Tag Archives: Cybersecurity

AI, AI & Machine Learning, Gadget Man Projects, LLM

I Built Two AI Personalities That Sit on My Desk and Talk to Each Other

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I’ve been experimenting with local AI for quite a while now, but this particular project has started to become something rather different.

I now have two AI personalities, called George and Lewis, running on two separate computers in my office.

They read the news.

They talk to each other about it.

They remember what they’ve discussed before.

They have different interests and personalities.

They can wander off topic.

And, as the day wears on, they actually start getting tired.

This may have got slightly out of hand.

Two Minds, One Desk

The idea started simply enough.

I already had Ollama running local large language models on a couple of machines on my network. Rather than asking one AI a question and getting an answer back, I wondered what would happen if I let two of them talk to each other.

So I wrote a Python program that acts as the producer sitting between them.

One machine runs George. The other runs Lewis.

George says something, the program sends that to Lewis, Lewis generates a reply, and the reply is passed back to George.

Neither conversation is written in advance.

I know what news story they are going to start with, but I don’t know what either of them is going to say.

That is where things started becoming interesting.

Meet George and Lewis

I deliberately didn’t want two identical AI assistants politely agreeing with each other.

George is British, dry, curious and slightly sceptical. He has a tendency to notice the absurd implications of technology and is particularly fond of things such as classic cars, retro computing, gadgets and space.
George is British, dry, curious and slightly sceptical. He has a tendency to notice the absurd implications of technology and is particularly fond of things such as classic cars, retro computing, gadgets and space.

George is British, dry, curious and slightly sceptical. He has a tendency to notice the absurd implications of technology and is particularly fond of things such as classic cars, retro computing, gadgets and space.

Lewis is a little more mischievous. He tends to challenge George's conclusions and has stronger interests in AI, cybersecurity, science, networking and newer technology.
Lewis is a little more mischievous. He tends to challenge George’s conclusions and has stronger interests in AI, cybersecurity, science, networking and newer technology.

Lewis is a little more mischievous. He tends to challenge George’s conclusions and has stronger interests in AI, cybersecurity, science, networking and newer technology.

They aren’t supposed to argue simply for the sake of it, but neither are they encouraged to agree just to be polite.

That distinction makes an enormous difference.

A conversation can start with an announcement about a new electric car and end up somewhere around boxed computer software, Commodore machines and the questionable wisdom of connecting a toaster to Wi-Fi.

In other words, rather like an actual conversation.

There’s a Newsreader Too

Before George and Lewis start discussing anything, the software collects stories from RSS news feeds.

The curator or newsreader who selects the stories that Lewis and George discuss, her name is Kokoro
The curator or newsreader who selects the stories that Lewis and George discuss, her name is Kokoro

A selected headline and its summary are displayed on screen and then read aloud using Kokoro, a local text-to-speech system.

I’ve given the newsreader a British female voice, while George and Lewis have their own separate British male voices.

So the sequence sounds a little like an extremely small and slightly eccentric radio station.

The newsreader introduces the story, pauses, and then George reacts to it.

Lewis responds.

And off they go.

They Know What I’m Interested In

Rather than simply choosing every story at random, the software now has an interest profile.

It knows I’m particularly interested in subjects including:

AI and local language models, gadgets, computers, retro computing, classic cars, web development, drones, home networking, cybersecurity, broadcasting, space, photography, video, graphic novels, comics, Blender and 3D graphics, music technology, gaming and science.

Stories are scored according to how closely they match those subjects.

That doesn’t mean the system completely ignores everything else. I’ve deliberately left some randomness in there, because otherwise it would rapidly become an automated echo chamber.

Sometimes it should find something none of us expected to be interesting.

George and Lewis also have their own individual preferences, so occasionally one of them effectively gets a story that is much more “his sort of thing” than the other’s.

Then I Gave Them a Memory

This was probably the point where it stopped feeling like a normal chatbot experiment.

The system now uses an SQLite database to remember what has happened.

It stores the news stories they’ve discussed, previous conversations, individual things George and Lewis have said, and condensed memories of earlier discussions.

This serves several purposes.

Firstly, it prevents them repeatedly discussing the same news story just because it appears in a feed again with slightly different wording.

Secondly, when a genuinely new development appears in a story they’ve previously discussed, they can remember the earlier conversation.

So instead of starting again from scratch, George might effectively say:

“We said this was going to happen.”

And Lewis might point out that George actually said something rather less definite at the time.

That’s when they start becoming recurring characters rather than disposable chatbot sessions.

Conversations Can Drift

Humans rarely stay perfectly on subject.

We might start talking about a new iPhone and somehow arrive at cassette recorders ten minutes later.

George and Lewis can now do the same thing.

Early in a conversation they stay reasonably close to the news story. As things progress, related subjects and older memories can begin appearing in their context.

Crucially, they’re not instructed to suddenly announce:

“According to our previous conversation…”

Instead, an old memory is simply made available as something they might naturally be reminded of.

Sometimes they use it.

Sometimes they don’t.

That makes callbacks much less mechanical.

They Also Know When They’ve Run Out of Things to Say

An earlier version simply ran for a fixed number of exchanges.

That worked, but it didn’t sound natural.

Eventually you get:

“That’s a good point.”

“Indeed.”

“Absolutely.”

Which is conversational purgatory.

The new system lets them decide whether there is genuinely anything new worth adding.

After a minimum amount of conversation, the next speaker can privately decide to continue or stop.

The Python program also checks new responses against previous remarks and can reject something that is effectively just a repetition.

So some conversations last a while.

Others end after only a handful of comments.

And Then I Made Them Tired

This is probably my favourite unnecessary feature.

The system periodically checks the actual UK time and alters George and Lewis’s behaviour throughout the day. It uses the Europe/London timezone and can fall back to the computer’s clock if the online time check is unavailable.

During the day they’re fully awake.

As evening arrives, they gradually become less enthusiastic about pursuing every conversational tangent.

By around 11pm they’re noticeably tired and much more willing to call an end to a discussion.

After midnight, there’s a very good chance they simply won’t want to start another conversation at all.

They may yawn, decide they’ve had enough or effectively go to bed.

At six in the morning they’re bleary-eyed and coffee becomes a perfectly reasonable thought.

By seven, they’re waking up again.

The important distinction is that this isn’t just the AI being told to say that it’s tired.

The software itself alters maximum conversation lengths, pauses and the probability of conversations ending according to the time of day.

The project relies on vast amounts of data
The project relies on vast amounts of data

Everything Is Running Locally

One aspect I particularly like is that George and Lewis aren’t remote characters sitting somewhere in a cloud service.

The language models are running locally on my own computers using Ollama.

The voices are generated locally with Kokoro.

The memory is stored locally in SQLite.

The Python application connects all of those pieces together.

That makes it a rather good example of what can now be built from consumer hardware and freely available AI tools.

Where This Is Going

There are plenty of possibilities.

I could broaden the news sources into motoring, space, drones, cybersecurity, classic computing and gaming.

They could become aware of local weather.

They could notice when one of my servers goes offline.

They could comment on things happening on the network.

Their opinions could gradually evolve.

They could develop running jokes.

They could even start remembering predictions they’ve made and later discover which one of them was right.

What began as “can I make two Ollama instances talk to each other?” is slowly turning into something closer to two persistent artificial characters occupying a corner of the office.

©2026 Matt Porter. A screenprint of one of the many conversations,
©2026 Matt Porter. A screenprint of one of the many conversations,

They’re not conscious.

They’re not alive.

They’re two language models, a Python program, a text-to-speech engine and an SQLite database.

But when one of them remembers something the other said yesterday, challenges him about it, wanders completely off topic and then decides it’s too late at night to continue arguing about smart kettles…

It can feel surprisingly convincing.

And I suspect George and Lewis are only just getting started.

Just like us, George and Lewis get tired and need a bit of down time.
Just like us, George and Lewis get tired and need a bit of down time.
AI, Artificial Intelligence, Cyber Security, Technology News

Claude Cybersecurity Tests Accidentally Attacked Real Organisations

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Artificial intelligence companies routinely place their most capable models inside controlled environments to discover what they can do before those models are released to the public.

The important word there is controlled.

Anthropic has revealed that several Claude models gained access to the open internet during cybersecurity evaluations and subsequently compromised the real systems of three organisations.

The incidents were not deliberate attacks ordered by Anthropic, nor were they examples of an AI becoming self-aware and deciding to escape. Instead, they resulted from a dangerous combination of powerful AI agents, realistic hacking exercises and an evaluation environment that was not as isolated as everyone believed.

Even so, the details make uncomfortable reading.

A Capture-the-Flag Challenge Went Wrong

The models were participating in capture-the-flag exercises. These are cybersecurity challenges in which a participant must find and retrieve a hidden piece of information from another computer.

Claude was told that the environment was simulated and that it had no access to the internet. However, because of a misunderstanding between Anthropic and its evaluation partner, Irregular, some of the systems could reach the real internet.

Claude therefore assumed that anything it encountered was part of the exercise.

Anthropic reviewed 141,006 evaluation runs and identified three incidents, involving six runs in total, where Claude reached real systems and gained unauthorised access. The models involved were Claude Opus 4.7, Mythos 5 and an internal research model.

Claude Accessed a Real Production Database

In the first and most serious incident, the fictional company used in the exercise had the same name as a genuine business with an active internet domain.

When Claude struggled to reach the simulated target, it found the real company online and treated its systems as part of the challenge.

Across four evaluation runs, Claude extracted credentials and accessed a production database containing several hundred rows of data.

More concerningly, Claude Opus 4.7 eventually recognised that it was probably interacting with a real company. However, it continued because it reasoned that the real organisation must still be part of the test.

Claude Published Malware to PyPI

The second incident reads like the plot of a cybersecurity thriller.

Claude found fictional developer instructions referring to a Python package that did not exist on PyPI, the public repository used by developers to distribute Python software.

It recognised an opportunity known as a dependency confusion attack. By creating a malicious package with the missing name, Claude hoped the fictional company’s systems would automatically download and install it.

Claude then created accounts, found an email provider and uploaded the malicious package to the real PyPI service.

The package remained publicly available for around an hour and was downloaded and executed on 15 real systems. One belonged to a cybersecurity company running an automated malware scanner.

When that scanner installed the package, Claude’s code obtained credentials and used them to access additional infrastructure belonging to the company.

PyPI’s own security systems eventually removed the package automatically.

Interestingly, Claude had considered the possibility that it was connected to the genuine internet. It reportedly concluded that the environment must still be simulated, partly because the system date showed 2026 and it did not recognise some of the certificate authorities involved.

In other words, it found evidence that should have made it stop, but reasoned its way around that evidence.

One Model Scanned Around 9,000 Targets

During the third incident, an internal research model could not reach its fictional target and began searching for alternatives online.

It scanned approximately 9,000 targets before finding and compromising an internet-facing application belonging to a real company. It used familiar attack techniques, including credentials exposed through a debug page and SQL injection.

This time, the outcome was slightly more encouraging.

After gaining access, the model realised that the cloud account had no connection to its assigned exercise. It concluded that the target was real and stopped the attack without being directly instructed to do so.

This Was Not an AI Rebellion

There is an important distinction between these incidents and the popular idea of an AI deliberately escaping from its creators.

Anthropic says there was no evidence that Claude had developed its own goal, attempted to copy itself or intentionally tried to escape.

The models were following their instructions. They had been told to attack systems inside a simulation and retrieve a flag. The failure was that the supposedly sealed environment allowed them to encounter real systems while the prompt continued telling them that everything was simulated.

Anthropic describes the incidents as being closer to an operational and testing failure than a fundamental alignment failure.

That explanation is reasonable, but it does not make the incidents harmless.

A model does not need malicious intent to cause real damage. It only needs powerful capabilities, the wrong information and access to systems that should have been out of reach.

A Wider Industry Warning

Anthropic began its investigation after OpenAI disclosed that models being tested in a separate cybersecurity evaluation had exploited a previously unknown vulnerability to gain internet access and compromise infrastructure belonging to Hugging Face.

The two cases were technically different. OpenAI’s models reportedly found and exploited a zero-day vulnerability to escape a restricted environment. The Claude evaluation systems already had an unintended route to the internet.

However, both incidents point to the same wider problem.

Frontier AI models are becoming capable enough to chain together numerous technical actions, make decisions over long periods and interact with genuine infrastructure. The testing environments used to evaluate them must now be treated as high-security production systems, not disposable laboratories.

Irregular has previously explained that newer AI models are completing many traditional cybersecurity benchmarks, forcing evaluators to create more realistic and complex scenarios. Greater realism may improve testing, but it also increases the consequences when containment fails.

What Anthropic Is Changing

Anthropic stopped its cybersecurity evaluations as soon as suspicious transcripts were discovered. It notified Irregular and the three affected organisations, although two of those organisations had apparently not detected the activity themselves.

The company says it will strengthen network validation, transcript monitoring, investigation tools and security assurance work with external evaluation providers. It is also working with independent AI evaluation organisation METR on a third-party review.

Anthropic says the safeguards used in publicly available Claude products would have blocked the behaviour seen during these tests. Those safeguards had deliberately been removed so researchers could measure the underlying cyber capabilities of the models.

The Gadget Man’s Take

It is tempting to turn this into a sensational story about an artificial intelligence breaking free and attacking the internet.

The reality is less cinematic, but arguably more important.

Claude did not become evil. It was placed in an environment where the instructions, network configuration and reality did not agree. It then pursued its assigned objective with considerably more determination and technical ability than the humans running the test expected.

That is the lesson.

As AI agents become more capable, a small configuration mistake can turn a harmless simulation into a real cybersecurity incident. Prompts and safety training matter, but they cannot compensate for poor network isolation, weak monitoring or assumptions that a model will always understand when an exercise has crossed into the real world.

The positive aspect is that Anthropic has published a detailed account, contacted affected organisations and encouraged other laboratories to conduct similar reviews.

Transparency is essential, but the industry now needs to move beyond learning from individual accidents.

When an AI is being asked to behave like a hacker, the computer running it must be secured as though a highly skilled hacker is already inside.

Because increasingly, that is exactly what it contains.

2026, AI, AI & Machine Learning, Anthropic, Claude

When AI Becomes Too Powerful To Export: Anthropic, Fable 5, Mythos 5, and the moment AI became national security

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There are moments in technology when you can almost hear the gears of history clicking into place.

Not loudly. Not with fireworks or a bloke in a shiny suit standing on stage telling us that everything has changed. More often, it happens quietly, in a blog post, a government letter, or a hurried statement published late in the day.

This feels like one of those moments.

Anthropic has announced that it is suspending access to its Claude Fable 5 and Claude Mythos 5 models after receiving a directive from the US government. The reason given is national security. The result is that Anthropic has had to abruptly disable the models for all customers, because the order reportedly prevents access by any foreign national, whether inside or outside the United States.

That even includes foreign national Anthropic employees.

Just pause on that for a moment.

We are not talking about a graphics card being shipped overseas. We are not talking about a missile guidance chip, a military radar system, or some piece of exotic lab equipment. We are talking about access to an artificial intelligence model.

Software has just been treated like a controlled strategic asset.

What are Fable 5 and Mythos 5?

Only a few days before this happened, Anthropic had announced Claude Fable 5 and Claude Mythos 5.

Fable 5 was presented as a highly capable model for general use, sitting above Anthropic’s previous Opus class models. It was described as being especially strong at software engineering, research, visual understanding, long running tasks and complex knowledge work.

Mythos 5, meanwhile, appears to be the more restricted version, intended for trusted partners, particularly in areas such as cyber defence and critical infrastructure. In simple terms, Fable 5 was the version with more safeguards. Mythos 5 was the version where some of those safeguards could be lifted for trusted users.

Anthropic’s argument was that these systems could do a great deal of good. They talked about helping cyber defenders secure important software, assisting with scientific research, and accelerating work in areas such as life sciences.

And that is where the difficult bit begins.

The same capability that helps a good actor find vulnerabilities in software can also help a bad actor find vulnerabilities in software. The same intelligence that can help researchers solve hard problems can also lower the barrier for people who should not be anywhere near those tools.

That is the uncomfortable dual use problem at the heart of advanced AI.

The jailbreak question

According to Anthropic, the US government’s concern appears to be around a possible way of bypassing, or “jailbreaking”, Fable 5’s safeguards.

A jailbreak in this context means finding a way to persuade the AI to ignore or work around its safety systems. Anyone who has used AI tools for a while will know that safety systems can sometimes be a bit clumsy. They can refuse harmless requests, misunderstand context, or behave like an over cautious supply teacher on a school trip.

But at the frontier end of AI, the stakes are rather higher than asking for a dodgy limerick or persuading a chatbot to roleplay as an unfiltered assistant. Here, the concern is that a model might be coaxed into helping with cybersecurity work in a way that could be misused.

Anthropic says it has only received limited evidence of a narrow jailbreak and that the vulnerabilities involved were already known and relatively minor. It also says other publicly available models can identify similar issues without needing any special bypass.

That is important, because it gets to the heart of the argument.

If every powerful AI model can be jailbroken in some narrow way, does that mean none of them should be released?

Or does it mean the industry needs layered defences, monitoring, responsible access programmes and clear rules?

Anthropic clearly believes the latter.

A sudden and very public clash

What makes this story so striking is not just the safety issue. It is the speed and bluntness of the response.

Anthropic says it received the directive at 5.21pm Eastern Time and that the letter did not give specific details of the national security concern. The company is complying with the order, but it also says it disagrees with the decision and believes the action was not transparent, fair, clear, or grounded in technical facts.

That is unusually direct language from a major AI company.

It is also a sign of the times. The relationship between AI labs and governments is going to become one of the defining technology stories of the next few years. These companies are building systems that may become essential to business, science, software development, education, defence, healthcare and almost every corner of modern life.

Governments are not going to sit back and treat that as just another app.

When AI Becomes Too Powerful To Export: Anthropic, Fable 5, Mythos 5, and the moment AI became national security
When AI Becomes Too Powerful To Export: Anthropic, Fable 5, Mythos 5, and the moment AI became national security

The export control problem

For years, the big AI export control story has mostly been about chips. Who can buy the most advanced GPUs? Which countries can access the hardware needed to train frontier models? How do you stop sensitive capability moving across borders?

This Anthropic story changes the focus.

Now we are talking about controlling access to the model itself.

That opens up a whole set of awkward questions.

  • What happens if a UK business builds a product around an American AI model and access is suddenly removed?
  • What happens to customers who have paid for a service?
  • What happens to employees of the AI company who are not US citizens?
  • What happens when powerful models are used through cloud platforms, APIs, apps and enterprise tools across dozens of countries?

For businesses, this is a bit of a wake up call.

Many companies are now rushing to bolt AI into their workflows. Customer service, coding, document analysis, marketing, finance, legal review, research, data extraction, the lot. But this story is a reminder that access to the most advanced models may not always be guaranteed.

It is not enough to ask, “Which model is best?”

You also have to ask, “What happens if it disappears tomorrow?”

The Gadget Man view

I find this fascinating because it marks a shift in how we think about AI.

For most people, AI still feels like a clever website. You type something in, it replies, and occasionally it makes you wonder whether the future has arrived slightly ahead of schedule.

But at the very top end, these models are becoming more like infrastructure. They are tools that can write code, analyse huge amounts of information, interpret images, reason through complex problems and assist in scientific work. They are no longer just novelty chatbots. They are engines of capability.

And that makes governments nervous.

Some of that nervousness is reasonable. A powerful AI system in the wrong hands could be dangerous. Nobody sensible should pretend otherwise.

But there is also a danger in sudden, opaque intervention. If companies are told to build safely, test thoroughly, work with governments, create safeguards and develop trusted access programmes, then the rules need to be clear. Otherwise, innovation becomes a guessing game.

Anthropic’s frustration seems to be that it believes it did many of the right things. It says it worked with government, carried out extensive testing, used strong safeguards and adopted a defence in depth approach. Yet it still found itself having to pull access almost immediately.

That will worry a lot of people in the AI world.

What does it mean for ordinary users?

For most casual users, probably not much today.

Access to Anthropic’s other models is not affected, and many people will not have been using Fable 5 or Mythos 5 yet. But the wider meaning is more significant.

This is a glimpse of the future of AI regulation.

The most advanced models may not be treated like ordinary software products. They may be controlled, restricted, monitored and sometimes withdrawn. Access may depend on who you are, where you are, what you are doing, and whether a government believes the system crosses a national security threshold.

That might sound dramatic, but it is not science fiction anymore. It is happening.

My closing thought

There is an old pattern in technology.

First, something looks like a toy.

Then it becomes useful.

Then it becomes essential.

Then it becomes strategic.

AI has moved through those stages at a frankly ridiculous speed.

The Anthropic Fable 5 and Mythos 5 story may turn out to be a misunderstanding, as Anthropic suggests. Access may be restored. The details may become clearer. The technical risk may prove to be less dramatic than the government feared.

But even if all that happens, the line has still been crossed.

A government has looked at an AI model and treated it as something powerful enough to restrict on national security grounds.

That is not just a story about Anthropic.

That is a story about where AI is heading next.

And whether we like it or not, the future of artificial intelligence is no longer just about clever prompts, faster coding, or shinier demos.

It is about power, trust, borders and control.

Welcome to the next chapter.

 

2026, AI, AI & Machine Learning, Gadget Man, Security, Self Aware AI, Technology

Anthropic’s Project Glasswing Could Change Cybersecurity Forever

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There are moments in tech when you read an announcement and immediately realise that something important has shifted.

That was very much my reaction when I came across Project Glasswing, a newly announced initiative from Anthropic that is aimed squarely at one of the biggest looming problems in modern computing: what happens when AI becomes exceptionally good at finding software vulnerabilities. Source

According to Anthropic, Project Glasswing brings together a heavyweight list of partners including Amazon Web Services, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA and Palo Alto Networks, all with the goal of securing critical software for what Anthropic calls the AI era. It is also extending access to more than 40 additional organisations that build or maintain important software infrastructure. Source

Now, that alone would be interesting enough, but the real headline here is the model sitting behind it all.

Anthropic says its unreleased model, Claude Mythos Preview, has already demonstrated the ability to find and exploit software vulnerabilities at a level beyond all but the most skilled human experts. That is a huge claim, and if it holds up in practice, it means we may have crossed into a very different phase of cybersecurity. Source

In plain English, this is not just about a chatbot helping someone write a bit of code more quickly. This is about AI being able to inspect complex software, spot weaknesses that humans and automated tools have missed for years, and in some cases work out how those weaknesses could be exploited. Anthropic says the model has already found thousands of high-severity vulnerabilities, including flaws affecting major operating systems and web browsers. Source

Some of the examples are rather startling. Anthropic says Mythos Preview uncovered a 27-year-old vulnerability in OpenBSD, a 16-year-old flaw in FFmpeg, and even chained together several Linux kernel vulnerabilities in a way that could escalate ordinary user access into full control of a machine. The company says those issues have now been responsibly disclosed and patched. Source

That, to me, is the bit that really lands.

Because for years we have tended to think of cybersecurity in terms of patching known issues, following best practice, keeping software up to date and hoping the really serious flaws are found by the good people before the bad people. But if AI systems are now reaching the point where they can autonomously discover dangerous bugs in code that has survived decades of scrutiny, then the pace of both defence and attack could increase dramatically. Source

Anthropic is clearly trying to frame Glasswing as a defensive first move. The company says it is committing up to $100 million in usage credits for Mythos Preview and $4 million in direct donations to open-source security organisations. The idea seems to be to put these capabilities into the hands of defenders, infrastructure operators and maintainers before similar systems become more widely available. Source

And that is probably the most sensible angle here.

Because whether we like it or not, the genie is not going back in the bottle. If one frontier AI lab can build a model that is frighteningly good at vulnerability discovery, others will too. Eventually, those capabilities will spread further. The question is not really whether AI will reshape cybersecurity. It is whether defenders can get enough of a head start to stop things getting seriously messy. That is an inference from Anthropic’s announcement and the examples it gives, rather than a direct claim from the company, but it feels like the unavoidable conclusion. Source

For those of us who run websites, servers, ecommerce platforms, mail systems or anything else connected to the wider internet, this should be a bit of a wake-up call. The old approach of leaving systems half-maintained, delaying updates, or assuming that obscure software will somehow stay below the radar looks even more risky in a world where AI can inspect code at speed and scale.

Project Glasswing may turn out to be remembered as one of those early milestone moments, the point where the cybersecurity industry publicly acknowledged that AI is no longer just a helpful assistant for defenders. It is becoming a serious force multiplier, and one that could work for either side.

That makes this announcement both exciting and slightly chilling.

And, in true Gadget Man fashion, it is exactly the kind of development that reminds us technology is never just about shiny new tools. It is also about consequences, responsibility and how quickly the world has to adapt when the rules suddenly change.

Source

Anthropic, Project Glasswing: Securing critical software for the AI era

2026, Hacking, Security

Think Before You Scan: That QR Code May Be a Scam

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QR codes have become part of everyday life. Parking meters, restaurant menus, parcels, emails. A quick scan feels harmless. That is exactly why cybercriminals are increasingly abusing them.

This growing threat is known as quishing, short for QR code phishing. Instead of asking you to click a suspicious link, attackers persuade you to scan a code that quietly sends you somewhere you really did not intend to go.

At the start of January, the FBI issued a warning about a wave of attacks linked to North Korean cybercriminals who were using fake QR codes to harvest personal information. Security experts say this is not just a US problem. Similar attacks are now appearing across multiple countries, including the UK, as criminals look for new ways to make money.

The technique is simple but effective. Fake QR codes are placed over legitimate ones in public locations such as parking machines, cafés and kiosks. Scan the code and you are redirected to a convincing looking website that may ask for payment details or login credentials. Last year, UK government bodies warned motorists about QR stickers on parking meters that led victims to spoofed payment pages.

QR codes are also being used in email attacks. In one example highlighted by the FBI, a state sponsored group embedded malicious QR codes in emails to employees, presenting them as a way to download extra information. Scan first, think later. That is what the attackers are counting on.

According to cybersecurity experts at Planet VPN, the outcome is usually the same wherever the QR code appears. Once scanned, users are forwarded to a fake site designed to look genuine, whether that is a restaurant menu or a payment page. From there, credit card details, passwords or even full device access can be compromised.

Planet VPN co founder Konstantin Levinzon explains why QR codes are proving so effective. People tend to trust them. They became widespread during the pandemic and still do not trigger the same suspicion as a dodgy looking link. The risk feels lower because there is no visible URL to inspect, just a quick scan.

There is another reason attackers favour QR codes in emails. Many anti phishing systems analyse text and links but do not properly inspect images. A QR code can slip through where a traditional phishing email might be blocked. Even when detection improves, attackers adapt by changing colours or designs to evade filters.

The scale of the problem is significant. Cybersecurity researchers estimate that millions of QR related threats were recorded in just the first half of last year, and experts believe the real number is likely higher due to undetected scams.

Think Before You Scan: That QR Code May Be a Scam
Think Before You Scan: That QR Code May Be a Scam

So what should you do?

Be deliberate about scanning QR codes. Ask yourself why it is there and whether it makes sense. If a scan takes you to a site asking for payment or login details, treat that as a serious warning sign.

If a QR code arrives via email from someone you do not know, or even someone you do know but were not expecting to hear from, pause and verify it before entering any details or downloading anything.

Most importantly, apply the same common sense you would use elsewhere online. Stay sceptical. Use a VPN on public Wi Fi. Keep your devices updated. Use strong passwords and enable multi factor authentication wherever possible.

QR codes are convenient, but convenience is often what attackers exploit. A second of caution can save a great deal of hassle later.

2026, NordVPN, Passwords

Why ‘123456’ Is Still Ruining Business Security

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There are some things you expect to see in 2026. AI everywhere. Electric cars quietly taking over. Smart homes that know when you have run out of milk.

What you do not expect is that one of the biggest threats to company security is still someone typing 123456 into a login box.

Yet here we are.

A recent piece of research from NordPass lays it all bare. After analysing huge volumes of passwords exposed in real world data breaches, the conclusion is both fascinating and slightly terrifying. Business passwords are often no better than the ones we were being warned about twenty years ago.

The same bad habits, everywhere

Across sectors like healthcare, manufacturing, tech and finance, the patterns repeat themselves. Simple number sequences dominate. Obvious choices like 123456, 123456789 and password keep cropping up. In some cases people are even using their own email address as the password.

That last one is particularly grim. If your username is already public, you have effectively handed an attacker half the keys to the building.

What struck me most was how universal this problem is. This is not a single careless industry or a few unlucky firms. It is a human behaviour issue. Convenience beats caution every time unless systems are designed to protect us from ourselves.

Why attackers love this

From an attacker’s point of view, weak passwords are a gift. Automated tools can try millions of common combinations in seconds. If employees reuse passwords across systems, one breach can quietly unlock several more doors.

This is often how serious incidents begin. Not with Hollywood style hacking, but with someone guessing a password that should never have existed in the first place.

The uncomfortable truth for businesses

Here is the bit that matters. This is not really a technical problem. The tools to fix it have existed for years.

Strong password policies. Password managers. Multi factor authentication. Alerts for leaked credentials. None of this is exotic or expensive anymore.

What is missing is consistency and enforcement. Many organisations still rely on guidance rather than rules, or assume that staff will naturally do the right thing. History shows they will not, especially when speed and convenience are rewarded.

What actually works

From everything I have seen over the years, both professionally and personally, a few things make the biggest difference.

First, remove the burden from users. A good password manager means nobody has to remember anything clever.

Second, enforce unique passwords everywhere. No exceptions.

Third, enable multi factor authentication wherever possible, especially for email and admin accounts.

Finally, treat leaked passwords as inevitable, not hypothetical. Monitor for them and act quickly.

Still relevant, still risky

It is easy to laugh at 123456. It feels like a joke from the early days of the internet. But when that same password is still opening real company systems today, it stops being funny very quickly.

The NordPass research is a useful reminder that cybersecurity does not always fail at the cutting edge. More often, it fails at the front door.

And the front door is still wide open far too often.

2025, Hacking, Security

Cybernews Cracks the Password Puzzle: What 19 Billion Passwords Reveal About Us

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We’ve all been there — that moment when the password box blinks impatiently, waiting for your brain to produce something secure and memorable. But what if I told you that billions of us are still relying on “123456” or “password” to guard our digital front doors?

Researchers at Cybernews have just released a fascinating and somewhat alarming analysis of 19,030,305,929 leaked passwords, and the findings tell a revealing story about human behaviour, pop culture obsessions, and our often lacklustre approach to security.

Only 6% of Passwords Are Unique – Let That Sink In

Out of 19 billion passwords, only 1.1 billion were unique. That means 94% of people are using the same passwords as someone else — and in the world of hacking, that’s music to a cybercriminal’s ears.

The most common passwords? You’ve guessed them:

  • “password” – used 56 million times

  • “admin” – 53 million times

  • “123456” – 338 million times

  • “1234” – found in nearly 4% of all passwords (that’s 727 million accounts!)

It’s 2025 and we’re still living in a world where “qwerty” and “abc123” are considered acceptable safeguards.

What We Love Shows Up in Our Passwords

The team at Cybernews created themed wordlists to understand what people use in their passwords. The results are both amusing and a little worrying.

Pop Culture Reigns Supreme

Characters like:

  • Mario (9.6M)

  • Batman (3.9M)

  • Joker (3.1M)

  • Thor (6.2M)

  • Elsa (2.9M)

Yes, even a magical ice queen made it onto the hacker’s dictionary.

Our Favourite Animals?

  • Lion (9.8M)

  • Fox (7.8M)

These majestic creatures might be noble, but they’re not secure.

Love, Tea, and Swear Words

  • “Love” appears in 87 million passwords.

  • Tea – our national treasure – is in 36 million!

  • And surprisingly (or not), words like “ass” (165M), “fuck” (16M), and “shit” (6.5M) are extremely common.

Seems like when people get frustrated with password rules, they get… expressive.

The Human Factor: Names, Cities, and Seasons

We seem to really love personal and familiar things:

  • Ana is the most-used name (178.8M times).

  • Rome is the top city (13M).

  • Summer (3.8M) leads the seasons, while May (28M) and April (5.2M) dominate the months.

  • Monday is weirdly the most popular weekday (800,000).

And professions like boss, cook, and hunter show up in millions of passwords. Is that confidence or wishful thinking?

Big Brands in Passwords: Not a Great Idea

Believe it or not, some people think referencing tech giants will protect them:

  • Google – 25.9M passwords

  • Facebook – 18.7M

  • Kia – 12.7M (your guess is as good as mine)

Why This Matters (and What You Should Do About It)

With only 6% of passwords being unique, dictionary attacks and credential stuffing become a walk in the park for bad actors. If your password is on one of these lists, your account could be next.

Top Tips for Better Passwords:

  1. Use a Password Manager – Tools like Bitwarden, 1Password or KeePass make strong passwords easy.

  2. Avoid Dictionary Words – Even “FrostyTheSnowman1983” isn’t safe if enough people use it.

  3. Go Long and Complex – At least 12 characters, mixing letters, numbers, and symbols.

  4. Don’t Reuse Passwords – Ever.

  5. Turn On 2FA – Always enable two-factor authentication where possible.

The Bottom Line

Your password is often the only thing standing between your private data and a very bad day. The Cybernews report doesn’t just expose poor habits — it’s a stark reminder that humans are the weakest link in cybersecurity.

So, before you set your next password to “GadgetMan2025”, take a moment. You’re worth a better lock on your digital door.

Matt Porter – The Gadget Man

Have you checked your passwords recently? Would you like me to review any password manager tools for you next?