cryptonews

I have no context for this video—it’s from Reddit—but one of the commenters adds some context:

Hey everyone, squid biologist here! Wanted to add some stuff you might find interesting.

With so many people carrying around cameras, we’re getting more videos of giant squid at the surface than in previous decades. We’re also starting to notice a pattern, that around this time of year (peaking in January) we see a bunch of giant squid around Japan. We don’t know why this is happening. Maybe they gather around there to mate or something? who knows! but since so many people have cameras, those one-off monster-story encounters are now caught on video, like this one (which, btw, rips. This squid looks so healthy, it’s awesome).

When we see big (giant or colossal) healthy squid like this, it’s often because a fisher caught something else (either another squid or sometimes an antarctic toothfish). The squid is attracted to whatever was caught and they hop on the hook and go along for the ride when the target species is reeled in. There are a few colossal squid sightings similar to this from the southern ocean (but fewer people are down there, so fewer cameras, fewer videos). On the original instagram video, a bunch of people are like “Put it back! Release him!” etc, but he’s just enjoying dinner (obviously as the squid swims away at the end).

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

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The promise of personal AI assistants rests on a dangerous assumption: that we can trust systems we haven’t made trustworthy. We can’t. And today’s versions are failing us in predictable ways: pushing us to do things against our own best interests, gaslighting us with doubt about things we are or that we know, and being unable to distinguish between who we are and who we have been. They struggle with incomplete, inaccurate, and partial context: with no standard way to move toward accuracy, no mechanism to correct sources of error, and no accountability when wrong information leads to bad decisions.

These aren’t edge cases. They’re the result of building AI systems without basic integrity controls. We’re in the third leg of data security—the old CIA triad. We’re good at availability and working on confidentiality, but we’ve never properly solved integrity. Now AI personalization has exposed the gap by accelerating the harms.

The scope of the problem is large. A good AI assistant will need to be trained on everything we do and will need access to our most intimate personal interactions. This means an intimacy greater than your relationship with your email provider, your social media account, your cloud storage, or your phone. It requires an AI system that is both discreet and trustworthy when provided with that data. The system needs to be accurate and complete, but it also needs to be able to keep data private: to selectively disclose pieces of it when required, and to keep it secret otherwise. No current AI system is even close to meeting this.

To further development along these lines, I and others have proposed separating users’ personal data stores from the AI systems that will use them. It makes sense; the engineering expertise that designs and develops AI systems is completely orthogonal to the security expertise that ensures the confidentiality and integrity of data. And by separating them, advances in security can proceed independently from advances in AI.

What would this sort of personal data store look like? Confidentiality without integrity gives you access to wrong data. Availability without integrity gives you reliable access to corrupted data. Integrity enables the other two to be meaningful. Here are six requirements. They emerge from treating integrity as the organizing principle of security to make AI trustworthy.

First, it would be broadly accessible as a data repository. We each want this data to include personal data about ourselves, as well as transaction data from our interactions. It would include data we create when interacting with others—emails, texts, social media posts—and revealed preference data as inferred by other systems. Some of it would be raw data, and some of it would be processed data: revealed preferences, conclusions inferred by other systems, maybe even raw weights in a personal LLM.

Second, it would be broadly accessible as a source of data. This data would need to be made accessible to different LLM systems. This can’t be tied to a single AI model. Our AI future will include many different models—some of them chosen by us for particular tasks, and some thrust upon us by others. We would want the ability for any of those models to use our data.

Third, it would need to be able to prove the accuracy of data. Imagine one of these systems being used to negotiate a bank loan, or participate in a first-round job interview with an AI recruiter. In these instances, the other party will want both relevant data and some sort of proof that the data are complete and accurate.

Fourth, it would be under the user’s fine-grained control and audit. This is a deeply detailed personal dossier, and the user would need to have the final say in who could access it, what portions they could access, and under what circumstances. Users would need to be able to grant and revoke this access quickly and easily, and be able to go back in time and see who has accessed it.

Fifth, it would be secure. The attacks against this system are numerous. There are the obvious read attacks, where an adversary attempts to learn a person’s data. And there are also write attacks, where adversaries add to or change a user’s data. Defending against both is critical; this all implies a complex and robust authentication system.

Sixth, and finally, it must be easy to use. If we’re envisioning digital personal assistants for everybody, it can’t require specialized security training to use properly.

I’m not the first to suggest something like this. Researchers have proposed a “Human Context Protocol” (https://papers.ssrn.com/sol3/ papers.cfm?abstract_id=5403981) that would serve as a neutral interface for personal data of this type. And in my capacity at a company called Inrupt, Inc., I have been working on an extension of Tim Berners-Lee’s Solid protocol for distributed data ownership.

The engineering expertise to build AI systems is orthogonal to the security expertise needed to protect personal data. AI companies optimize for model performance, but data security requires cryptographic verification, access control, and auditable systems. Separating the two makes sense; you can’t ignore one or the other.

Fortunately, decoupling personal data stores from AI systems means security can advance independently from performance (https:// ieeexplore.ieee.org/document/ 10352412). When you own and control your data store with high integrity, AI can’t easily manipulate you because you see what data it’s using and can correct it. It can’t easily gaslight you because you control the authoritative record of your context. And you determine which historical data are relevant or obsolete. Making this all work is a challenge, but it’s the only way we can have trustworthy AI assistants.

This essay was originally published in IEEE Security & Privacy.

I have long maintained that smart contracts are a dumb idea: that a human process is actually a security feature.

Here’s some interesting research on training AIs to automatically exploit smart contracts:

AI models are increasingly good at cyber tasks, as we’ve written about before. But what is the economic impact of these capabilities? In a recent MATS and Anthropic Fellows project, our scholars investigated this question by evaluating AI agents’ ability to exploit smart contracts on Smart CONtracts Exploitation benchmark (SCONE-bench)­a new benchmark they built comprising 405 contracts that were actually exploited between 2020 and 2025. On contracts exploited after the latest knowledge cutoffs (June 2025 for Opus 4.5 and March 2025 for other models), Claude Opus 4.5, Claude Sonnet 4.5, and GPT-5 developed exploits collectively worth $4.6 million, establishing a concrete lower bound for the economic harm these capabilities could enable. Going beyond retrospective analysis, we evaluated both Sonnet 4.5 and GPT-5 in simulation against 2,849 recently deployed contracts without any known vulnerabilities. Both agents uncovered two novel zero-day vulnerabilities and produced exploits worth $3,694, with GPT-5 doing so at an API cost of $3,476. This demonstrates as a proof-of-concept that profitable, real-world autonomous exploitation is technically feasible, a finding that underscores the need for proactive adoption of AI for defense.

The FBI is warning of AI-assisted fake kidnapping scams:

Criminal actors typically will contact their victims through text message claiming they have kidnapped their loved one and demand a ransom be paid for their release. Oftentimes, the criminal actor will express significant claims of violence towards the loved one if the ransom is not paid immediately. The criminal actor will then send what appears to be a genuine photo or video of the victim’s loved one, which upon close inspection often reveals inaccuracies when compared to confirmed photos of the loved one. Examples of these inaccuracies include missing tattoos or scars and inaccurate body proportions. Criminal actors will sometimes purposefully send these photos using timed message features to limit the amount of time victims have to analyze the images.

Images, videos, audio: It can all be faked with AI. My guess is that this scam has a low probability of success, so criminals will be figuring out how to automate it.

Two competing arguments are making the rounds. The first is by a neurosurgeon in the New York Times. In an op-ed that honestly sounds like it was paid for by Waymo, the author calls driverless cars a “public health breakthrough”:

In medical research, there’s a practice of ending a study early when the results are too striking to ignore. We stop when there is unexpected harm. We also stop for overwhelming benefit, when a treatment is working so well that it would be unethical to continue giving anyone a placebo. When an intervention works this clearly, you change what you do.

There’s a public health imperative to quickly expand the adoption of autonomous vehicles. More than 39,000 Americans died in motor vehicle crashes last year, more than homicide, plane crashes and natural disasters combined. Crashes are the No. 2 cause of death for children and young adults. But death is only part of the story. These crashes are also the leading cause of spinal cord injury. We surgeons see the aftermath of the 10,000 crash victims who come to emergency rooms every day.

The other is a soon-to-be-published book: Driving Intelligence: The Green Book. The authors, a computer scientist and a management consultant with experience in the industry, make the opposite argument. Here’s one of the authors:

There is something very disturbing going on around trials with autonomous vehicles worldwide, where, sadly, there have now been many deaths and injuries both to other road users and pedestrians. Although I am well aware that there is not, senso stricto, a legal and functional parallel between a “drug trial” and “AV testing,” it seems odd to me that if a trial of a new drug had resulted in so many deaths, it would surely have been halted and major forensic investigations carried out and yet, AV manufacturers continue to test their products on public roads unabated.

I am not convinced that it is good enough to argue from statistics that, to a greater or lesser degree, fatalities and injuries would have occurred anyway had the AVs had been replaced by human-driven cars: a pharmaceutical company, following death or injury, cannot simply sidestep regulations around the trial of, say, a new cancer drug, by arguing that, whilst the trial is underway, people would die from cancer anyway….

Both arguments are compelling, and it’s going to be hard to figure out what public policy should be.

This paper, from 2016, argues that we’re going to need other metrics than side-by-side comparisons: Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?“:

Abstract: How safe are autonomous vehicles? The answer is critical for determining how autonomous vehicles may shape motor vehicle safety and public health, and for developing sound policies to govern their deployment. One proposed way to assess safety is to test drive autonomous vehicles in real traffic, observe their performance, and make statistical comparisons to human driver performance. This approach is logical, but it is practical? In this paper, we calculate the number of miles of driving that would be needed to provide clear statistical evidence of autonomous vehicle safety. Given that current traffic fatalities and injuries are rare events compared to vehicle miles traveled, we show that fully autonomous vehicles would have to be driven hundreds of millions of miles and sometimes hundreds of billions of miles to demonstrate their reliability in terms of fatalities and injuries. Under even aggressive testing assumptions, existing fleets would take tens and sometimes hundreds of years to drive these miles—­an impossible proposition if the aim is to demonstrate their performance prior to releasing them on the roads for consumer use. These findings demonstrate that developers of this technology and third-party testers cannot simply drive their way to safety. Instead, they will need to develop innovative methods of demonstrating safety and reliability. And yet, the possibility remains that it will not be possible to establish with certainty the safety of autonomous vehicles. Uncertainty will remain. Therefore, it is imperative that autonomous vehicle regulations are adaptive­—designed from the outset to evolve with the technology so that society can better harness the benefits and manage the risks of these rapidly evolving and potentially transformative technologies.

One problem, of course, is that we treat death by human driver differently than we do death by autonomous computer driver. This is likely to change as we get more experience with AI accidents—and AI-caused deaths.

Here’s a fun paper: “The Naibbe cipher: a substitution cipher that encrypts Latin and Italian as Voynich Manuscript-like ciphertext“:

Abstract: In this article, I investigate the hypothesis that the Voynich Manuscript (MS 408, Yale University Beinecke Library) is compatible with being a ciphertext by attempting to develop a historically plausible cipher that can replicate the manuscript’s unusual properties. The resulting cipher­a verbose homophonic substitution cipher I call the Naibbe cipher­can be done entirely by hand with 15th-century materials, and when it encrypts a wide range of Latin and Italian plaintexts, the resulting ciphertexts remain fully decipherable and also reliably reproduce many key statistical properties of the Voynich Manuscript at once. My results suggest that the so-called “ciphertext hypothesis” for the Voynich Manuscript remains viable, while also placing constraints on plausible substitution cipher structures.

The vampire squid (Vampyroteuthis infernalis) has the largest cephalopod genome ever sequenced: more than 11 billion base pairs. That’s more than twice as large as the biggest squid genomes.

It’s technically not a squid: “The vampire squid is a fascinating twig tenaciously hanging onto the cephalopod family tree. It’s neither a squid nor an octopus (nor a vampire), but rather the last, lone remnant of an ancient lineage whose other members have long since vanished.”

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

Blog moderation policy.