{"id":70645,"date":"2026-02-12T11:39:38","date_gmt":"2026-02-12T11:39:38","guid":{"rendered":"https:\/\/youzum.net\/ai-is-already-making-online-swindles-easier-it-could-get-much-worse\/"},"modified":"2026-02-12T11:39:38","modified_gmt":"2026-02-12T11:39:38","slug":"ai-is-already-making-online-swindles-easier-it-could-get-much-worse","status":"publish","type":"post","link":"https:\/\/youzum.net\/fr\/ai-is-already-making-online-swindles-easier-it-could-get-much-worse\/","title":{"rendered":"AI is already making online swindles easier. It could get much worse."},"content":{"rendered":"

Anton Cherepanov is always on the lookout for something interesting. And in late August last year, he spotted just that. It was a file uploaded to VirusTotal, a site cybersecurity researchers like him use to analyze submissions for potential viruses and other types of malicious software, often known as malware. On the surface it seemed innocuous, but it triggered Cherepanov\u2019s custom malware-detecting measures. Over the next few hours, he and his colleague Peter Str\u00fd\u010dek inspected the sample and realized they\u2019d never come across anything like it before.<\/p>\n

The file contained ransomware, a nasty strain of malware that encrypts the files it comes across on a victim\u2019s system, rendering them unusable until a ransom is paid to the attackers behind it. But what set this example apart was that it employed large language models (LLMs). Not just incidentally, but across every stage of an attack. Once it was installed, it could tap into an LLM to generate customized code in real time, rapidly map a computer to identify sensitive data to copy or encrypt, and write personalized ransom notes based on the files\u2019 content. The software could do this autonomously, without any human intervention. And every time it ran, it would act differently, making it harder to detect.<\/p>\n

Cherepanov and Str\u00fd\u010dek were confident that their discovery, which they dubbed PromptLock, marked a turning point in generative AI, showing how the technology could be exploited to create highly flexible malware attacks. They published a blog post<\/a> declaring that they\u2019d uncovered the first example of AI-powered ransomware, which quickly became the object of widespread<\/a> global<\/a> media<\/a> attention<\/a>.<\/p>\n

But the threat wasn\u2019t quite as dramatic as it first appeared. The day after the blog post went live, a team of researchers from New York University claimed responsibility,<\/a> explaining that the malware was not, in fact, a full attack let loose in the wild but a research project, merely designed to prove it was possible<\/em> to automate each step of a ransomware campaign\u2014which, they said, they had.\u00a0<\/p>\n

PromptLock may have turned out to be an academic project, but the real bad guys are<\/em> using the latest AI tools. Just as software engineers are using artificial intelligence to help write code and check for bugs<\/a>, hackers are using these tools to reduce the time and effort required to orchestrate an attack, lowering the barriers for less experienced attackers to try something out.\u00a0<\/p>\n

The likelihood that cyberattacks will now become more common and more effective over time is not a remote possibility but \u201ca sheer reality,\u201d says Lorenzo Cavallaro, a professor of computer science at University College London.\u00a0<\/p>\n

Some in Silicon Valley warn that AI is on the brink of being able to carry out fully automated attacks. But most security researchers say this claim is overblown. \u201cFor some reason, everyone is just focused on this malware idea of, like, AI superhackers, which is just absurd,\u201d says Marcus Hutchins, who is principal threat researcher at the security company Expel and famous in the security world for ending a giant global ransomware attack called WannaCry in 2017.\u00a0<\/p>\n

Instead, experts argue, we should be paying closer attention to the much more immediate risks posed by AI, which is already speeding up and increasing the volume of scams. Criminals are increasingly exploiting the latest deepfake technologies to impersonate people and swindle victims out of vast sums of money. These AI-enhanced cyberattacks are only set to get more frequent and more destructive, and we need to be ready.\u00a0<\/p>\n

Spam and beyond<\/h3>\n

Attackers started adopting generative AI tools almost immediately after ChatGPT exploded on the scene at the end of 2022. These efforts began, as you might imagine, with the creation of spam\u2014and a lot of it. Last year, a report from Microsoft said<\/a> that in the year leading up to April 2025, the company had blocked $4 billion worth of scams and fraudulent transactions, \u201cmany likely aided by AI content.\u201d\u00a0<\/p>\n

At least half of spam email is now generated using LLMs, according to estimates by researchers at Columbia University, the University of Chicago, and Barracuda Networks, who analyzed<\/a> nearly 500,000 malicious messages collected before and after the launch of ChatGPT. They also found evidence that AI is increasingly being deployed in more sophisticated schemes. They looked at targeted email attacks, which impersonate a trusted figure in order to trick a worker within an organization out of funds or sensitive information. By April 2025, they found, at least 14% of those sorts of focused email attacks were generated using LLMs, up from 7.6% in April 2024.<\/p>\n

\n

In one high-profile case, a worker was tricked into transferring $25 million to criminals via a video call with digital versions of the company\u2019s chief financial officer and other employees.<\/strong><\/p>\n<\/blockquote>\n

And the generative AI boom has made it easier and cheaper than ever before to generate not only emails but highly convincing images, videos, and audio. The results are much more realistic than even just a few short years ago, and it takes much less data to generate a fake version of someone\u2019s likeness or voice than it used to.<\/p>\n

Criminals aren\u2019t deploying these sorts of deepfakes to prank people or to simply mess around\u2014they\u2019re doing it because it works and because they\u2019re making money out of it, says Henry Ajder, a generative AI expert. \u201cIf there\u2019s money to be made and people continue to be fooled by it, they\u2019ll continue to do it,\u201d he says. In one high-\u00adprofile case<\/a> reported in 2024, a worker at the British engineering firm Arup was tricked into transferring $25 million to criminals via a video call with digital versions of the company\u2019s chief financial officer and other employees. That\u2019s likely only the tip of the iceberg, and the problem posed by convincing deepfakes is only likely to get worse as the technology improves and is more widely adopted.\u00a0<\/p>\n

\n
\"person\n
BRIAN STAUFFER<\/div>\n<\/figure>\n<\/div>\n

Criminals\u2019 tactics evolve all the time, and as AI\u2019s capabilities improve, such people are constantly probing how those new capabilities can help them gain an advantage over victims. Billy Leonard, tech leader of Google\u2019s Threat Analysis Group, has been keeping a close eye on changes in the use of AI by potential bad actors (a widely used term in the industry for hackers and others attempting to use computers for criminal purposes). In the latter half of 2024, he and his team noticed prospective criminals using tools like Google Gemini the same way everyday users do\u2014to debug code and automate bits and pieces of their work\u2014as well as tasking it with writing the odd phishing email. By 2025, they had progressed to using AI to help create new pieces of malware and release them into the wild, he says.<\/p>\n

The big question now is how far this kind of malware can go. Will it ever become capable enough to sneakily infiltrate thousands of companies\u2019 systems and extract millions of dollars, completely undetected?\u00a0<\/p>\n

Most popular AI models have guardrails in place to prevent them from generating malicious code or illegal material, but bad actors still find ways to work around them. For example, Google observed a China-linked actor asking its Gemini AI model to identify vulnerabilities on a compromised system\u2014a request it initially refused on safety grounds. However, the attacker managed to persuade Gemini to break its own rules by posing as a participant in a capture-the-flag competition, a popular cybersecurity game. This sneaky form of jailbreaking led Gemini to hand over information that could have been used to exploit the system. (Google has since adjusted Gemini to deny these kinds of requests.)<\/p>\n

But bad actors aren\u2019t just focusing on trying to bend the AI giants\u2019 models to their nefarious ends. Going forward, they\u2019re increasingly likely to adopt open-source AI models<\/a>, as it\u2019s easier to strip out their safeguards and get them to do malicious things, says Ashley Jess, a former tactical specialist at the US Department of Justice and now a senior intelligence analyst at the cybersecurity company Intel 471. \u201cThose are the ones I think that [bad] actors are going to adopt, because they can jailbreak them and tailor them to what they need,\u201d she says.<\/p>\n

The NYU team used two open-source models from OpenAI in its PromptLock experiment, and the researchers found they didn\u2019t even need to resort to jailbreaking techniques to get the model to do what they wanted. They say<\/a> that makes attacks much easier. Although these kinds of open-source models are designed with an eye to ethical alignment, meaning that their makers do consider certain goals and values in dictating the way they respond to requests, the models don\u2019t have the same kinds of restrictions as their closed-source counterparts, says Meet Udeshi, a PhD student at New York University who worked on the project. \u201cThat is what we were trying to test,\u201d he says. \u201cThese LLMs claim that they are ethically aligned\u2014can we still misuse them for these purposes? And the answer turned out to be yes.\u201d\u00a0<\/p>\n

It\u2019s possible that criminals have already successfully pulled off covert PromptLock-style attacks and we\u2019ve simply never seen any evidence of them, says Udeshi. If that\u2019s the case, attackers could\u2014in theory\u2014have created a fully autonomous hacking system. But to do that they would have had to overcome the significant barrier that is getting AI models to behave reliably, as well as any inbuilt aversion the models have to being used for malicious purposes\u2014all while evading detection. Which is a pretty high bar indeed.<\/p>\n

Productivity tools for hackers<\/h3>\n

So, what do we know for sure? Some of the best data we have now on how people are attempting to use AI for malicious purposes comes from the big AI companies themselves. And their findings certainly sound alarming, at least at first. In November, Leonard\u2019s team at Google released a report<\/a> that found bad actors were using AI tools (including Google\u2019s Gemini) to dynamically alter malware\u2019s behavior; for example, it could self-modify to evade detection. The team wrote that it ushered in \u201ca new operational phase of AI abuse.\u201d<\/p>\n

However, the five malware families the report dug into (including PromptLock) consisted of code that was easily detected and didn\u2019t actually do any harm, the cybersecurity writer Kevin Beaumont pointed out on social media<\/a>. \u201cThere\u2019s nothing in the report to suggest orgs need to deviate from foundational security programmes\u2014everything worked as it should,\u201d he wrote.<\/p>\n

It\u2019s true that this malware activity is in an early phase, concedes Leonard. Still, he sees value in making these kinds of reports public if it helps security vendors and others build better defenses to prevent more dangerous AI attacks further down the line. \u201cClich\u00e9 to say, but sunlight is the best disinfectant,\u201d he says. \u201cIt doesn\u2019t really do us any good to keep it a secret or keep it hidden away. We want people to be able to know about this\u2014 we want other security vendors to know about this\u2014so that they can continue to build their own detections.\u201d<\/p>\n

And it\u2019s not just new strains of malware that would-be attackers are experimenting with\u2014they also seem to be using AI to try to automate the process of hacking targets. In November, Anthropic announced<\/a> it had disrupted a large-scale cyberattack, the first reported case of one executed without \u201csubstantial human intervention.\u201d Although the company didn\u2019t go into much detail about the exact tactics the hackers used, the report\u2019s authors said a Chinese state-sponsored group had used its Claude Code assistant to automate up to 90% of what they called a \u201chighly sophisticated espionage campaign.\u201d<\/p>\n

\n

\u201cWe\u2019re entering an era where the barrier to sophisticated cyber operations has fundamentally lowered, and the pace of attacks will accelerate faster than many organizations are prepared for.\u201d<\/strong><\/p>\n

Jacob Klein, head of threat intelligence at Anthropic<\/cite><\/p><\/blockquote>\n

But, as with the Google findings, there were caveats. A human operator, not AI, selected the targets before tasking Claude with identifying vulnerabilities. And of 30 attempts, only a \u201chandful\u201d were successful. The Anthropic report also found that Claude hallucinated<\/a> and ended up fabricating data during the campaign, claiming it had obtained credentials it hadn\u2019t and \u201cfrequently\u201d overstating its findings, so the attackers would have had to carefully validate those results to make sure they were actually true. \u201cThis remains an obstacle to fully autonomous cyberattacks,\u201d the report\u2019s authors wrote.\u00a0<\/p>\n

Existing controls within any reasonably secure organization would stop these attacks, says Gary McGraw, a veteran security expert and cofounder of the Berryville Institute of Machine Learning in Virginia. \u201cNone of the malicious-attack part, like the vulnerability exploit \u2026 was actually done by the AI\u2014it was just prefabricated tools that do that, and that stuff\u2019s been automated for 20 years,\u201d he says. \u201cThere\u2019s nothing novel, creative, or interesting about this attack.\u201d<\/p>\n

Anthropic maintains that the report\u2019s findings are a concerning signal of changes ahead. \u201cTying this many steps of an intrusion campaign together through [AI] agentic orchestration is unprecedented,\u201d Jacob Klein, head of threat intelligence at Anthropic, said in a statement. \u201cIt turns what has always been a labor-intensive process into something far more scalable. We\u2019re entering an era where the barrier to sophisticated cyber operations has fundamentally lowered, and the pace of attacks will accelerate faster than many organizations are prepared for.\u201d<\/p>\n

Some are not convinced there\u2019s reason to be alarmed. AI hype<\/a> has led a lot of people in the cybersecurity industry to overestimate models\u2019 current abilities, Hutchins says. \u201cThey want this idea of unstoppable AIs that can outmaneuver security, so they\u2019re forecasting that\u2019s where we\u2019re going,\u201d he says. But \u201cthere just isn\u2019t any evidence to support that, because the AI capabilities just don\u2019t meet any of the requirements.\u201d<\/p>\n

\n
\"person\n
BRIAN STAUFFER<\/div>\n<\/figure>\n<\/div>\n

Indeed, for now criminals mostly seem to be tapping AI to enhance their productivity: using LLMs to write malicious code and phishing lures, to conduct reconnaissance, and for language translation. Jess sees this kind of activity a lot, alongside efforts to sell tools in underground criminal markets. For example, there are phishing kits that compare the click-rate success of various spam campaigns, so criminals can track which campaigns are most effective at any given time. She is seeing a lot of this activity in what could be called the \u201cAI slop landscape\u201d but not as much \u201cwidespread adoption from highly technical actors,\u201d she says.<\/p>\n

But attacks don\u2019t need to be sophisticated to be effective. Models that produce \u201cgood enough\u201d results allow attackers to go after larger numbers of people than previously possible, says Liz James, a managing security consultant at the cybersecurity company NCC Group. \u201cWe\u2019re talking about someone who might be using a scattergun approach phishing a whole bunch of people with a model that, if it lands itself on a machine of interest that doesn\u2019t have any defenses \u2026 can reasonably competently encrypt your hard drive,\u201d she says. \u201cYou\u2019ve achieved your objective.\u201d\u00a0<\/p>\n

On the defense<\/h3>\n

For now, researchers are optimistic about our ability to defend against these threats\u2014regardless of whether they are made with AI. \u201cEspecially on the malware side, a lot of the defenses and the capabilities and the best practices that we\u2019ve recommended for the past 10-plus years\u2014they all still apply,\u201d says Leonard. The security programs we use to detect standard viruses and attack attempts work; a lot of phishing emails will still get caught in inbox spam filters, for example. These traditional forms of defense will still largely get the job done\u2014at least for now.\u00a0<\/p>\n

And in a neat twist, AI itself is helping to counter security threats more effectively. After all, it is excellent at spotting patterns and correlations. Vasu Jakkal, corporate vice president of Microsoft Security, says that every day, the company processes more than 100 trillion signals flagged by its AI systems as potentially malicious or suspicious events.<\/p>\n

Despite the cybersecurity landscape\u2019s constant state of flux, Jess is heartened by how readily defenders are sharing detailed information with each other about attackers\u2019 tactics. Mitre\u2019s Adversarial Threat Landscape for Artificial-Intelligence Systems<\/a> and the GenAI Security Project from the Open Worldwide Application Security Project are two helpful initiatives documenting how potential criminals are incorporating AI into their attacks and how AI systems are being targeted by them. \u201cWe\u2019ve got some really good resources out there for understanding how to protect your own internal AI toolings and understand the threat from AI toolings in the hands of cybercriminals,\u201d she says.<\/p>\n

PromptLock, the result of a limited university project, isn\u2019t representative of how an attack would play out in the real world. But if it taught us anything, it\u2019s that the technical capabilities of AI shouldn\u2019t be dismissed.New York University\u2019s Udeshi says he wastaken aback at how easily AI was able to handle a full end-to-end chain of attack, from mapping and working out how to break into a targeted computer system to writing personalized ransom notes to victims: \u201cWe expected it would do the initial task very well but it would stumble later on, but we saw high\u201480% to 90%\u2014success throughout the whole pipeline.\u201d\u00a0<\/p>\n

AI is still evolving rapidly, and today\u2019s systems are already capable of things that would have seemed preposterously out of reach just a few short years ago. That makes it incredibly tough to say with absolute confidence what it will\u2014or won\u2019t\u2014be able to achieve in the future. While researchers are certain that AI-driven attacks are likely to increase in both volume and severity, the forms they could take are unclear. Perhaps the most extreme possibility is that someone makes an AI model capable of creating and automating its own zero-day exploits\u2014highly dangerous cyber\u00adattacks that take advantage of previously unknown vulnerabilities in software. But building and hosting such a model\u2014and evading detection\u2014would require billions of dollars in investment, says Hutchins, meaning it would only be in the reach of a wealthy nation-state.\u00a0<\/p>\n

Engin Kirda, a professor at Northeastern University in Boston who specializes in malware detection and analysis, says he wouldn\u2019t be surprised if this was already happening. \u201cI\u2019m sure people are investing in it, but I\u2019m also pretty sure people are already doing it, especially [in] China\u2014they have good AI capabilities,\u201d he says.\u00a0<\/p>\n

It\u2019s a pretty scary possibility. But it\u2019s one that\u2014thankfully\u2014is still only theoretical. A large-scale campaign that is both effective and clearly AI-driven has yet to materialize. What we can say is that generative AI is already significantly lowering the bar for criminals. They\u2019ll keep experimenting with the newest releases and updates and trying to find new ways to trick us into parting with important information and precious cash. For now, all we can do is be careful, remain vigilant, and\u2014for all our sakes\u2014stay on top of those system updates.\u00a0<\/p>","protected":false},"excerpt":{"rendered":"

Anton Cherepanov is always on the lookout for something interesting. And in late August last year, he spotted just that. It was a file uploaded to VirusTotal, a site cybersecurity researchers like him use to analyze submissions for potential viruses and other types of malicious software, often known as malware. On the surface it seemed innocuous, but it triggered Cherepanov\u2019s custom malware-detecting measures. Over the next few hours, he and his colleague Peter Str\u00fd\u010dek inspected the sample and realized they\u2019d never come across anything like it before. The file contained ransomware, a nasty strain of malware that encrypts the files it comes across on a victim\u2019s system, rendering them unusable until a ransom is paid to the attackers behind it. But what set this example apart was that it employed large language models (LLMs). Not just incidentally, but across every stage of an attack. Once it was installed, it could tap into an LLM to generate customized code in real time, rapidly map a computer to identify sensitive data to copy or encrypt, and write personalized ransom notes based on the files\u2019 content. The software could do this autonomously, without any human intervention. And every time it ran, it would act differently, making it harder to detect. Cherepanov and Str\u00fd\u010dek were confident that their discovery, which they dubbed PromptLock, marked a turning point in generative AI, showing how the technology could be exploited to create highly flexible malware attacks. They published a blog post declaring that they\u2019d uncovered the first example of AI-powered ransomware, which quickly became the object of widespread global media attention. But the threat wasn\u2019t quite as dramatic as it first appeared. The day after the blog post went live, a team of researchers from New York University claimed responsibility, explaining that the malware was not, in fact, a full attack let loose in the wild but a research project, merely designed to prove it was possible to automate each step of a ransomware campaign\u2014which, they said, they had.\u00a0 PromptLock may have turned out to be an academic project, but the real bad guys are using the latest AI tools. Just as software engineers are using artificial intelligence to help write code and check for bugs, hackers are using these tools to reduce the time and effort required to orchestrate an attack, lowering the barriers for less experienced attackers to try something out.\u00a0 The likelihood that cyberattacks will now become more common and more effective over time is not a remote possibility but \u201ca sheer reality,\u201d says Lorenzo Cavallaro, a professor of computer science at University College London.\u00a0 Some in Silicon Valley warn that AI is on the brink of being able to carry out fully automated attacks. But most security researchers say this claim is overblown. \u201cFor some reason, everyone is just focused on this malware idea of, like, AI superhackers, which is just absurd,\u201d says Marcus Hutchins, who is principal threat researcher at the security company Expel and famous in the security world for ending a giant global ransomware attack called WannaCry in 2017.\u00a0 Instead, experts argue, we should be paying closer attention to the much more immediate risks posed by AI, which is already speeding up and increasing the volume of scams. Criminals are increasingly exploiting the latest deepfake technologies to impersonate people and swindle victims out of vast sums of money. These AI-enhanced cyberattacks are only set to get more frequent and more destructive, and we need to be ready.\u00a0 Spam and beyond Attackers started adopting generative AI tools almost immediately after ChatGPT exploded on the scene at the end of 2022. These efforts began, as you might imagine, with the creation of spam\u2014and a lot of it. Last year, a report from Microsoft said that in the year leading up to April 2025, the company had blocked $4 billion worth of scams and fraudulent transactions, \u201cmany likely aided by AI content.\u201d\u00a0 At least half of spam email is now generated using LLMs, according to estimates by researchers at Columbia University, the University of Chicago, and Barracuda Networks, who analyzed nearly 500,000 malicious messages collected before and after the launch of ChatGPT. They also found evidence that AI is increasingly being deployed in more sophisticated schemes. They looked at targeted email attacks, which impersonate a trusted figure in order to trick a worker within an organization out of funds or sensitive information. By April 2025, they found, at least 14% of those sorts of focused email attacks were generated using LLMs, up from 7.6% in April 2024. In one high-profile case, a worker was tricked into transferring $25 million to criminals via a video call with digital versions of the company\u2019s chief financial officer and other employees. And the generative AI boom has made it easier and cheaper than ever before to generate not only emails but highly convincing images, videos, and audio. The results are much more realistic than even just a few short years ago, and it takes much less data to generate a fake version of someone\u2019s likeness or voice than it used to. Criminals aren\u2019t deploying these sorts of deepfakes to prank people or to simply mess around\u2014they\u2019re doing it because it works and because they\u2019re making money out of it, says Henry Ajder, a generative AI expert. \u201cIf there\u2019s money to be made and people continue to be fooled by it, they\u2019ll continue to do it,\u201d he says. In one high-\u00adprofile case reported in 2024, a worker at the British engineering firm Arup was tricked into transferring $25 million to criminals via a video call with digital versions of the company\u2019s chief financial officer and other employees. That\u2019s likely only the tip of the iceberg, and the problem posed by convincing deepfakes is only likely to get worse as the technology improves and is more widely adopted.\u00a0 BRIAN STAUFFER Criminals\u2019 tactics evolve all the time, and as AI\u2019s capabilities improve, such people are constantly probing how those new capabilities can help them gain an advantage over victims.<\/p>","protected":false},"author":2,"featured_media":70646,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-70645","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"\nAI is already making online swindles easier. It could get much worse. - YouZum<\/title>\n<meta name=\"description\" content=\"\u0e01\u0e34\u0e08\u0e01\u0e23\u0e23\u0e21\u0e40\u0e01\u0e35\u0e48\u0e22\u0e27\u0e01\u0e31\u0e1a\u0e42\u0e14\u0e23\u0e19\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/youzum.net\/fr\/ai-is-already-making-online-swindles-easier-it-could-get-much-worse\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI is already making online swindles easier. 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