cryptonews

Meta is suing NSO Group, basically claiming that the latter hacks WhatsApp and not just WhatsApp users. We have a procedural ruling:

Under the order, NSO Group is prohibited from presenting evidence about its customers’ identities, implying the targeted WhatsApp users are suspected or actual criminals, or alleging that WhatsApp had insufficient security protections.

[…]

In making her ruling, Northern District of California Judge Phyllis Hamilton said NSO Group undercut its arguments to use evidence about its customers with contradictory statements.

“Defendants cannot claim, on the one hand, that its intent is to help its clients fight terrorism and child exploitation, and on the other hand say that it has nothing to do with what its client does with the technology, other than advice and support,” she wrote. “Additionally, there is no evidence as to the specific kinds of crimes or security threats that its clients actually investigate and none with respect to the attacks at issue.”

I have written about the issues at play in this case.

This seems like an important advance in LLM security against prompt injection:

Google DeepMind has unveiled CaMeL (CApabilities for MachinE Learning), a new approach to stopping prompt-injection attacks that abandons the failed strategy of having AI models police themselves. Instead, CaMeL treats language models as fundamentally untrusted components within a secure software framework, creating clear boundaries between user commands and potentially malicious content.

[…]

To understand CaMeL, you need to understand that prompt injections happen when AI systems can’t distinguish between legitimate user commands and malicious instructions hidden in content they’re processing.

[…]

While CaMeL does use multiple AI models (a privileged LLM and a quarantined LLM), what makes it innovative isn’t reducing the number of models but fundamentally changing the security architecture. Rather than expecting AI to detect attacks, CaMeL implements established security engineering principles like capability-based access control and data flow tracking to create boundaries that remain effective even if an AI component is compromised.

Research paper. Good analysis by Simon Willison.

I wrote about the problem of LLMs intermingling the data and control paths here.

The company doesn’t keep logs, so couldn’t turn over data:

Windscribe, a globally used privacy-first VPN service, announced today that its founder, Yegor Sak, has been fully acquitted by a court in Athens, Greece, following a two-year legal battle in which Sak was personally charged in connection with an alleged internet offence by an unknown user of the service.

The case centred around a Windscribe-owned server in Finland that was allegedly used to breach a system in Greece. Greek authorities, in cooperation with INTERPOL, traced the IP address to Windscribe’s infrastructure and, unlike standard international procedures, proceeded to initiate criminal proceedings against Sak himself, rather than pursuing information through standard corporate channels.

Text “SQUID” to 1-833-SCI-TEXT for daily squid facts. The website has merch.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Long story of a $250 million cryptocurrency theft that, in a complicated chain events, resulted in a pretty brutal kidnapping.

Interesting:

The company has released a working rootkit called “Curing” that uses io_uring, a feature built into the Linux kernel, to stealthily perform malicious activities without being caught by many of the detection solutions currently on the market.

At the heart of the issue is the heavy reliance on monitoring system calls, which has become the go-to method for many cybersecurity vendors. The problem? Attackers can completely sidestep these monitored calls by leaning on io_uring instead. This clever method could let bad actors quietly make network connections or tamper with files without triggering the usual alarms.

Here’s the code.

Note the self-serving nature of this announcement: ARMO, the company that released the research and code, has a product that it claims blocks this kind of attack.

Interesting research: “Guillotine: Hypervisors for Isolating Malicious AIs.”

Abstract:As AI models become more embedded in critical sectors like finance, healthcare, and the military, their inscrutable behavior poses ever-greater risks to society. To mitigate this risk, we propose Guillotine, a hypervisor architecture for sandboxing powerful AI models—models that, by accident or malice, can generate existential threats to humanity. Although Guillotine borrows some well-known virtualization techniques, Guillotine must also introduce fundamentally new isolation mechanisms to handle the unique threat model posed by existential-risk AIs. For example, a rogue AI may try to introspect upon hypervisor software or the underlying hardware substrate to enable later subversion of that control plane; thus, a Guillotine hypervisor requires careful co-design of the hypervisor software and the CPUs, RAM, NIC, and storage devices that support the hypervisor software, to thwart side channel leakage and more generally eliminate mechanisms for AI to exploit reflection-based vulnerabilities. Beyond such isolation at the software, network, and microarchitectural layers, a Guillotine hypervisor must also provide physical fail-safes more commonly associated with nuclear power plants, avionic platforms, and other types of mission critical systems. Physical fail-safes, e.g., involving electromechanical disconnection of network cables, or the flooding of a datacenter which holds a rogue AI, provide defense in depth if software, network, and microarchitectural isolation is compromised and a rogue AI must be temporarily shut down or permanently destroyed.

The basic idea is that many of the AI safety policies proposed by the AI community lack robust technical enforcement mechanisms. The worry is that, as models get smarter, they will be able to avoid those safety policies. The paper proposes a set technical enforcement mechanisms that could work against these malicious AIs.

Android phones will soon reboot themselves after sitting idle for three days. iPhones have had this feature for a while; it’s nice to see Google add it to their phones.

A live colossal squid was filmed for the first time in the ocean. It’s only a juvenile: a foot long.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Discord is testing the feature:

“We’re currently running tests in select regions to age-gate access to certain spaces or user settings,” a spokesperson for Discord said in a statement. “The information shared to power the age verification method is only used for the one-time age verification process and is not stored by Discord or our vendor. For Face Scan, the solution our vendor uses operates on-device, which means there is no collection of any biometric information when you scan your face. For ID verification, the scan of your ID is deleted upon verification.”

I look forward to all the videos of people hacking this system using various disguises.

Mitre’s CVE’s program—which provides common naming and other informational resources about cybersecurity vulnerabilities—was about to be cancelled, as the US Department of Homeland Security failed to renew the contact. It was funded for eleven more months at the last minute.

This is a big deal. The CVE program is one of those pieces of common infrastructure that everyone benefits from. Losing it will bring us back to a world where there’s no single way to talk about vulnerabilities. It’s kind of crazy to think that the US government might damage its own security in this way—but I suppose no crazier than any of the other ways the US is working against its own interests right now.

Sasha Romanosky, senior policy researcher at the Rand Corporation, branded the end to the CVE program as “tragic,” a sentiment echoed by many cybersecurity and CVE experts reached for comment.

“CVE naming and assignment to software packages and versions are the foundation upon which the software vulnerability ecosystem is based,” Romanosky said. “Without it, we can’t track newly discovered vulnerabilities. We can’t score their severity or predict their exploitation. And we certainly wouldn’t be able to make the best decisions regarding patching them.”

Ben Edwards, principal research scientist at Bitsight, told CSO, “My reaction is sadness and disappointment. This is a valuable resource that should absolutely be funded, and not renewing the contract is a mistake.”

He added “I am hopeful any interruption is brief and that if the contract fails to be renewed, other stakeholders within the ecosystem can pick up where MITRE left off. The federated framework and openness of the system make this possible, but it’ll be a rocky road if operations do need to shift to another entity.”

More similar quotes in the article.

My guess is that we will somehow figure out how to continue this program without the US government. But a little notice would have been nice.

As AI coding assistants invent nonexistent software libraries to download and use, enterprising attackers create and upload libraries with those names—laced with malware, of course.

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

• I’m giving an online talk on AI and trust for the Weizenbaum Institute on April 24, 2025 at 2:00 PM CEST (8:00 AM ET).

The list is maintained on this page.

The Wall Street Journal has the story:

Chinese officials acknowledged in a secret December meeting that Beijing was behind a widespread series of alarming cyberattacks on U.S. infrastructure, according to people familiar with the matter, underscoring how hostilities between the two superpowers are continuing to escalate.

The Chinese delegation linked years of intrusions into computer networks at U.S. ports, water utilities, airports and other targets, to increasing U.S. policy support for Taiwan, the people, who declined to be named, said.

The admission wasn’t explicit:

The Chinese official’s remarks at the December meeting were indirect and somewhat ambiguous, but most of the American delegation in the room interpreted it as a tacit admission and a warning to the U.S. about Taiwan, a former U.S. official familiar with the meeting said.

No surprise.

Researchers are trying to use squid color-changing biochemistry for solar tech.

This appears to be new and related research to a 2019 squid post.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Microsoft is reporting that its AI systems are able to find new vulnerabilities in source code:

Microsoft discovered eleven vulnerabilities in GRUB2, including integer and buffer overflows in filesystem parsers, command flaws, and a side-channel in cryptographic comparison.

Additionally, 9 buffer overflows in parsing SquashFS, EXT4, CramFS, JFFS2, and symlinks were discovered in U-Boot and Barebox, which require physical access to exploit.

The newly discovered flaws impact devices relying on UEFI Secure Boot, and if the right conditions are met, attackers can bypass security protections to execute arbitrary code on the device.

Nothing major here. These aren’t exploitable out of the box. But that an AI system can do this at all is impressive, and I expect their capabilities to continue to improve.

Imagine that all of us—all of society—have landed on some alien planet and need to form a government: clean slate. We do not have any legacy systems from the United States or any other country. We do not have any special or unique interests to perturb our thinking. How would we govern ourselves? It is unlikely that we would use the systems we have today. Modern representative democracy was the best form of government that eighteenth-century technology could invent. The twenty-first century is very different: scientifically, technically, and philosophically. For example, eighteenth-century democracy was designed under the assumption that travel and communications were both hard.

Indeed, the very idea of representative government was a hack to get around technological limitations. Voting is easier now. Does it still make sense for all of us living in the same place to organize every few years and choose one of us to go to a single big room far away and make laws in our name? Representative districts are organized around geography because that was the only way that made sense two hundred-plus years ago. But we do not need to do it that way anymore. We could organize representation by age: one representative for the thirty-year-olds, another for the forty-year-olds, and so on. We could organize representation randomly: by birthday, perhaps. We can organize in any way we want. American citizens currently elect people to federal posts for terms ranging from two to six years. Would ten years be better for some posts? Would ten days be better for others? There are lots of possibilities. Maybe we can make more use of direct democracy by way of plebiscites. Certainly we do not want all of us, individually, to vote on every amendment to every bill, but what is the optimal balance between votes made in our name and ballot initiatives that we all vote on?

For the past three years, I have organized a series of annual two-day workshops to discuss these and other such questions.¹ For each event, I brought together fifty people from around the world: political scientists, economists, law professors, experts in artificial intelligence, activists, government types, historians, science-fiction writers, and more. We did not come up with any answers to our questions—and I would have been surprised if we had—but several themes emerged from the event. Misinformation and propaganda was a theme, of course, and the inability to engage in rational policy discussions when we cannot agree on facts. The deleterious effects of optimizing a political system for economic outcomes was another theme. Given the ability to start over, would anyone design a system of government for the near-term financial interest of the wealthiest few? Another theme was capitalism and how it is or is not intertwined with democracy. While the modern market economy made a lot of sense in the industrial age, it is starting to fray in the information age. What comes after capitalism, and how will it affect the way we govern ourselves?

Many participants examined the effects of technology, especially artificial intelligence (AI). We looked at whether—and when—we might be comfortable ceding power to an AI system. Sometimes deciding is easy. I am happy for an AI system to figure out the optimal timing of traffic lights to ensure the smoothest flow of cars through my city. When will we be able to say the same thing about the setting of interest rates? Or taxation? How would we feel about an AI device in our pocket that voted in our name, thousands of times per day, based on preferences that it inferred from our actions? Or how would we feel if an AI system could determine optimal policy solutions that balanced every voter’s preferences: Would it still make sense to have a legislature and representatives? Possibly we should vote directly for ideas and goals instead, and then leave the details to the computers.

These conversations became more pointed in the second and third years of our workshop, after generative AI exploded onto the internet. Large language models are poised to write laws, enforce both laws and regulations, act as lawyers and judges, and plan political strategy. How this capacity will compare to human expertise and capability is still unclear, but the technology is changing quickly and dramatically. We will not have AI legislators anytime soon, but just as today we accept that all political speeches are professionally written by speechwriters, will we accept that future political speeches will all be written by AI devices? Will legislators accept AI-written legislation, especially when that legislation includes a level of detail that human-based legislation generally does not? And if so, how will that change affect the balance of power between the legislature and the administrative state? Most interestingly, what happens when the AI tools we use to both write and enforce laws start to suggest policy options that are beyond human understanding? Will we accept them, because they work? Or will we reject a system of governance where humans are only nominally in charge?

Scale was another theme of the workshops. The size of modern governments reflects the technology at the time of their founding. European countries and the early American states are a particular size because that was a governable size in the eighteenth and nineteenth centuries. Larger governments—those of the United States as a whole and of the European Union—reflect a world where travel and communications are easier. Today, though, the problems we have are either local, at the scale of cities and towns, or global. Do we really have need for a political unit the size of France or Virginia? Or is it a mixture of scales that we really need, one that moves effectively between the local and the global?

As to other forms of democracy, we discussed one from history and another made possible by today’s technology. Sortition is a system of choosing political officials randomly. We use it today when we pick juries, but both the ancient Greeks and some cities in Renaissance Italy used it to select major political officials. Today, several countries—largely in Europe—are using the process to decide policy on complex issues. We might randomly choose a few hundred people, representative of the population, to spend a few weeks being briefed by experts, debating the issues, and then deciding on environmental regulations, or a budget, or pretty much anything.

“Liquid democracy” is a way of doing away with elections altogether. The idea is that everyone has a vote and can assign it to anyone they choose. A representative collects the proxies assigned to him or her and can either vote directly on the issues or assign all the proxies to someone else. Perhaps proxies could be divided: this person for economic matters, another for health matters, a third for national defense, and so on. In the purer forms of this system, people might transfer their votes to someone else at any time. There would be no more election days: vote counts might change every day.

And then, there is the question of participation and, more generally, whose interests are taken into account. Early democracies were really not democracies at all; they limited participation by gender, race, and land ownership. These days, to achieve a more comprehensive electorate we could lower the voting age. But, of course, even children too young to vote have rights, and in some cases so do other species. Should future generations be given a “voice,” whatever that means? What about nonhumans, or whole ecosystems? Should everyone have the same volume and type of voice? Right now, in the United States, the very wealthy have much more influence than others do. Should we encode that superiority explicitly? Perhaps younger people should have a more powerful vote than everyone else. Or maybe older people should.

In the workshops, those questions led to others about the limits of democracy. All democracies have boundaries limiting what the majority can decide. We are not allowed to vote Common Knowledge out of existence, for example, but can generally regulate speech to some degree. We cannot vote, in an election, to jail someone, but we can craft laws that make a particular action illegal. We all have the right to certain things that cannot be taken away from us. In the community of our future, what should be our rights as individuals? What should be the rights of society, superseding those of individuals?

Personally, I was most interested, at each of the three workshops, in how political systems fail. As a security technologist, I study how complex systems are subverted—hacked, in my parlance—for the benefit of a few at the expense of the many. Think of tax loopholes, or tricks to avoid government regulation. These hacks are common today, and AI tools will make them easier to find—and even to design—in the future. I would want any government system to be resistant to trickery. Or, to put it another way: I want the interests of each individual to align with the interests of the group at every level. We have never had a system of government with this property, but—in a time of existential risks such as climate change—it is important that we develop one.

Would this new system of government even be called “democracy”? I truly do not know.

Such speculation is not practical, of course, but still is valuable. Our workshops did not produce final answers and were not intended to do so. Our discourse was filled with suggestions about how to patch our political system where it is fraying. People regularly debate changes to the US Electoral College, or the process of determining voting districts, or the setting of term limits. But those are incremental changes. It is difficult to find people who are thinking more radically: looking beyond the horizon—not at what is possible today but at what may be possible eventually. Thinking incrementally is critically important, but it is also myopic. It represents a hill-climbing strategy of continuous but quite limited improvements. We also need to think about discontinuous changes that we cannot easily get to from here; otherwise, we may be forever stuck at local maxima. And while true innovation in politics is a lot harder than innovation in technology, especially without a violent revolution forcing changes on us, it is something that we as a species are going to have to get good at, one way or another.

Our workshop will reconvene for a fourth meeting in December 2025.

Neiman Lab has some good advice on how to leak a story to a journalist.

At a Congressional hearing earlier this week, Matt Blaze made the point that CALEA, the 1994 law that forces telecoms to make phone calls wiretappable, is outdated in today’s threat environment and should be rethought:

In other words, while the legally-mandated CALEA capability requirements have changed little over the last three decades, the infrastructure that must implement and protect it has changed radically. This has greatly expanded the “attack surface” that must be defended to prevent unauthorized wiretaps, especially at scale. The job of the illegal eavesdropper has gotten significantly easier, with many more options and opportunities for them to exploit. Compromising our telecommunications infrastructure is now little different from performing any other kind of computer intrusion or data breach, a well-known and endemic cybersecurity problem. To put it bluntly, something like Salt Typhoon was inevitable, and will likely happen again unless significant changes are made.

This is the access that the Chinese threat actor Salt Typhoon used to spy on Americans:

The Wall Street Journal first reported Friday that a Chinese government hacking group dubbed Salt Typhoon broke into three of the largest U.S. internet providers, including AT&T, Lumen (formerly CenturyLink), and Verizon, to access systems they use for facilitating customer data to law enforcement and governments. The hacks reportedly may have resulted in the “vast collection of internet traffic”; from the telecom and internet giants. CNN and The Washington Post also confirmed the intrusions and that the U.S. government’s investigation is in its early stages.

In “Secrets and Lies” (2000), I wrote:

It is poor civic hygiene to install technologies that could someday facilitate a police state.

It’s something a bunch of us were saying at the time, in reference to the vast NSA’s surveillance capabilities.

I have been thinking of that quote a lot as I read news stories of President Trump firing the Director of the National Security Agency. General Timothy Haugh.

A couple of weeks ago, I wrote:

We don’t know what pressure the Trump administration is using to make intelligence services fall into line, but it isn’t crazy to worry that the NSA might again start monitoring domestic communications.

The NSA already spies on Americans in a variety of ways. But that’s always been a sideline to its main mission: spying on the rest of the world. Once Trump replaces Haugh with a loyalist, the NSA’s vast surveillance apparatus can be refocused domestically.

Giving that agency all those powers in the 1990s, in the 2000s after the terrorist attacks of 9/11, and in the 2010s was always a mistake. I fear that we are about to learn how big a mistake it was.

Here’s PGP creator Phil Zimmerman in 1996, spelling it out even more clearly:

The Clinton Administration seems to be attempting to deploy and entrench a communications infrastructure that would deny the citizenry the ability to protect its privacy. This is unsettling because in a democracy, it is possible for bad people to occasionally get elected—sometimes very bad people. Normally, a well-functioning democracy has ways to remove these people from power. But the wrong technology infrastructure could allow such a future government to watch every move anyone makes to oppose it. It could very well be the last government we ever elect.

When making public policy decisions about new technologies for the government, I think one should ask oneself which technologies would best strengthen the hand of a police state. Then, do not allow the government to deploy those technologies. This is simply a matter of good civic hygiene.

The Brooklyn indie art-punk group, Two-Man Giant Squid, just released a new album.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

In case you need proof that anyone, even people who do cybersecurity for a living, Troy Hunt has a long, iterative story on his webpage about how he got phished. Worth reading.

If you’ve ever taken a computer security class, you’ve probably learned about the three legs of computer security—confidentiality, integrity, and availability—known as the CIA triad. When we talk about a system being secure, that’s what we’re referring to. All are important, but to different degrees in different contexts. In a world populated by artificial intelligence (AI) systems and artificial intelligent agents, integrity will be paramount.

What is data integrity? It’s ensuring that no one can modify data—that’s the security angle—but it’s much more than that. It encompasses accuracy, completeness, and quality of data—all over both time and space. It’s preventing accidental data loss; the “undo” button is a primitive integrity measure. It’s also making sure that data is accurate when it’s collected—that it comes from a trustworthy source, that nothing important is missing, and that it doesn’t change as it moves from format to format. The ability to restart your computer is another integrity measure.

The CIA triad has evolved with the Internet. The first iteration of the Web—Web 1.0 of the 1990s and early 2000s—prioritized availability. This era saw organizations and individuals rush to digitize their content, creating what has become an unprecedented repository of human knowledge. Organizations worldwide established their digital presence, leading to massive digitization projects where quantity took precedence over quality. The emphasis on making information available overshadowed other concerns.

As Web technologies matured, the focus shifted to protecting the vast amounts of data flowing through online systems. This is Web 2.0: the Internet of today. Interactive features and user-generated content transformed the Web from a read-only medium to a participatory platform. The increase in personal data, and the emergence of interactive platforms for e-commerce, social media, and online everything demanded both data protection and user privacy. Confidentiality became paramount.

We stand at the threshold of a new Web paradigm: Web 3.0. This is a distributed, decentralized, intelligent Web. Peer-to-peer social-networking systems promise to break the tech monopolies’ control on how we interact with each other. Tim Berners-Lee’s open W3C protocol, Solid, represents a fundamental shift in how we think about data ownership and control. A future filled with AI agents requires verifiable, trustworthy personal data and computation. In this world, data integrity takes center stage.

For example, the 5G communications revolution isn’t just about faster access to videos; it’s about Internet-connected things talking to other Internet-connected things without our intervention. Without data integrity, for example, there’s no real-time car-to-car communications about road movements and conditions. There’s no drone swarm coordination, smart power grid, or reliable mesh networking. And there’s no way to securely empower AI agents.

In particular, AI systems require robust integrity controls because of how they process data. This means technical controls to ensure data is accurate, that its meaning is preserved as it is processed, that it produces reliable results, and that humans can reliably alter it when it’s wrong. Just as a scientific instrument must be calibrated to measure reality accurately, AI systems need integrity controls that preserve the connection between their data and ground truth.

This goes beyond preventing data tampering. It means building systems that maintain verifiable chains of trust between their inputs, processing, and outputs, so humans can understand and validate what the AI is doing. AI systems need clean, consistent, and verifiable control processes to learn and make decisions effectively. Without this foundation of verifiable truth, AI systems risk becoming a series of opaque boxes.

Recent history provides many sobering examples of integrity failures that naturally undermine public trust in AI systems. Machine-learning (ML) models trained without thought on expansive datasets have produced predictably biased results in hiring systems. Autonomous vehicles with incorrect data have made incorrect—and fatal—decisions. Medical diagnosis systems have given flawed recommendations without being able to explain themselves. A lack of integrity controls undermines AI systems and harms people who depend on them.

They also highlight how AI integrity failures can manifest at multiple levels of system operation. At the training level, data may be subtly corrupted or biased even before model development begins. At the model level, mathematical foundations and training processes can introduce new integrity issues even with clean data. During execution, environmental changes and runtime modifications can corrupt previously valid models. And at the output level, the challenge of verifying AI-generated content and tracking it through system chains creates new integrity concerns. Each level compounds the challenges of the ones before it, ultimately manifesting in human costs, such as reinforced biases and diminished agency.

Think of it like protecting a house. You don’t just lock a door; you also use safe concrete foundations, sturdy framing, a durable roof, secure double-pane windows, and maybe motion-sensor cameras. Similarly, we need digital security at every layer to ensure the whole system can be trusted.

This layered approach to understanding security becomes increasingly critical as AI systems grow in complexity and autonomy, particularly with large language models (LLMs) and deep-learning systems making high-stakes decisions. We need to verify the integrity of each layer when building and deploying digital systems that impact human lives and societal outcomes.

At the foundation level, bits are stored in computer hardware. This represents the most basic encoding of our data, model weights, and computational instructions. The next layer up is the file system architecture: the way those binary sequences are organized into structured files and directories that a computer can efficiently access and process. In AI systems, this includes how we store and organize training data, model checkpoints, and hyperparameter configurations.

On top of that are the application layers—the programs and frameworks, such as PyTorch and TensorFlow, that allow us to train models, process data, and generate outputs. This layer handles the complex mathematics of neural networks, gradient descent, and other ML operations.

Finally, at the user-interface level, we have visualization and interaction systems—what humans actually see and engage with. For AI systems, this could be everything from confidence scores and prediction probabilities to generated text and images or autonomous robot movements.

Why does this layered perspective matter? Vulnerabilities and integrity issues can manifest at any level, so understanding these layers helps security experts and AI researchers perform comprehensive threat modeling. This enables the implementation of defense-in-depth strategies—from cryptographic verification of training data to robust model architectures to interpretable outputs. This multi-layered security approach becomes especially crucial as AI systems take on more autonomous decision-making roles in critical domains such as healthcare, finance, and public safety. We must ensure integrity and reliability at every level of the stack.

The risks of deploying AI without proper integrity control measures are severe and often underappreciated. When AI systems operate without sufficient security measures to handle corrupted or manipulated data, they can produce subtly flawed outputs that appear valid on the surface. The failures can cascade through interconnected systems, amplifying errors and biases. Without proper integrity controls, an AI system might train on polluted data, make decisions based on misleading assumptions, or have outputs altered without detection. The results of this can range from degraded performance to catastrophic failures.

We see four areas where integrity is paramount in this Web 3.0 world. The first is granular access, which allows users and organizations to maintain precise control over who can access and modify what information and for what purposes. The second is authentication—much more nuanced than the simple “Who are you?” authentication mechanisms of today—which ensures that data access is properly verified and authorized at every step. The third is transparent data ownership, which allows data owners to know when and how their data is used and creates an auditable trail of data providence. Finally, the fourth is access standardization: common interfaces and protocols that enable consistent data access while maintaining security.

Luckily, we’re not starting from scratch. There are open W3C protocols that address some of this: decentralized identifiers for verifiable digital identity, the verifiable credentials data model for expressing digital credentials, ActivityPub for decentralized social networking (that’s what Mastodon uses), Solid for distributed data storage and retrieval, and WebAuthn for strong authentication standards. By providing standardized ways to verify data provenance and maintain data integrity throughout its lifecycle, Web 3.0 creates the trusted environment that AI systems require to operate reliably. This architectural leap for integrity control in the hands of users helps ensure that data remains trustworthy from generation and collection through processing and storage.

Integrity is essential to trust, on both technical and human levels. Looking forward, integrity controls will fundamentally shape AI development by moving from optional features to core architectural requirements, much as SSL certificates evolved from a banking luxury to a baseline expectation for any Web service.

Web 3.0 protocols can build integrity controls into their foundation, creating a more reliable infrastructure for AI systems. Today, we take availability for granted; anything less than 100% uptime for critical websites is intolerable. In the future, we will need the same assurances for integrity. Success will require following practical guidelines for maintaining data integrity throughout the AI lifecycle—from data collection through model training and finally to deployment, use, and evolution. These guidelines will address not just technical controls but also governance structures and human oversight, similar to how privacy policies evolved from legal boilerplate into comprehensive frameworks for data stewardship. Common standards and protocols, developed through industry collaboration and regulatory frameworks, will ensure consistent integrity controls across different AI systems and applications.

Just as the HTTPS protocol created a foundation for trusted e-commerce, it’s time for new integrity-focused standards to enable the trusted AI services of tomorrow.

This essay was written with Davi Ottenheimer, and originally appeared in Communications of the ACM.

John Kelsey and I wrote a short paper for the Rossfest Festschrift: “Rational Astrologies and Security“:

There is another non-security way that designers can spend their security budget: on making their own lives easier. Many of these fall into the category of what has been called rational astrology. First identified by Randy Steve Waldman [Wal12], the term refers to something people treat as though it works, generally for social or institutional reasons, even when there’s little evidence that it works—­and sometimes despite substantial evidence that it does not.

[…]

Both security theater and rational astrologies may seem irrational, but they are rational from the perspective of the people making the decisions about security. Security theater is often driven by information asymmetry: people who don’t understand security can be reassured with cosmetic or psychological measures, and sometimes that reassurance is important. It can be better understood by considering the many non-security purposes of a security system. A monitoring bracelet system that pairs new mothers and their babies may be security theater, considering the incredibly rare instances of baby snatching from hospitals. But it makes sense as a security system designed to alleviate fears of new mothers [Sch07].

Rational astrologies in security result from two considerations. The first is the principal­-agent problem: The incentives of the individual or organization making the security decision are not always aligned with the incentives of the users of that system. The user’s well-being may not weigh as heavily on the developer’s mind as the difficulty of convincing his boss to take a chance by ignoring an outdated security rule or trying some new technology.

The second consideration that can lead to a rational astrology is where there is a social or institutional need for a solution to a problem for which there is actually not a particularly good solution. The organization needs to reassure regulators, customers, or perhaps even a judge and jury that “they did all that could be done” to avoid some problem—even if “all that could be done” wasn’t very much.

I have heard stories of more aggressive interrogation of electronic devices at US border crossings. I know a lot about securing computers, but very little about securing phones.

Are there easy ways to delete data—files, photos, etc.—on phones so it can’t be recovered? Does resetting a phone to factory defaults erase data, or is it still recoverable? That is, does the reset erase the old encryption key, or just sever the password that access that key? When the phone is rebooted, are deleted files still available?

We need answers for both iPhones and Android phones. And it’s not just the US; the world is going to become a more dangerous place to oppose state power.