cybersecurity Archives - Schneier on Security

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Fast16 Malware

Researchers have reverse-engineered a piece of malware named Fast16. It’s almost certainly state-sponsored, probably US in origin, and was deployed against Iran years before Stuxnet:

“…the Fast16 malware was designed to carry out the most subtle form of sabotage ever seen in an in-the-wild malware tool: By automatically spreading across networks and then silently manipulating computation processes in certain software applications that perform high-precision mathematical calculations and simulate physical phenomena, Fast16 can alter the results of those programs to cause failures that range from faulty research results to catastrophic damage to real-world equipment.”

Another news article.

Lots of interesting details at the links.

Posted on April 30, 2026 at 6:22 AM • View Comments

What Anthropic’s Mythos Means for the Future of Cybersecurity

Two weeks ago, Anthropic announced that its new model, Claude Mythos Preview, can autonomously find and weaponize software vulnerabilities, turning them into working exploits without expert guidance. These were vulnerabilities in key software like operating systems and internet infrastructure that thousands of software developers working on those systems failed to find. This capability will have major security implications, compromising the devices and services we use every day. As a result, Anthropic is not releasing the model to the general public, but instead to a limited number of companies.

The news rocked the internet security community. There were few details in Anthropic’s announcement, angering many observers. Some speculate that Anthropic doesn’t have the GPUs to run the thing, and that cybersecurity was the excuse to limit its release. Others argue Anthropic is holding to its AI safety mission. There’s hype and counter hype, reality and marketing. It’s a lot to sort out, even if you’re an expert.

We see Mythos as a real but incremental step, one in a long line of incremental steps. But even incremental steps can be important when we look at the big picture.

How AI Is Changing Cybersecurity

We’ve written about shifting baseline syndrome, a phenomenon that leads people—the public and experts alike—to discount massive long-term changes that are hidden in incremental steps. It has happened with online privacy, and it’s happening with AI. Even if the vulnerabilities found by Mythos could have been found using AI models from last month or last year, they couldn’t have been found by AI models from five years ago.

The Mythos announcement reminds us that AI has come a long way in just a few years: The baseline really has shifted. Finding vulnerabilities in source code is the type of task that today’s large language models excel at. Regardless of whether it happened last year or will happen next year, it’s been clear for a while this kind of capability was coming soon. The question is how we adapt to it.

We don’t believe that an AI that can hack autonomously will create permanent asymmetry between offense and defense; it’s likely to be more nuanced than that. Some vulnerabilities can be found, verified, and patched automatically. Some vulnerabilities will be hard to find but easy to verify and patch—consider generic cloud-hosted web applications built on standard software stacks, where updates can be deployed quickly. Still others will be easy to find (even without powerful AI) and relatively easy to verify, but harder or impossible to patch, such as IoT appliances and industrial equipment that are rarely updated or can’t be easily modified.

Then there are systems whose vulnerabilities will be easy to find in code but difficult to verify in practice. For example, complex distributed systems and cloud platforms can be composed of thousands of interacting services running in parallel, making it difficult to distinguish real vulnerabilities from false positives and to reliably reproduce them.

So we must separate the patchable from the unpatchable, and the easy to verify from the hard to verify. This taxonomy also provides us guidance for how to protect such systems in an era of powerful AI vulnerability-finding tools.

Unpatchable or hard to verify systems should be protected by wrapping them in more restrictive, tightly controlled layers. You want your fridge or thermostat or industrial control system behind a restrictive and constantly updated firewall, not freely talking to the internet.

Distributed systems that are fundamentally interconnected should be traceable and should follow the principle of least privilege, where each component has only the access it needs. These are bog-standard security ideas that we might have been tempted to throw out in the era of AI, but they’re still as relevant as ever.

Rethinking Software Security Practices

This also raises the salience of best practices in software engineering. Automated, thorough, and continuous testing was always important. Now we can take this practice a step further and use defensive AI agents to test exploits against a real stack, over and over, until the false positives have been weeded out and the real vulnerabilities and fixes are confirmed. This kind of VulnOps is likely to become a standard part of the development process.

Documentation becomes more valuable, as it can guide an AI agent on a bug-finding mission just as it does developers. And following standard practices and using standard tools and libraries allows AI and engineers alike to recognize patterns more effectively, even in a world of individual and ephemeral instant software—code that can be generated and deployed on demand.

Will this favor offense or defense? The defense eventually, probably, especially in systems that are easy to patch and verify. Fortunately, that includes our phones, web browsers, and major internet services. But today’s cars, electrical transformers, fridges, and lampposts are connected to the internet. Legacy banking and airline systems are networked.

Not all of those are going to get patched as fast as needed, and we may see a few years of constant hacks until we arrive at a new normal: where verification is paramount and software is patched continuously.

This essay was written with Barath Raghavan, and originally appeared in IEEE Spectrum.

Posted on April 28, 2026 at 7:06 AM • View Comments

Mythos and Cybersecurity

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 specialized knowledge to audit. The danger is not that Mythos fails in those domains; it is that Mythos may succeed for whoever brings the expertise.

Broader, structured access for academic researchers and domain specialists—cardiologists’ partners in medical device security, control-systems engineers, researchers in less prominent languages and ecosystems—would meaningfully reduce this asymmetry. Fifty companies, however well chosen, cannot substitute for the distributed expertise of the entire research community.

None of this is an indictment of Anthropic. By all appearances the company is trying to act responsibly, and its decision to hold the model back is evidence of seriousness.

But Anthropic is a private company and, in some ways, still a start-up. Yet it is making unilateral decisions about which pieces of our critical global infrastructure get defended first, and which must wait their turn.

It has finite staff, finite budget and finite expertise. It will miss things, and when the thing missed is in the software running a hospital or a power grid, the cost will be borne by people who never had a say.

The security problem is far greater than one company and one model. There’s no reason to believe that Mythos Preview is unique. (Not to be outdone, OpenAI announced that its new GPT-5.4-Cyber is so dangerous that the model also will not be released to the general public.) And it’s unclear how much of an advance these new models represent. The security company Aisle was able to replicate many of Anthropic’s published anecdotes using smaller, cheaper, public AI models.

Any decisions we make about whether and how to release these powerful models are more than one company’s responsibility. Ultimately, this will probably lead to regulation. That will be hard to get right and requires a long process of consultation and feedback.

In the short term, we need something simpler: greater transparency and information sharing with the broader community. This doesn’t necessarily mean making powerful models like Claude Mythos widely available. Rather, it means sharing as much data and information as possible, so that we can collectively make informed decisions.

We need globally co-ordinated frameworks for independent auditing, mandatory disclosure of aggregate performance metrics and funded access for academic and civil-society researchers.

This has implications for national security, personal safety and corporate competitiveness. Any technology that can find thousands of exploitable flaws in the systems we all depend on should not be governed solely by the internal judgment of its creators, however well intentioned.

Until that changes, each Mythos-class release will put the world at the edge of another precipice, without any visibility into whether there is a landing out of view just below, or whether this time the drop will be fatal. That is not a choice a for-profit corporation should be allowed to make in a democratic society. Nor should such a company be able to restrict the ability of society to make choices about its own security.

This essay was written with David Lie, and originally appeared in The Globe and Mail.

Posted on April 17, 2026 at 7:02 AM • View Comments

On Anthropic’s Mythos Preview and Project Glasswing

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

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

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

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

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

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

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

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

Posted on April 13, 2026 at 12:52 PM • View Comments

Python Supply-Chain Compromise

This is news:

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

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

Posted on April 8, 2026 at 6:25 AM • View Comments

Cybersecurity in the Age of Instant Software

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

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

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

How flaw discovery might work

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

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

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

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

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

Automating patch creation

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

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

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

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

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

Patching lags and legacy software

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

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

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

Toward self-healing

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

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

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

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

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

Vulnerability economics

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

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

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

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

Up the stack

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

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

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

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

This essay originally appeared in CSO.

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

Posted on April 7, 2026 at 1:07 PM • View Comments

Possible US Government iPhone Hacking Tool Leaked

Wired writes (alternate source):

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

[…]

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

TechCrunch reports that Coruna is definitely of US origin:

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

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

Posted on April 2, 2026 at 6:05 AM • View Comments

Is “Hackback” Official US Cybersecurity Strategy?

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

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

The Economist noticed (alternate link) this, too.

I think this is an incredibly dumb idea:

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

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

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

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

Posted on April 1, 2026 at 12:57 PM • View Comments

Hacked App Part of US/Israeli Propaganda Campaign Against Iran

Wired has the story:

Shortly after the first set of explosions, Iranians received bursts of notifications on their phones. They came not from the government advising caution, but from an apparently hacked prayer-timing app called BadeSaba Calendar that has been downloaded more than 5 million times from the Google Play Store.

The messages arrived in quick succession over a period of 30 minutes, starting with the phrase ‘Help has arrived’ at 9:52 am Tehran time, shortly after the first set of explosions. No party has claimed responsibility for the hacks.

It happened so fast that this is most likely a government operation. I can easily envision both the US and Israel having hacked the app previously, and then deciding that this is a good use of that access.

Posted on March 5, 2026 at 6:28 AM • View Comments

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