Friday Squid Blogging: Another Squid
Someone named “Squid” seems to be a “West Country legend.”
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
Blog: May 2026 Archives
Chilling Effects
Younger Americans have soured on the second Donald Trump presidency, but they are not protesting it.
Despite an unpopular Iran war and an even more unpopular Trump administration, college campus protests nationwide have gone silent. And at many schools, student activism is virtually nonexistent.
This silence comes in the wake of a relentless Trump administration war on campus speech that has involved lawsuits, arrests, deportations and expulsions.
Reports cite a range of complicated factors for the restraint, from apathy to technology-induced incapacity. But as public policy and law and social science experts, we believe students aren’t protesting for a very simple reason: They are afraid. They are self-censoring and disengaging from campaign activism to avoid punitive measures.
In law and social science, we call this impact a chilling effect—the behavioral tendency for people in face of a threat to self-censor and restrain their activities for self-protection.
It’s increasingly clear to us that these impacts are not incidental or ancillary to Trump administration policy. Rather, the chilling effects are the point. This is the closest thing to a consistent governing strategy in Trump’s second term.
The broader chill of Trump threats
Chilling effects can be subtle, but today they are everywhere. And it’s not just students who are chilled by Trump administration threats.
Professors are censoring themselves in lectures and rewriting syllabuses. Researchers are stripping grant applications of words that might attract federal scrutiny, or abandoning the topics entirely. Media outlets are modifying their news coverage to avoid Trump lawsuits or sanctions.
Law enforcement and regulatory agencies are refusing to investigate Trump-aligned actors inside or outside government, and major national law firms are declining cases challenging Trump administration policies.
Publishers are “stepping back” from LGBTQ+ books and other progressive subjects. Many in targeted immigrant communities are afraid to leave home to go to work or school.
In most cases, these people and institutions are not being specifically targeted or threatened by Trump. But they are afraid, and their fear is doing the administration’s work for it. They stay silent, avoid attention and confrontation, and look the other way. In other cases, they change their speech and behavior to accommodate or conform to the administration’s worldview.
Of course, there are counterexamples, such as the winter protests in Minneapolis in response to brutality by agents with U.S. Immigration and Customs Enforcement, and the recent “No Kings” rallies. But even here, the broader but less visible trend—chilling effects—is evident.
For instance, in recent reporting on the latest No Kings rallies, many media outlets observed that students were noticeably missing, despite the Trump administration’s unpopularity among younger Americans.
A persistent strategy
We believe none of this is by accident.
In a new book, “Chilling Effects: Repression, Conformity, and Power in the Digital Age,” one of us—Jon Penney—explains how law, technology, and state and corporate power are weaponized to chill and repress, and the dangers this poses for the United States and other democratic societies. The other—Bruce Schneier—has extensively studied the security infrastructure enabling this.
What we see isn’t gratuitous government cruelty, chaos or vengeance. Instead, we see a persistent strategy to maximize fear and chilling effects in ways that are corrosive to freedom and democracy.
Research suggests that surveillance, personal threats, uncertainty and abuse of power are key factors in doing so. The federal government has a clear and systematic pattern of employing these very mechanisms across a number of domains far beyond campuses.
They are evident in militarized raids by Immigration and Customs Enforcement and in journalists being arrested and indicted for reporting on protests. They are made clear in the long list of political enemies the Trump administration has investigated or threatened, including the Federal Reserve chairman. And they can also be seen in the weaponization of technology, including ramping up surveillance to target critics and protestors.
Corrosive to freedom and democracy
History offers some guidance on impacts.
During the McCarthy era, overreaching laws, surveillance, and public and private sector reprisals ostensibly targeted alleged communists. But the real aim was often to suppress progressive journalists, trade unions and political opposition.
In the 1960s, these same tactics were reused by Southern states to chill the Civil Rights Movement. Historians have written about how the widespread fear and conformity of these periods reshaped American society in enduring ways, including the destruction of progressive political movements and both delaying and muting the Civil Rights Movement itself.
When such state threats are systematized, they can foment a broader climate of fear, self-censorship and conformity. In that climate, dissenting speech, political opposition, democratic mobilization and other checks on power become increasingly difficult, even dangerous. It is no surprise, for instance, that Trump critics regularly admit to self-censorship, fearing for their safety.
Chilling effects are thus not only repressive—causing self-censorship—but productive. They produce conforming and compliant speech and behavior, which can have longer-term social impacts. They not only undermine protected rights and suppress accountability but can promote social change—even without a popular mandate to do so.
This latter point is often missed. It explains Trump’s assaults on universities and cultural institutions such as the Kennedy Center for the Arts and the Smithsonian. Often dismissed as peculiar Trump obsessions, they are fully consistent with Project 2025—the sweeping policy blueprint for Trump’s second term authored by a coalition of conservative groups and its call to target the “institutions of American civil society” and “wield federal power” to “reverse” decades of progressive cultural advancements.
In the near term, this means an increasingly weakened democratic society, with the government and its patrons enjoying freedom to pursue their objectives. Over the long term, this can mean a changed society as more conformist and compliant speech and culture become more widely accepted and entrenched.
Not inevitable
In our view, this future is not inevitable, just as the McCarthy era “Red Scare” and violent civil rights era repression were not. In both cases, fear and chilling effects were resisted in law and civil society, as they can be today.
But the central mechanisms—surveillance, uncertainty, personal threats and abuse of power—would need to be addressed. For instance, new legislation could ensure justice for lawless government actors and constrain surveillance. Courts can block abuses of federal power, including illegal arrests, detentions and mass citizen databases.
The media, lawyers and civil society can hold the government accountable. And students, teachers, universities and cultural institutions can resist the tendency to self-censor and conform.
The citizen mobilization in Minnesota and the No Kings rallies are examples of that. But to resist chilling effects and their dangers over the long term, this would have to be the norm, not the exception.
This essay was written with Jon Penney, and originally appeared in The Conversation.
FBI’s 2025 Internet Crime Report
The 2025 Internet Crime Report was published a few weeks ago, but I only just saw it.
Lots of interesting statistics.
Identifying People Using Wi-Fi Routers
Not identifying people based on their use of Wi-Fi routers, but identifying people using Wi-Fi signals.
This is accomplished through what is known as WiFi sensing, or the use of WiFi signals to infer information about a physical environment. When radio signals like WiFi travel through a space, they interact with the objects and people around them. Those signals can be reflected, scattered, or absorbed. By analyzing how the signal is expected to behave compared with how it is actually received, researchers can infer details about the surrounding environment.
“By observing the propagation of radio waves, we can create an image of the surroundings and of persons who are present,” said Thorsten Strufe, a KIT professor and study co-author, in a press release. “This works similar to a normal camera, the difference being that in our case, radio waves instead of light waves are used for the recognition.”
Friday Squid Blogging: Regulating Squid Fishing in the South Pacific
The South Pacific Regional Fisheries Management Organization (SPRFMO) needs to regulate squid fishing in the South Pacific.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
CISA Security Leak
Crazy story:
Until this past weekend, a contractor for the Cybersecurity & Infrastructure Security Agency (CISA) maintained a public GitHub repository that exposed credentials to several highly privileged AWS GovCloud accounts and a large number of internal CISA systems. Security experts said the public archive included files detailing how CISA builds, tests and deploys software internally, and that it represents one of the most egregious government data leaks in recent history.
News article.
macOS Kernel Memory Corruption Exploit
A group used Anthropic’s Mythos AI model to help find a kernel memory corruption vulnerability and exploit on Apple’s M5.
News article.
On AI Security
Good report:
Executive Summary: Let’s say you wanted to make sure that your AI is secure. Can you just maximize the security and privacy benchmark and call it a day? Nope, because benchmarks don’t actually work for measuring AI capabilities (even when they are NOT emergent systemic properties like security). So let’s take a step back: how do you measure security in the first place? Good question. Over the last 30 years, security engineering for software evolved from black box penetration testing, through whitebox code analysis and architectural risk analysis to de facto process-driven standards like the Building Security In Maturity Model (BSIMM). Software had a very deep impact on business operations, and it appears that AI is going to have an even deeper impact. Will a software security-like measurement move work for AI? Probably. In the meantime we can make real progress in AI security by cleaning up our WHAT piles and managing risk by identifying and applying good assurance processes. (Spoiler alert: no matter what we do, we still don’t get a security meter for AI, so we need to be extra vigilant about security.)
Laurie Anderson Is Quoting Me
Not by name, but Laurie Anderson quotes me in one of the tracks of her new album:
My favorite quote is from a cryptologist who said “If you think technology will solve your problems, you don’t understand technology and you don’t understand your problems.”
Also in interviews:
“Of course, it’s ridiculous, outrageous, blah, blah, blah,” Anderson says about the ad. ‘But, I mean, my favorite quote on this is from a cryptologist who said, ‘If you think technology will solve your problems, you don’t understand technology  and you don’t understand your problems.’ And I think I’m completely on board with that.”
People are telling me that she has been reciting this quote in performances for years. (I lost track of her since college and her 1981 hit “O Superman.”)
The origins of the quote is from Roger Needham:
If you think cryptography can solve your problem, you don’t understand your problem and you don’t understand cryptography.
I modified the quote in the preface to my 2000 book Secrets and Lies:
A few years ago I heard a quotation, and I am going to modify it here: If you think technology can solve your security problems, then you don’t understand the problems and you don’t understand the technology.
I can’t tell you why me in 2000 didn’t credit Needham by name. I should have.
I have used the quote pretty consistently since then. Somewhere along the line I dropped “security” from the phrase, and now say it more like Anderson quotes me:
If you think technology will solve your problem, you don’t understand your problem and you don’t understand technology.
I sometimes use singular and sometimes use plural. Sometimes I say “the problem” and “the technology.” But I think the quote flows better ending with just the word “technology.”
EDITED TO ADD (5/12): It gets weirder. A friend sent me some 1997 emails that talk about this. Roger Needham wrote: “Butler Lampson and I each attribute to the other the remark.” I wrote: “Roger Needham claims that Robert Morris said it. Robert Morris claims that Roger Needham said it. No one knows who the originator is.” I said it from stage at Defcon that year—definitely not the originator.
Zero-Day Exploit Against Windows BitLocker
It’s nasty, but it requires physical access to the computer:
The exploit, named YellowKey, was published earlier this week by a researcher who goes by the alias Nightmare-Eclipse. It reliably bypasses default Windows 11 deployments of BitLocker, the full-volume encryption protection Microsoft provides to make disk contents off-limits to anyone without the decryption key, which is stored in a secured piece of hardware known as a trusted platform module (TPM). BitLocker is a mandatory protection for many organizations, including those that contract with governments.
Slashdot thread. And here’s Nightmare-Eclipse’s GitHub account.
Friday Squid Blogging: Bigfin Squid
Article about the bigfin squid.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
Bypassing On-Camera Age-Verification Checks
Some AI-based video age-verification checks can be fooled with a fake mustache.
Upcoming Speaking Engagements
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.
How Dangerous Is Anthropic’s Mythos AI?
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 that have come before. And it’s less capable than models that will come after. AIs are much better at writing software than they were just six months ago. There’s every reason to believe that they will continue to get better, which means that they will get better at writing more secure software. The endgame gives AI-enhanced defenders advantages over AI-enhanced attackers.
Even more interesting are the broader implications. The same searching, pattern-matching and reasoning capabilities that make these models so good at analyzing software almost certainly apply to similar systems. The tax code isn’t computer code, but it’s a series of algorithms with inputs and outputs. It has vulnerabilities; we call them tax loopholes. It has exploits; we call them tax avoidance strategies. And it has black hat hackers: attorneys and accountants.
Just as these models are finding hundreds of vulnerabilities in complex software systems, we should expect them to be equally effective at finding many new and undiscovered tax loopholes. I am confident that the major investment banks are working on this right now, in secret. They’ve fed AI the tax code of the US, or the UK, or maybe every industrialized country, and tasked the system with looking for money-saving strategies. How many tax loopholes will those AIs find? Ten? One hundred? One thousand? The Double Dutch Irish Sandwich is a tax loophole that involves multiple different tax jurisdictions. Can AIs find loopholes even more complex? We have no idea.
Sure, the AIs will come up with a bunch of tricks that won’t work, but that’s where those attorneys and accountants come in—to verify, and then justify, the loopholes. And then to market them to their wealthy clients.
As goes the tax code, so goes any other complex system of rules and strategies. These models could be tasked with finding loopholes in environmental rules, or food and safety rules—anywhere there are complex regulatory systems and powerful people who want to evade those rules.
The results will be much worse than insecure computers. Tax loopholes result in less revenue collected by governments, and regulatory loopholes allow the powerful to skirt the rules, both of which have all sorts of social ramifications. And while software vendors can patch their systems in days, it generally takes years for a country to amend its tax code. And that process is political, with lobbyists pressuring legislators not to patch. Just look at the carried interest loophole, a US tax dodge that has been exploited for decades. Various administrations have tried to close the vulnerability, but legislators just can’t seem to resist lobbyists long enough to patch it.
AI technologies are poised to remake much of society. Just as the industrial revolution gave humans the ability to consume calories outside of their bodies at scale, the AI revolution will give humans the ability to perform cognitive tasks outside of their bodies at scale. Our systems aren’t designed for that; they’re designed for more human paces of cognition. We’re seeing it right now in the deluge of software vulnerabilities that these models are finding and exploiting. And we will soon see it in a deluge of vulnerabilities in all sorts of other systems of rules. Adapting to this new reality will be hard, but we don’t have any choice.
This essay originally appeared in The Guardian.
OpenAI’s GPT-5.5 is as Good as Mythos at Finding Security Vulnerabilities
The UK’s AI Security Institute evaluated GPT-5.5’s ability to find security vulnerabilities, and found that it is comparable to Claude Mythos. Note that the OpenAI model is generally available.
Here is the Institute’s evaluation of Mythos.
And here is an analysis of a smaller, cheaper model. It requires more scaffolding from the prompter, but it is also just as good.
Copy.Fail Linux Vulnerability
This is the worst Linux vulnerability in years.
TL;DR
- copy.fail is a Linux kernel local privilege escalation, not a browser or clipboard attack. Disclosed by Theori on 29 April 2026 with a working PoC.
- It abuses the kernel crypto API (AF_ALG sockets) plus splice() to write four bytes at a time straight into the page cache of a file the attacker does not own.
- The exploit works unmodified across Ubuntu, RHEL, Debian, SUSE, Amazon Linux, Fedora and most others. No race condition, no per-distro offsets.
- The file on disk is never modified. AIDE, Tripwire and checksum-based monitoring see nothing.
- Kubernetes Pod Security Standards (Restricted) and the default RuntimeDefault seccomp profile do not block the syscall used. A custom seccomp profile is needed.
- The mainline fix landed on 1 April. Distros are rolling kernels out now. Patch.
“Local privilege escalation” sounds dry, so let me unpack it. It means: an attacker who already has some way to run code on the machine, even as the most boring unprivileged user, can promote themselves to root. From there they can read every file, install backdoors, watch every process, and pivot to other systems.
Why does that matter on shared infrastructure? Because “local” covers a lot of ground in 2026: every container on a shared Kubernetes node, every tenant on a shared hosting box, every CI/CD job that runs untrusted pull-request code, every WSL2 instance on a Windows laptop, every containerised AI agent given shell access. They all share one Linux kernel with their neighbours. A kernel LPE collapses that boundary.
News article.
LLMs and Text-in-Text Steganography
Turns out that LLMs are really good at hiding text messages in other text messages.
Friday Squid Blogging: Giant Squid Live in the Waters of Western Australia
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.
Insider Betting on Polymarket
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.
Smart Glasses for the Authorities
ICE is developing its own version of smart glasses, with facial recognition tied to various databases.
Rowhammer Attack Against NVIDIA Chips
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.
DarkSword Malware
DarkSword is a sophisticated piece of malware—probably government designed—that targets iOS.
Google Threat Intelligence Group (GTIG) has identified a new iOS full-chain exploit that leveraged multiple zero-day vulnerabilities to fully compromise devices. Based on toolmarks in recovered payloads, we believe the exploit chain to be called DarkSword. Since at least November 2025, GTIG has observed multiple commercial surveillance vendors and suspected state-sponsored actors utilizing DarkSword in distinct campaigns. These threat actors have deployed the exploit chain against targets in Saudi Arabia, Turkey, Malaysia, and Ukraine.
DarkSword supports iOS versions 18.4 through 18.7 and utilizes six different vulnerabilities to deploy final-stage payloads. GTIG has identified three distinct malware families deployed following a successful DarkSword compromise: GHOSTBLADE, GHOSTKNIFE, and GHOSTSABER. The proliferation of this single exploit chain across disparate threat actors mirrors the previously discovered Coruna iOS exploit kit. Notably, UNC6353, a suspected Russian espionage group previously observed using Coruna, has recently incorporated DarkSword into their watering hole campaigns.
A week after it was identified, a version of it leaked onto the internet, where it is being used more broadly.
This news is a month old. Your devices are safe, assuming you patch regularly.
Hacking Polymarket
Polymarket is a platform where people can bet on real-world events, political and otherwise. Leaving the ethical considerations of this aside (for one, it facilitates assassination), one of the issues with making this work is the verification of these real-world events. Polymarket gamblers have threatened a journalist because his story was being used to verify an event. And now, gamblers are taking hair dryers to weather sensors to rig weather bets.
There’s also insider trading: a lot of it.
A Ransomware Negotiator Was Working for a Ransomware Gang
Someone pleaded guilty to secretly working for a ransomware gang as he negotiated ransomware payments for clients.
Sidebar photo of Bruce Schneier by Joe MacInnis.