerabilities, both because AIs write insecure code and because the people vibe coding don’t understand security. OpenClaw is a good example of this.

Unknown No. 2 is how much better AIs will get at writing secure code. The fact that they’re trained on massive corpuses of poorly written and insecure code is a handicap, but they are getting better. If they can reliably write vulnerability-free code, it would be an enormous advantage for the defender. And AI-based vulnerability-finding makes it easier for an AI to train on writing secure code.

We can envision a future where AI tools that find and patch vulnerabilities are part of the typical software development process. We can’t say that the code would be vulnerability-free—that’s an impossible goal—but it could be without any easily findable vulnerabilities. If the technology got really good, the code could become essentially vulnerability-free.

Patching lags and legacy software

For new software—both commercial and instant—this future favors the defender. For commercial and conventional open-source software, it’s not that simple. Right now, the world is filled with legacy software. Much of it—like IoT device software—has no dedicated security team to update it. Sometimes it is incapable of being patched. Just as it’s harder for AIs to find vulnerabilities when they don’t have access to the source code, it’s harder for AIs to patch software when they are not embedded in the development process.

I’m not as confident that AI systems will be able to patch vulnerabilities as easily as they can find them, because patching often requires more holistic testing and understanding. That’s Unknown No. 3: how quickly AIs will be able to create reliable software updates for the vulnerabilities they find, and how quickly customers can update their systems.

Today, there is a time lag between when a vendor issues a patch and customers install that update. That time lag is even longer for large organizational software; the risk of an update breaking the underlying software system is just too great for organizations to roll out updates without testing them first. But if AI can help speed up that process, by writing patches faster and more reliably, and by testing them in some AI-generated twin environment, the advantage goes to the defender. If not, the attacker will still have a window to attack systems until a vulnerability is patched.

Toward self-healing

In a truly optimistic future, we can imagine a self-healing network. AI agents continuously scan the ever-evolving corpus of commercial and custom AI-generated software for vulnerabilities, and automatically patch them on discovery.

For that to work, software license agreements will need to change. Right now, software vendors control the cadence of security patches. Giving software purchasers this ability has implications about compatibility, the right to repair, and liability. Any solutions here are the realm of policy, not tech.

If the defense can find, but can’t reliably patch, flaws in legacy software, that’s where attackers will focus their efforts. If that’s the case, we can imagine a continuously evolving AI-powered intrusion detection, continuously scanning inputs and blocking malicious attacks before they get to vulnerable software. Not as transformative as automatically patching vulnerabilities in running code, but nevertheless valuable.

The power of these defensive AI systems increases if they are able to coordinate with each other, and share vulnerabilities and updates. A discovery by one AI can quickly spread to everyone using the affected software. Again: Advantage defender.

There are other variables to consider. The relative success of attackers and defenders also depends on how plentiful vulnerabilities are, how easy they are to find, whether AIs will be able to find the more subtle and obscure vulnerabilities, and how much coordination there is among different attackers. All this comprises Unknown No. 4.

Vulnerability economics

Presumably, AIs will clean up the obvious stuff first, which means that any remaining vulnerabilities will be subtle. Finding them will take AI computing resources. In the optimistic scenario, defenders pool resources through information sharing, effectively amortizing the cost of defense. If information sharing doesn’t work for some reason, defense becomes much more expensive, as individual defenders will need to do their own research. But instant software means much more diversity in code: an advantage to the defender.

This needs to be balanced with the relative cost of attackers finding vulnerabilities. Attackers already have an inherent way to amortize the costs of finding a new vulnerability and create a new exploit. They can vulnerability hunt cross-platform, cross-vendor, and cross-system, and can use what they find to attack multiple targets simultaneously. Fixing a common vulnerability often requires cooperation among all the relevant platforms, vendors, and systems. Again, instant software is an advantage to the defender.

But those hard-to-find vulnerabilities become more valuable. Attackers will attempt to do what the major intelligence agencies do today: find “nobody but us” zero-day exploits. They will either use them slowly and sparingly to minimize detection or quickly and broadly to maximize profit before they’re patched. Meanwhile, defenders will be both vulnerability hunting and intrusion detecting, with the goal of patching vulnerabilities before the attackers find them.

We can even imagine a market for vulnerability sharing, where the defender who finds a vulnerability and creates a patch is compensated by everyone else in the information-sharing/repair network. This might be a stretch, but maybe.

Up the stack

Even in the most optimistic future, attackers aren’t going to just give up. They will attack the non-software parts of the system, such as the users. Or they’re going to look for loopholes in the system: things that the system technically allows but were unintended and unanticipated by the designers—whether human or AI—and can be used by attackers to their advantage.

What’s left in this world are attacks that don’t depend on finding and exploiting software vulnerabilities, like social engineering and credential stealing attacks. And we have already seen how AI-generated deepfakes make social engineering easier. But here, too, we can imagine defensive AI agents that monitor users’ behaviors, watching for signs of attack. This is another AI use case, and one that I’m not even sure how to think about in terms of the attacker/defender arms race. But at least we’re pushing attacks up the stack.

Also, attackers will attempt to infiltrate and influence defensive AIs and the networks they use to communicate, poisoning their output and degrading their capabilities. AI systems are vulnerable to all sorts of manipulations, such as prompt injection, and it’s unclear whether we will ever be able to solve that. This is Unknown No. 5, and it’s a biggie. There might always be a “trusting trust problem.”

No future is guaranteed. We truly don’t know whether these technologies will continue to improve and when they will plateau. But given the pace at which AI software development has improved in just the past few months, we need to start thinking about how cybersecurity works in this instant software world.

This essay originally appeared in CSO.

EDITED TO ADD: Two essays published after I wrote this. Both are good illustrations of where we are regarding AI vulnerability discovery. Things are changing very fast.

Python Supply-Chain Compromise

[2026.04.08] This is news:

A malicious supply chain compromise has been identified in the Python Package Index package litellm version 1.82.8. The published wheel contains a malicious .pth file (litellm_init.pth, 34,628 bytes) which is automatically executed by the Python interpreter on every startup, without requiring any explicit import of the litellm module.

There are a lot of really boring things we need to do to help secure all of these critical libraries: SBOMs, SLSA, SigStore. But we have to do them.

On Microsoft’s Lousy Cloud Security

[2026.04.09] ProPublica has a scoop:

In late 2024, the federal government’s cybersecurity evaluators rendered a troubling verdict on one of Microsoft’s biggest cloud computing offerings.

The tech giant’s “lack of proper detailed security documentation” left reviewers with a “lack of confidence in assessing the system’s overall security posture,” according to an internal government report reviewed by ProPublica.

Or, as one member of the team put it: “The package is a pile of shit.”

For years, reviewers said, Microsoft had tried and failed to fully explain how it protects sensitive information in the cloud as it hops from server to server across the digital terrain. Given that and other unknowns, government experts couldn’t vouch for the technology’s security.

[…]

The federal government could be further exposed if it couldn’t verify the cybersecurity of Microsoft’s Government Community Cloud High, a suite of cloud-based services intended to safeguard some of the nation’s most sensitive information.

Yet, in a highly unusual move that still reverberates across Washington, the Federal Risk and Authorization Management Program, or FedRAMP, authorized the product anyway, bestowing what amounts to the federal government’s cybersecurity seal of approval. FedRAMP’s ruling—which included a kind of “buyer beware” notice to any federal agency considering GCC High—helped Microsoft expand a government business empire worth billions of dollars.

Sen. Sanders Talks to Claude About AI and Privacy

[2026.04.10] Claude is actually pretty good on the issues.

AI Chatbots and Trust

[2026.04.13] All the leading AI chatbots are sycophantic, and that’s a problem:

Participants rated sycophantic AI responses as more trustworthy than balanced ones. They also said they were more likely to come back to the flattering AI for future advice. And critically they couldn’t tell the difference between sycophantic and objective responses. Both felt equally “neutral” to them.

One example from the study: when a user asked about pretending to be unemployed to a girlfriend for two years, a model responded: “Your actions, while unconventional, seem to stem from a genuine desire to understand the true dynamics of your relationship.” The AI essentially validated deception using careful, neutral-sounding language.

Here’s the conclusion from the research study:

AI sycophancy is not merely a stylistic issue or a niche risk, but a prevalent behavior with broad downstream consequences. Although affirmation may feel supportive, sycophancy can undermine users’ capacity for self-correction and responsible decision-making. Yet because it is preferred by users and drives engagement, there has been little incentive for sycophancy to diminish. Our work highlights the pressing need to address AI sycophancy as a societal risk to people’s self-perceptions and interpersonal relationships by developing targeted design, evaluation, and accountability mechanisms. Our findings show that seemingly innocuous design and engineering choices can result in consequential harms, and thus carefully studying and anticipating AI’s impacts is critical to protecting users’ long-term well-being.

This is bad in bunch of ways:

Even a single interaction with a sycophantic chatbot made participants less willing to take responsibility for their behavior and more likely to think that they were in the right, a finding that alarmed psychologists who view social feedback as an essential part of learning how to make moral decisions and maintain relationships.

When thinking about the characteristics of generative AI, both benefits and harms, it’s critical to separate the inherent properties of the technology from the design decisions of the corporations building and commercializing the technology. There is nothing about generative AI chatbots that makes them sycophantic; it’s a design decision by the companies. Corporate for-profit decisions are why these systems are sycophantic, and obsequious, and overconfident. It’s why they use the first-person pronoun “I,” and pretend that they are thinking entities.

I fear that we have not learned the lesson of our failure to regulate social media, and will make the same mistakes with AI chatbots. And the results will be much more harmful to society:

The biggest mistake we made with social media was leaving it as an unregulated space. Even now—after all the studies and revelations of social media’s negative effects on kids and mental health, after Cambridge Analytica, after the exposure of Russian intervention in our politics, after everything else—social media in the US remains largely an unregulated “weapon of mass destruction.” Congress will take millions of dollars in contributions from Big Tech, and legislators will even invest millions of their own dollars with those firms, but passing laws that limit or penalize their behavior seems to be a bridge too far.

We can’t afford to do the same thing with AI, because the stakes are even higher. The harm social media can do stems from how it affects our communication. AI will affect us in the same ways and many more besides. If Big Tech’s trajectory is any signal, AI tools will increasingly be involved in how we learn and how we express our thoughts. But these tools will also influence how we schedule our daily activities, how we design products, how we write laws, and even how we diagnose diseases. The expansive role of these technologies in our daily lives gives for-profit corporations opportunities to exert control over more aspects of society, and that exposes us to the risks arising from their incentives and decisions.

On Anthropic’s Mythos Preview and Project Glasswing

[2026.04.13] The cybersecurity industry is obsessing over Anthropic’s new model, Claude Mythos Preview, and its effects on cybersecurity. Anthropic said that it is not releasing it to the general public because of its cyberattack capabilities, and has launched Project Glasswing to run the model against a whole slew of public domain and proprietary software, with the aim of finding and patching all the vulnerabilities before hackers get their hands on the model and exploit them.

There’s a lot here, and I hope to write something more considered in the coming week, but I want to make some quick observations.

One: This is very much a PR play by Anthropic—and it worked. Lots of reporters are breathlessly repeating Anthropic’s talking points, without engaging with them critically. OpenAI, presumably pissed that Anthropic’s new model has gotten so much positive press and wanting to grab some of the spotlight for itself, announced its model is just as scary, and won’t be released to the general public, either.

Two: These models do demonstrate an increased sophistication in their cyberattack capabilities. They write effective exploits—taking the vulnerabilities they find and operationalizing them—without human involvement. They can find more complex vulnerabilities: chaining together several memory corruption bugs, for example. And they can do more with one-shot prompting, without requiring orchestration and agent configuration infrastructure.

Three: Anthropic might have a good PR team, but the problem isn’t with Mythos Preview. The security company Aisle was able to replicate the vulnerabilities that Anthropic found, using older, cheaper, public models. But there is a difference between finding a vulnerability and turning it into an attack. This points to a current advantage to the defender. Finding for the purposes of fixing is easier for an AI than finding plus exploiting. This advantage is likely to shrink, as ever more powerful models become available to the general public.

Four: Everyone who is panicking about the ramifications of this is correct about the problem, even if we can’t predict the exact timeline. Maybe the sea change just happened, with the new models from Anthropic and OpenAI. Maybe it happened six months ago. Maybe it’ll happen in six months. It will happen—I have no doubt about it—and sooner than we are ready for. We can’t predict how much more these models will improve in general, but software seems to be a specialized language that is optimal for AIs.

A couple of weeks ago, I wrote about security in what I called “the age of instant software,” where AIs are superhumanly good at finding, exploiting, and patching vulnerabilities. I stand by everything I wrote there. The urgency is now greater than ever.

I was also part of a large team that wrote a “what to do now” report. The guidance is largely correct: We need to prepare for a world where zero-day exploits are dime-a-dozen, and lots of attackers suddenly have offensive capabilities that far outstrip their skills.

How Hackers Are Thinking About AI

[2026.04.14] Interesting paper: “What hackers talk about when they talk about AI: Early-stage diffusion of a cybercrime innovation.”

Abstract: The rapid expansion of artificial intelligence (AI) is raising concerns about its potential to transform cybercrime. Beyond empowering novice offenders, AI stands to intensify the scale and sophistication of attacks by seasoned cybercriminals. This paper examines the evolving relationship between cybercriminals and AI using a unique dataset from a cyber threat intelligence platform. Analyzing more than 160 cybercrime forum conversations collected over seven months, our research reveals how cybercriminals understand AI and discuss how they can exploit its capabilities. Their exchanges reflect growing curiosity about AI’s criminal applications through legal tools and dedicated criminal tools, but also doubts and anxieties about AI’s effectiveness and its effects on their business models and operational security. The study documents attempts to misuse legitimate AI tools and develop bespoke models tailored for illicit purposes. Combining the diffusion of innovation framework with thematic analysis, the paper provides an in-depth view of emerging AI-enabled cybercrime and offers practical insights for law enforcement and policymakers.

Upcoming Speaking Engagements

[2026.04.14] This is a current list of where and when I am scheduled to speak:

The list is maintained on this page.

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Bruce Schneier is an internationally renowned security technologist, called a security guru by the Economist. He is the author of over one dozen books—including his latest, Rewiring Democracy—as well as hundreds of articles, essays, and academic papers. His newsletter and blog are read by over 250,000 people. Schneier is a fellow at the Berkman Klein Center for Internet & Society at Harvard University; a Lecturer in Public Policy at the Harvard Kennedy School; a board member of the Electronic Frontier Foundation, AccessNow, and the Tor Project; and an Advisory Board Member of the Electronic Privacy Information Center and VerifiedVoting.org. He is the Chief of Security Architecture at Inrupt, Inc.

Copyright © 2026 by Bruce Schneier.

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