Security & IT News

This is a current list of where and when I am scheduled to speak: I’m giving a virtual talk on “The Security of Trust in the Age of AI,” hosted by the Financial Women’s Association of New York , at 6:00 PM ET on May 21, 2026. I’m speaking at the Potsdam Conference on National Cybersecurity at the Hasso Plattner Institut in Potsdam, Germany. The event runs June 24–25, 2026, and my talk will be the evening of June 24. I’m speaking at the Digital Humanism Conference in Vienna, Austria, on Tuesday, June 26, 2026. I’m speaking at the Nuremberg Digital Festival in Nuremburg, Germany, on Wednesday, July 1, 2026. The list is maintained on this page .

Last month, Anthropic made a remarkable announcement about its new model, Claude Mythos Preview: it was so good at finding security vulnerabilities in software that the company would not release it to the general public. Instead, it would only be available to a select group of companies to scan and fix their own software. The announcement requires context—but it contained an essential truth. While Anthropic’s model is really good at finding software vulnerabilities, so are other models. The UK’s AI Security Institute found that OpenAI’s GPT-5.5, already generally available, is comparable in capability. The company Aisle reproduced Anthropic’s published results with smaller, cheaper models. At the same time, Anthropic’s refusal to publicly release its new model makes a virtue out of necessity. Mythos is very expensive to run, and the company doesn’t appear to have the resources for a general release. What better way to juice the company’s valuation than to hint at capabilities but not prove them, and then have others parrot their claims? Nonetheless, the truth is scary. Modern generative AI systems—not just Anthropic’s, but OpenAI’s and other, open-source models—are getting really good at finding and exploiting vulnerabilities in software. And that has important ramifications for cybersecurity: on both the offense and the defense. Attackers will use these capabilities to find, and automatically hack, vulnerabilities in systems of all kinds. They will be able to break into critical systems around the world, sometimes to plant ransomware and make money, sometimes to steal data for espionage purposes, and sometimes to control systems in times of hostility. This will make the world a much more dangerous, and more volatile, place. But at the same time, defenders will use these same capabilities to find, and then patch, many of those same systems. For example, Mozilla used Mythos to find 271 vulnerabilities in Firefox. Those vulnerabilities have been fixed, and will never again be available to attackers. In the future, AIs automatically finding and fixing vulnerabilities in all software will be a normal part of the development process, which will result in much more secure software. Of course, it’s not that simple. We should expect a deluge of both attackers using newly found vulnerabilities to break into systems, and at the same time much more frequent software updates for every app and device we use. But lots of systems aren’t patchable, and many systems that are don’t get patched, meaning that many vulnerabilities will stick around. And it does seem that finding and exploiting is easier than finding and fixing. All of this points to a more dangerous short-term future. Organizations will need to adapt their security to this new reality. But it’s the long term that we need to focus on. Mythos isn’t unique, but it’s more capable than many models t

Turns out that LLMs are really good at hiding text messages in other text messages.

Evidence of them has been found by analyzing DNA in the seawater. 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.

Insider trading is rife on Polymarket: Analysis by the Anti-Corruption Data Collective, a non-profit research and advocacy group, found that long-shot bets—­defined as wagers of $2,500 or more at odds of 35 percent or less—­on the platform had an average win rate of around 52 percent in markets on military and defense actions. That compares with a win rate of 25 percent across all politics-focused markets and just 14 percent for all markets on the platform as a whole. It is absolutely insane that this is legal. We already know how insider betting warps sports. Insider betting warping politics—and military actions—is orders of magnitude worse.

ICE is developing its own version of smart glasses, with facial recognition tied to various databases.

A new rowhammer attack gives complete control of NVIDIA CPUs. On Thursday, two research teams, working independently of each other, demonstrated attacks against two cards from Nvidia’s Ampere generation that take GPU rowhammering into new—­and potentially much more consequential—­territory: GDDR bitflips that give adversaries full control of CPU memory, resulting in full system compromise of the host machine. For the attack to work, IOMMU memory management must be disabled, as is the default in BIOS settings. “Our work shows that Rowhammer, which is well-studied on CPUs, is a serious threat on GPUs as well,” said Andrew Kwong, co-author of one of the papers. “ GDDRHammer: Greatly Disturbing DRAM Rows­Cross-Component Rowhammer Attacks from Modern GPUs .” “With our work, we… show how an attacker can induce bit flips on the GPU to gain arbitrary read/write access to all of the CPU’s memory, resulting in complete compromise of the machine.” Update Friday, April 3: On Friday, researchers unveiled a third Rowhammer attack that also demonstrates Rowhammer attacks on the RTX A6000 that achieves privilege escalation to a root shell. Unlike the previous two, the researchers said, it works even when IOMMU is enabled. The second paper is GeForge: Hammering GDDR Memory to Forge GPU Page Tables for Fun and Profit : …does largely the same thing, except that instead of exploiting the last-level page table, as GDDRHammer does, it manipulates the last-level page directory. It was able to induce 1,171 bitflips against the RTX 3060 and 202 bitflips against the RTX 6000. GeForge, too, uses novel hammering patterns and memory massaging to corrupt GPU page table mappings in GDDR6 memory to acquire read and write access to the GPU memory space. From there, it acquires the same privileges over host CPU memory. The GeForge proof-of-concept exploit against the RTX 3060 concludes by opening a root shell window that allows the attacker to issue commands that run unfettered privileges on the host machine. The researchers said that both GDDRHammer and GeForge could do the same thing against the RTC 6000.

Sent by a Spanish diplomat. Apparently people have been working on it since it was rediscovered in 1860.

Science news : Scientists have finally cracked a long-standing mystery about squid and cuttlefish evolution by analyzing newly sequenced genomes alongside global datasets. The research reveals that these bizarre, intelligent creatures likely originated deep in the ocean over 100 million years ago, surviving mass extinction events by retreating into oxygen-rich deep-sea refuges. For millions of years, their evolution barely changed—until a dramatic post-extinction boom sparked rapid diversification as they moved into new shallow-water habitats. 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.

It was used to track a Dutch naval ship: Dutch journalist Just Vervaart, working for regional media network Omroep Gelderland, followed the directions posted on the Dutch government website and mailed a postcard with a hidden tracker inside. Because of this, they were able to track the ship for about a day, watching it sail from Heraklion, Crete, before it turned towards Cyprus. While it only showed the location of that one vessel, knowing that it was part of a carrier strike group sailing in the Mediterranean could potentially put the entire fleet at risk. […] Navy officials reported that the tracker was discovered within 24 hours of the ship’s arrival, during mail sorting, and was eventually disabled. Because of this incident, the Dutch authorities now ban electronic greeting cards, which, unlike packages, weren’t x-rayed before being brought on the ship.

404 Media reports (alternate site ): The FBI was able to forensically extract copies of incoming Signal messages from a defendant’s iPhone, even after the app was deleted, because copies of the content were saved in the device’s push notification database…. The news shows how forensic extraction—­when someone has physical access to a device and is able to run specialized software on it—­can yield sensitive data derived from secure messaging apps in unexpected places. Signal already has a setting that blocks message content from displaying in push notifications; the case highlights why such a feature might be important for some users to turn on. “We learned that specifically on iPhones, if one’s settings in the Signal app allow for message notifications and previews to show up on the lock screen, [then] the iPhone will internally store those notifications/message previews in the internal memory of the device,” a supporter of the defendants who was taking notes during the trial told 404 Media.

Grupo Seguritech is a Mexican surveillance company that is expanding into the US.

The New York Times has a long article where the author lays out an impressive array of circumstantial evidence that the inventor of Bitcoin is the cypherpunk Adam Back. I don’t know. The article is convincing, but it’s written to be convincing. I can’t remember if I ever met Adam. I was a member of the Cypherpunks mailing list for a while, but I was never really an active participant. I spent more time on the Usenet newsgroup sci.crypt. I knew a bunch of the Cypherpunks, though, from various conferences around the world at the time. I really have no opinion about who Satoshi Nakamoto really is.

Pretty fantastic video from Japan of a giant squid eating another squid. 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.

Last week, Anthropic pulled back the curtain on Claude Mythos Preview , an AI model so capable at finding and exploiting software vulnerabilities that the company decided it was too dangerous to release to the public. Instead, access has been restricted to roughly 50 organizations—Microsoft, Apple, Amazon Web Services, CrowdStrike and other vendors of critical infrastructure—under an initiative called Project Glasswing . The announcement was accompanied by a barrage of hair-raising anecdotes: thousands of vulnerabilities uncovered across every major operating system and browser, including a 27-year-old bug in OpenBSD, a 16-year-old flaw in FFmpeg. Mythos was able to weaponize a set of vulnerabilities it found in the Firefox browser into 181 usable attacks; Anthropic’s previous flagship model could only achieve two. This is, in many respects, exactly the kind of responsible disclosure that security researchers have long urged. And yet the public has been given remarkably little with which to evaluate Anthropic’s decision. We have been shown a highlight reel of spectacular successes. However, we can’t tell if we have a blockbuster until they let us see the whole movie. For example, we don’t know how many times Mythos mistakenly flagged code as vulnerable. Anthropic said security contractors agreed with the AI’s severity rating 198 times, with an 89 per cent severity agreement. That’s impressive, but incomplete. Independent researchers examining similar models have found that AI that detects nearly every real bug also hallucinates plausible-sounding vulnerabilities in patched, correct code. This matters. A model that autonomously finds and exploits hundreds of vulnerabilities with inhuman precision is a game changer, but a model that generates thousands of false alarms and non-working attacks still needs skilled and knowledgeable humans. Without knowing the rate of false alarms in Mythos’s unfiltered output, we cannot tell whether the examples showcased are representative. There is a second, subtler problem. Large language models, including Mythos, perform best on inputs that resemble what they were trained on: widely used open-source projects, major browsers, the Linux kernel and popular web frameworks. Concentrating early access among the largest vendors of precisely this software is sensible; it lets them patch first, before adversaries catch up. But the inverse is also true. Software outside the training distribution—industrial control systems, medical device firmware, bespoke financial infrastructure, regional banking software, older embedded systems—is exactly where out-of-the-box Mythos is likely least able to find or exploit bugs. However, a sufficiently motivated attacker with domain expertise in one of these fields could nevertheless wield Mythos’s advanced reasoning capabilities as a force multiplier, probing systems that Anthropic’s own engineers lack the speci

Interesting research: “ Humans expect rationality and cooperation from LLM opponents in strategic games .” Abstract: As Large Language Models (LLMs) integrate into our social and economic interactions, we need to deepen our understanding of how humans respond to LLMs opponents in strategic settings. We present the results of the first controlled monetarily-incentivised laboratory experiment looking at differences in human behaviour in a multi-player p-beauty contest against other humans and LLMs. We use a within-subject design in order to compare behaviour at the individual level. We show that, in this environment, human subjects choose significantly lower numbers when playing against LLMs than humans, which is mainly driven by the increased prevalence of ‘zero’ Nash-equilibrium choices. This shift is mainly driven by subjects with high strategic reasoning ability. Subjects who play the zero Nash-equilibrium choice motivate their strategy by appealing to perceived LLM’s reasoning ability and, unexpectedly, propensity towards cooperation. Our findings provide foundational insights into the multi-player human-LLM interaction in simultaneous choice games, uncover heterogeneities in both subjects’ behaviour and beliefs about LLM’s play when playing against them, and suggest important implications for mechanism design in mixed human-LLM systems.

This article on the walls of Constantinople is fascinating. The system comprised four defensive lines arranged in formidable layers: The brick-lined ditch, divided by bulkheads and often flooded, 15­20 meters wide and up to 7 meters deep. A low breastwork, about 2 meters high, enabling defenders to fire freely from behind. The outer wall, 8 meters tall and 2.8 meters thick, with 82 projecting towers. The main wall—a towering 12 meters high and 5 meters thick—with 96 massive towers offset from those of the outer wall for maximum coverage. Behind the walls lay broad terraces: the parateichion, 18 meters wide, ideal for repelling enemies who crossed the moat, and the peribolos, 15–­20 meters wide between the inner and outer walls. From the moat’s bottom to the highest tower top, the defences reached nearly 30 meters—a nearly unscalable barrier of stone and ingenuity.

This is a current list of where and when I am scheduled to speak: I’m speaking at DemocracyXChange 2026 in Toronto, Ontario, Canada, on April 18, 2026. I’m speaking at the SANS AI Cybersecurity Summit 2026 in Arlington, Virginia, USA, at 9:40 AM ET on April 20, 2026. I’m speaking at the Greater Good Gathering in New York City, USA, on Tuesday, April 21, 2026. I’m speaking at the Nemertes [Next] Virtual Conference Spring 2026 , a virtual event, on April 29, 2026. I’m speaking at RightsCon 2026 in Lusaka, Zambia, on May 6 and 7, 2026. I’m giving a keynote address and participating in a panel discussion at an ICTLuxembourg event called “ Europe at the Crossroads of AI, Power the Future of Democracy .” The event will be held at the University of Luxembourg’s Belval Campus on May 12, 2026. I’m speaking at the Potsdam Conference on National Cybersecurity at the Hasso Plattner Institut in Potsdam, Germany. The event runs June 24–25, 2026, and my talk will be the evening of June 24. I’m speaking at the Digital Humanism Conference in Vienna, Austria, on Tuesday, June 26, 2026. I’m speaking at the Nuremberg Digital Festival in Nuremburg, Germany, on Wednesday, July 1, 2026. The list is maintained on this page .

OX Security recently analyzed 216 million security findings across 250 organizations over a 90-day period. The primary takeaway: while raw alert volume grew by 52% year-over-year, prioritized critical risk grew by nearly 400%. The surge in AI-assisted development is creating a "velocity gap" where the density of high-impact vulnerabilities is scaling faster than

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 w