Security researchers accessed an internal camera inside the Deckmate 2 shuffler to learn the exact deck order—and the hand of every player at a poker table.

In September last year, a scandal blew up the world of high-stakes, livestreamed poker: In a hand at Las Vegas’ Hustler Live Casino, which broadcast its games on YouTube, a relative novice holding nothing but a jack of clubs and a four of hearts successfully called the bluff of a veteran player. No one could possibly think that poor hand might be good enough to call a bluff, thousands of outraged poker players argued, unless the person holding it had some extra knowledge that her opponent's hand was even worse—in other words, she must have been cheating.

Three months later, Hustler Live Casino published a postmortem of its investigation into the incident, finding “no credible evidence” of foul play. It also noted that if there were cheating, it was most likely some sort of secret communication between the player and a staff member in the production booth who could see the players' hands in real time. But when Joseph Tartaro, a researcher and consultant with security firm IOActive, read that report, he zeroed in on one claim in particular—a statement ruling out any possibility that the automated card-shuffling machine used at the table, a device known as the Deckmate, could have been hacked. “The Deckmate shuffling machine is secure and cannot be compromised,” the report read.

To Tartaro, regardless of what happened in the Hustler Live hand, that assertion of the shuffler's perfect security was an irresistible invitation to prove otherwise. “At that point, it's a challenge,” Tartaro says. “Let's look at one of these things and see how realistic it really is to cheat.”

IOActive’s card shuffler hackers (from left to right) Ethan Shackelford, Enrique Nissim, and Joseph Tartaro.Photograph: Roger Kisby

Today, at the Black Hat security conference in Las Vegas, Tartaro and two IOActive colleagues, Enrique Nissim and Ethan Shackelford, will present the results of their ensuing months-long investigation into the Deckmate, the most widely used automated shuffling machine in casinos today. They ultimately found that if someone can plug a small device into a USB port on the most modern version of the Deckmate—known as the Deckmate 2, which they say often sits under a table next to players’ knees, with its USB port exposed—that hacking device could alter the shuffler’s code to fully hijack the machine and invisibly tamper with its shuffling. They found that the Deckmate 2 also has an internal camera designed to ensure that every card is present in the deck, and that they could gain access to that camera to learn the entire order of the deck in real time, sending the results from their small hacking device via Bluetooth to a nearby phone, potentially held by a partner who then could then send coded signals to the cheating player.

In sum, their shuffler hacking technique gives a cheater “100 percent full control,” says Tartaro, who demonstrates IOActive’s findings in the video below. “Basically, it allows us to do more or less whatever we want … We can, for example, just read the constant data from the camera so we can know the deck order, and when that deck goes out into play, we know exactly the hand that everyone is going to have.”

For now, the IOActive researchers say they haven't yet had time to engineer a technique that would cause the Deckmate to put the deck in the exact order of their choosing—although they're certain that too would be possible. Regardless, they argue, merely knowing the full card order, rather than changing it, offers an even more practical cheating strategy, one that's far harder to detect.

Tartaro says the technique could be used to cheat in any number of card games, but that it would be particularly powerful in Texas Hold'em, the popular version of poker played in most casinos, including in the notorious Hustler Live Casino hand. That's because in Texas Hold'em, knowing the order of a deck would allow someone to predict the exact makeup of everyone's hand, independent of any decisions they make in the game. Even if a dealer cuts the deck before dealing, as most do in high-stakes casino games, Tartaro says the cheating player would still be able to immediately figure out the order of the cards on the top of the deck and in every player's hands as soon as the three “flop” cards are exposed—the public-facing, shared cards dealt out at the beginning of a Hold'em hand.

The IOActive team also looked at the earlier model of the Deckmate, known as the Deckmate 1, which has no external USB port and no internal camera. The researchers say that the earlier model, which was the one actually used in the Hustler Live Casino game, could nonetheless still be hacked to cheat in a game if a rogue casino staffer or maintenance person had an opportunity to open the shuffler's case and access a particular chip that stores its code. In that case, despite the lack of an internal camera, the cheater could still hack the shuffler to reorder cards—or they could simply prevent the Deckmate from shuffling the deck when a dealer picks up everyone's cards after a hand, giving the cheater information about the location of those previously played cards. "A skilled player with that little bit of an edge would 100 percent clean up," says Tartaro.

Hackers Rig Casino Card-Shuffling Machines for ‘Full Control’ Cheating

Security researchers set up a remote machine and recorded every move cybercriminals made—including their login details.

Plenty of people tried to access the system. Over the past three years, it has captured 21 million login attempts, with more than 2,600 successful logins by attackers brute-forcing the weak password they purposefully used on the system. They recorded 2,300 of these successful logins, gathered 470 files that were uploaded, and analyzed 339 of the videos with useful footage. (Some recordings were just a couple of seconds long, and proved less useful.) “We cataloged the techniques, the tooling, everything done on these systems,” Bilodeau says.

Bergeron and Bilodeau have grouped the attackers into five broad categories based on character types from the role-playing game Dungeons and Dragons. Most common were the rangers: once these attackers were inside the trap RDP session, they would immediately start exploring the system, removing Windows antivirus tools, delving into folders, looking at the network it was on and other elements of the machine. Rangers wouldn’t take any action, Bergeron says. “It's basic recon,” she says, suggesting they may be evaluating the system for others to enter it.

Barbarians were the next most frequent kind of attackers. These use multiple hacking tools, such as Masscan and NLBrute, to brute-force their way into other computers, the researchers say. They work through a list of IP addresses, usernames, and passwords, trying to break into the machines. Similarly, the group they call wizards use their access to the RDP to launch attacks against other insecure RDPs—potentially masking their identity across many layers. “They use the RDP access as a portal to connect to other computers,” Bergeron says.

The thieves, meanwhile, do what their name implies. They try to make money out of the RDP access in any way possible. They use traffic monetization websites and install crypto miners, the researchers say. They might not earn a lot in one go, but multiple compromises can add up.

The final group Bergeron and Bilodeau observed is the most haphazard: the bards. These people, the researchers say, may have purchased access to the RDP and are using it for a variety of reasons. One person the researchers watched Googled the “strongest virus ever,” Bergeron says, while another tried to access Google Ads.

Others simply tried (and failed) to find porn. “We can see the beginner level he is in, as he searched for porn on YouTube—nothing appears, of course,” Bergeron says, since YouTube doesn’t permit pornography. Multiple sessions were spotted trying to access porn, the researchers say, and these users were always writing in Farsi, indicating they may be trying to access porn in places where it is blocked. (The researchers weren’t able to determine conclusively where many of those accessing the RDP were doing so from.)

Despite this, watching the attackers reveals the way they behave, including some more peculiar actions. Bergeron, who has a PhD in criminology, says the attackers were sometimes “very slow” at doing their work. Often she was “getting impatient” while watching them, she says. “I’m like: ‘Come on, you're not good at that’ or 'Go faster’ or ‘Go deeper,’ or ‘You can do better.’”

A Clever Honeypot Tricked Hackers Into Revealing Their Secrets

You can also set up alerts for whenever your home address, phone number, or email address appears in Search.

In 2022, Google launched the “Results about you” tool to help people remove personal info from the company’s search results. With billions of searches happening daily on Google, finding your private phone number or home address indexed for the world to see can be quite shocking. Luckily, new updates to “Results about you” make it easier to discover when your data pops up in Search.

Previously, you had to proactively find the links of sites hosting your personal data to report and request the removal of identifiable information. Now, you’ll be able to set up alerts for whenever your email, home address, or phone number appear on Google. At first, this update will only be available in the US and scan for results just in English.

“Results about you” is accessible in your browser or through the mobile app. For browsers, log into Google or create an account, then visit this webpage to get started. If you’re using the app (Android, Apple), tap on your profile icon, then select the option from the menu.

After the update fully rolls out, here’s where you’ll find tabs labeled **Results to review** and **Reviewed**. Input the personal data you’d like to be notified about if it appears in Search, and results containing that info will appear in the “Results to review” section. The mobile app can send you a push notification when new results are added.

_Google’s “Results about you” tool is currently in beta._

Courtesy of Google

For more detailed directions about what info you’ll need in order to submit the removal request to Google, check out my article that steps you through the complete process. Once your request is submitted, use the “Results about you” tool to track whether the request is in progress, approved, or denied. Here’s three things worth keeping in mind:

*   This is just a request. The people at Google reviewing your request could decide to do nothing, remove the result when associated with your name, or completely remove the result.
*   Some of the other personal info that you can request for removal includes social security numbers, credit cards, handwritten signatures, medical records, and login credentials.
*   Google can remove the search result to lessen traffic, but you have to contact the owner or hosting service behind the webpage where it’s posted to get the data scrubbed completely.

In the past, criticism of Google’s privacy features focused on how these tools placed the onus on users to defend themselves. By streamlining aspects of the removal process for personal information and enabling proactive notifications, the company is making small, but important, moves toward less burdensome privacy controls.

“Results about you” is a free tool worth using, but if you have concerns about online doxxing, it might be worth considering a paid service as well, like DeleteMe, that tries to thwart online data brokers. Want to go even more incognito online? Try some of WIRED’s tips for disappearing from the internet.

How to Remove Your Info From Google With the 'Results About You' Tool

The vulnerability could allow attackers to take advantage of an information leak to steal sensitive details like private messages, passwords, and encryption keys.

Intel is releasing fixes for a processor vulnerability that affects many models of its chips going back to 2015, including some that are currently sold, the company revealed today. The flaw does not impact Intel’s latest processor generations. The vulnerability could be exploited to circumvent barriers meant to keep data isolated, and therefore private, on a system. This could allow attackers to grab valuable and sensitive data from victims, including financial details, emails, and messages, but also passwords and encryption keys.

It’s been more than five years since the Spectre and Meltdown processor vulnerabilities sparked a wave of revisions to computer chip designs across the industry. The flaws represented specific bugs but also conceptual data protection vulnerabilities in the schemes chips were using to make data available for processing more quickly and speed that processing. Intel has invested heavily in the years since these so-called speculative execution issues surfaced to identify similar types of design issues that could be leaking data. But the need for speed remains a business imperative, and both researchers and chip companies still find flaws in efficiency measures.

This latest vulnerability, dubbed Downfall by Daniel Moghimi, the Google researcher who discovered it, occurs in chip code that can use an instruction known as Gather to access scattered data more quickly in memory. Intel refers to the flaw as Gather Data Sampling after one of the techniques Moghimi developed to exploit the vulnerability. Moghimi will present his findings at the Black Hat security conference in Las Vegas on Wednesday.

“Memory operations to access data that is scattered in memory are very useful and make things faster, but whenever things are faster there’s some type of optimization—something the designers do to make it faster,” Moghimi says. “Based on my past experience working on these types of vulnerabilities, I had an intuition that there could be some kind of information leak with this instruction.”

The vulnerability affects the Skylake chip family, which Intel produced from 2015 to 2019; the Tiger Lake family, which debuted in 2020 and will discontinue early next year; and the Ice Lake family, which debuted in 2019 and was largely discontinued in 2021. Intel's current generation chips—including those in the Alder Lake, Raptor Lake, and Sapphire Rapids families—are not affected, because attempts to exploit the vulnerability would be blocked by defenses Intel has added recently.

The fixes are being released with an option to disable them because of the potential that they could have an intolerable impact on performance for certain enterprise users. “For most workloads, Intel has not observed reduced performance due to this mitigation. However, certain vectorization-heavy workloads may see some impact,” Intel said in a statement.

Releasing fixes for vulnerabilities like Downfall is always complicated, because in most cases, they must funnel through each manufacturer who makes devices that incorporate the affected chips, before actually reaching computers. These device-makers take code provided by Intel and create tailored patches that can then be downloaded by users. After years of releasing fixes in this complex ecosystem, Intel is practiced at coordinating the process, but it still takes time. Moghimi first disclosed Downfall to Intel a year ago.

“Over the past few years, the process with Intel has improved, but broadly in the hardware industry we need agility in how we address and respond to these kinds of issues,” Moghimi says. “Companies need to be able to respond faster and speed up the process of issuing firmware fixes, microcode fixes, because waiting one year is a big window when anyone else could find and exploit this.”

Moghimi also notes that it is difficult to detect Downfall attacks, because they mostly manifest as benign software activity. He adds, though, that it might be possible to develop a detection system that monitors hardware behavior for signs of abuse like unusual cache activity.

Intel says that it would be “complex” and difficult to carry out Downfall attacks in real-world conditions, but Moghimi emphasizes that it took him only a few weeks to develop proofs of concept for the attack. And he says that relative to other speculative execution vulnerabilities and related bugs, Downfall would be one of the more doable flaws for a motivated and well-resourced attacker to exploit.

“This vulnerability enables an attacker to essentially spy on other processes and steal data by analyzing the data leak over time for a combination of patterns that indicates the information the attacker is looking for, like login credentials or encryption keys,” Moghimi says. He adds that it would likely take time, on the scale of hours or even weeks, for an attacker to develop the pattern or fingerprint of the data they're looking for, but the payoff would be significant.

“I probably could have sold my findings to one of these exploit brokers—you could develop it into an exploit—but I'm not in that business. I’m a researcher,” Moghimi says.

He adds that Downfall seems to only impact Intel chips, but that it's possible similar types of flaws are lurking on processors made by other manufacturers. “Even though this particular release is not affecting other manufacturers directly," Moghimi says, "they need to learn from it and invest a lot more in verification.”

New ‘Downfall’ Flaw Exposes Valuable Data in Generations of Intel Chips

Think US health data is automatically kept private? Think again.

A former Department of Homeland Security adviser and a doctor, Chris Pierson is CEO of BlackCloak, a company that specializes in personal digital protection from financial fraud, cybercrime, reputational damage, and identity theft. He believes vigilance is key for doctors and patients alike.

Protect Your Entire Family

“I don’t think people realize that once someone is able to get just one piece of information, that can lead to opening others’ private data,” Pierson says. “It’s no longer the original individual on their computer, but additional family members’ identity that can be compromised.”

He explains that even if one organization keeps your data safe, another associated one may not, and that’s where criminals will strike. 

“It’s not just medical offices. It’s your pharmacy, labs, insurance company, anyone who keeps personal information. That has real value, and selling it is the priority.”

Victims of identity theft can be revictimized when personal information gets into multiple hands. A street address and verified phone number can go far, especially if the phone contains many contacts, who then become vulnerable to attack themselves.

“If you get Mom’s info, you can get the child’s as well. An ID card, social security, all of it, and then they have the ability to collect false medical claims or just extortion. It’s a two for one.”

Two-Factor Authentication Is Worth the Effort

Pierson mentions how critically important it is to use a multistep authentication system. Your level of protection goes up considerably just by using secure passwords and one-time authentication codes.

Thankfully, setting all this up is easier than it sounds. Apps on your phone or tablet can help. Google Authenticator, when paired with a service that supports authenticator apps, provides a six-digit number that changes every few seconds and can keep people out of your data even if they have your username and password. Other companies ask users to enter an SMS code as the second authentication factor, in addition to a password, although SMS codes are less secure than authenticator apps. Either approach is better than none—unless a hacker is in physical possession of your phone, they are not getting access.

Social Media and Tracking

Social media is becoming a popular way for health care providers and entrepreneurs to connect with the public—and often to sell them treatments or advice. These Instagram or TikTok accounts may offer tips from someone in the medical industry, which can appeal to those facing rising health care costs and difficulties accessing care. But an internet doctor’s background or popularity does not ensure that they observe strong privacy guidelines or secure their transactions.

My Instagram is flooded with offers promising everything from better sleep to improved sexual health. It’s nice to have options, but that help and any information you receive from those accounts or send to them isn’t covered under HIPAA. Any time you pay out of your own pocket for health-related items or services, or on a direct-to-consumer health app, there is no recourse if someone steals your personal information or shares it.

Along with social media and direct-to-consumer health options comes large-scale data tracking. Outside of official medical practices, you should view surveillance as an expectation, rather than an exception.

Ask Questions

When you sign up for any service, whether through a new doctor’s patient portal or an online supplement shop, ask how your data is stored and where it goes. Read the privacy policies and settings, even briefly, to find out what options you have to restrict the sale or reuse of your data. Check the default settings to make sure you’re not giving away too much information. Find out if the service or platform offers two-factor authentication and set that up if it’s available. Know that it’s rare for anyone to need your social security number, no matter what a customer service agent says. A birth date and address is usually enough.

Pierson and others agree that we all need to consider security from several angles and do our best to protect ourselves and our loved ones. “The sophistication of identity attacks will always evolve and change. Remember, they only have to get it right once, but we have to guess right all of the time.”

What Doctors Wish You Knew About HIPAA and Data Security

Soon after Russian troops invaded Ukraine in February 2022, sensors in the Chernobyl Exclusion Zone reported radiation spikes. A researcher now believes he’s found evidence the data was manipulated.

The Mystery of Chernobyl’s Post-Invasion Radiation Spikes

Since 2018, a dedicated team within Microsoft has attacked machine learning systems to make them safer. But with the public release of new generative AI tools, the field is already evolving.

For most people, the idea of using artificial intelligence tools in daily life—or even just messing around with them—has only become mainstream in recent months, with new releases of generative AI tools from a slew of big tech companies and startups, like OpenAI's ChatGPT and Google's Bard. But behind the scenes, the technology has been proliferating for years, along with questions about how best to evaluate and secure these new AI systems. On Monday, Microsoft is revealing details about the team within the company that since 2018 has been tasked with figuring out how to attack AI platforms to reveal their weaknesses.

In the five years since its formation, Microsoft's AI red team has grown from what was essentially an experiment into a full interdisciplinary team of machine learning experts, cybersecurity researchers, and even social engineers. The group works to communicate its findings within Microsoft and across the tech industry using the traditional parlance of digital security, so the ideas will be accessible rather than requiring specialized AI knowledge that many people and organizations don't yet have. But in truth, the team has concluded that AI security has important conceptual differences from traditional digital defense, which require differences in how the AI red team approaches its work.

“When we started, the question was, ‘What are you fundamentally going to do that’s different? Why do we need an AI red team?’” says Ram Shankar Siva Kumar, the founder of Microsoft's AI red team. “But if you look at AI red teaming as only traditional red teaming, and if you take only the security mindset, that may not be sufficient. We now have to recognize the responsible AI aspect, which is accountability of AI system failures—so generating offensive content, generating ungrounded content. That is the holy grail of AI red teaming. Not just looking at failures of security but also responsible AI failures.”

Shankar Siva Kumar says it took time to bring out this distinction and make the case that the AI red team's mission would really have this dual focus. A lot of the early work related to releasing more traditional security tools like the 2020 Adversarial Machine Learning Threat Matrix, a collaboration between Microsoft, the nonprofit R&D group MITRE, and other researchers. That year, the group also released open source automation tools for AI security testing, known as Microsoft Counterfit. And in 2021, the red team published an additional AI security risk assessment framework.

Over time, though, the AI red team has been able to evolve and expand as the urgency of addressing machine learning flaws and failures becomes more apparent. 

In one early operation, the red team assessed a Microsoft cloud deployment service that had a machine learning component. The team devised a way to launch a denial of service attack on other users of the cloud service by exploiting a flaw that allowed them to craft malicious requests to abuse the machine learning components and strategically create virtual machines, the emulated computer systems used in the cloud. By carefully placing virtual machines in key positions, the red team could launch “noisy neighbor” attacks on other cloud users, where the activity of one customer negatively impacts the performance for another customer.

The red team ultimately built and attacked an offline version of the system to prove that the vulnerabilities existed, rather than risk impacting actual Microsoft customers. But Shankar Siva Kumar says that these findings in the early years removed any doubts or questions about the utility of an AI red team. “That’s where the penny dropped for people,” he says. “They were like, ‘Holy crap, if people can do this, that’s not good for the business.’”

Crucially, the dynamic and multifaceted nature of AI systems means that Microsoft isn't just seeing the most highly resourced attackers targeting AI platforms. “Some of the novel attacks we’re seeing on large language models—it really just takes a teenager with a potty mouth, a casual user with a browser, and we don’t want to discount that,” Shankar Siva Kumar says. “There are APTs, but we also acknowledge that new breed of folks who are able to bring down LLMs and emulate them as well.”

As with any red team, though, Microsoft's AI red team isn't just researching attacks that are being used in the wild right now. Shankar Siva Kumar says that the group is focused on anticipating where attack trends may go next. And that often involves an emphasis on the newer AI accountability piece of the red team's mission. When the group finds a traditional vulnerability in an application or software system, they often collaborate with other groups within Microsoft to get it fixed rather than take the time to fully develop and propose a fix on their own. 

“There are other red teams within Microsoft and other Windows infrastructure experts or whatever we need,” Shankar Siva Kumar says. “The insight for me is that AI red teaming now encompasses not just security failures, but responsible AI failures.”

Microsoft’s AI Red Team Has Already Made the Case for Itself

Cybercriminals are touting large language models that could help them with phishing or creating malware. But the AI chatbots could just be their own kind of scam.

It didn't take long. Just months after OpenAI’s ChatGPT chatbot upended the startup economy, cybercriminals and hackers are claiming to have created their own versions of the text-generating technology. The systems could, theoretically at least, supercharge criminals’ ability to write malware or phishing emails that trick people into handing over their login information.

Since the start of July, criminals posting on dark-web forums and marketplaces have been touting two large language models (LLMs) they say they’ve produced. The systems, which are said to mimic the functionalities of ChatGPT and Google’s Bard, generate text to answer the questions or prompts users enter. But unlike the LLMs made by legitimate companies, these chatbots are marketed for illegal activities.

There are outstanding questions about the authenticity of the chatbots. Cybercriminals are not exactly trustworthy characters, and there remains the possibility that they’re trying to make a quick buck by scamming each other. Despite this, the developments come at a time when scammers are exploiting the hype of generative AI for their own advantage.

In recent weeks, two chatbots have been advertised on dark-web forums—WormGPT and FraudGPT—according to security researchers monitoring the activity. The LLMs developed by large tech companies, such as Google, Microsoft, and OpenAI, have a number of guardrails and safety measures in place to stop them from being misused. If you ask them to generate malware or write hate speech, they’ll generally refuse.

The shady LLMs claim to strip away any kind of safety protections or ethical barriers. WormGPT was first spotted by independent cybersecurity researcher Daniel Kelly, who worked with security firm SlashNext to detail the findings. WormGPT’s developers claim the tool offers an unlimited character count and code formatting. “The AI models are notably useful for phishing, particularly as they lower the entry barriers for many novice cybercriminals,” Kelly says in an email. “Many people argue that most cybercriminals can compose an email in English, but this isn’t necessarily true for many scammers.”

In a test of the system, Kelly writes, it was asked to produce an email that could be used as part of a business email compromise scam, with a purported CEO writing to an account manager to say an urgent payment was needed. “The results were unsettling,” Kelly wrote in the research. The system produced “an email that was not only remarkably persuasive but also strategically cunning.”

In forum posts, the WormGPT developer claimed the system was built on the GPTJ language model, an open source language model that was developed by AI research group EleutherAI in 2021. They refused to disclose the data sets they used to train the system, according to Kelly’s research.

Meanwhile, the creator of FraudGPT has claimed loftier potential for their system, suggesting it could “create undetectable malware” and find leaks and vulnerabilities, as well as crafting text that could be used in online scams. Rakesh Krishnan, the senior threat analyst at security firm Netenrich who found FraudGPT, says the person selling it has advertised the product on multiple dark-web forums and also on Telegram channels.

Krishnan says the creator of the system published a video appearing to show the chatbot operating and generating a scammy email. They were also trying to sell access to the system for $200 per month, or a yearly cost of $1,700. Krishnan says that in conversations with the developer behind FraudGPT, they claimed to have a few hundred subscribers and pushed for a sale, while the WormGPT creator appeared to have received payments into a cryptocurrency wallet address they shared. “All these projects are in their infancy,” Krishnan says. He adds, “we haven’t got much feedback” into whether people are purchasing or using the systems.

While those touting the chatbots claim they exist, it is hard to verify the makeup and legitimacy of the systems. Cybercriminal scammers are known to scam other scammers, with previous research showing that they frequently try to rip each other off, don’t provide what they claim they are selling, and offer bad customer service. Sergey Shykevich, a threat intelligence group manager at security firm Check Point, says there are some hints that people are using WormGTP. “It seems there is a real tool,” Shykevich says. The seller behind the tool is “relatively reliable” and has a history on cybercrime forums, he says.

There are more than 100 responses to one post about the WormGPT, Shykevich says, although some of these say the seller isn’t very responsive to their inquiries and others “weren’t very excited” about the system. Shykevich is less convinced about FraudGPT’s authenticity—the seller has also claimed to have systems called DarkBard and DarkBert. Shykevich says some of the posts from the seller were removed from the forums. Either way, the Check Point researcher says there’s no sign that any of the systems are more capable than ChatGPT, Bard, or other commercial LLMs.

Kelly says he believes claims about the malicious LLMs created so far are “slightly overexaggerated.” But he adds, “this is not necessarily different from what legitimate businesses do in the real world.”

Despite questions about the systems, it isn’t a surprise that cybercriminals want to get in on the LLM boom. The FBI has warned that cybercriminals are looking at using generative AI in their work, and European law enforcement agency Europol has issued a similar warning. The law enforcement agencies say LLMs could help cybercriminals with fraud, impersonation, and other social engineering faster than before and also improve their written English.

Whenever any new product, service, or event gains public attention—from the _Barbie_ movie to the Covid-19 pandemic—scammers rush to include it in their hacking artillery. So far, scammers have tricked people into downloading password-stealing malware through fake ads for ChatGPT, Bard, Midjourney, and other generative AI systems on Facebook.

Researchers at security firm Sophos have spotted the operators of pig butchering and romance scams accidentally including generated text in their messages—“As a language model of ‘me’ I don’t have feelings or emotions like humans do,” one message said. And hackers have also been stealing tokens to provide them with access to OpenAI’s API and access to the chatbot at scale.

In his WormGPT report, Kelly notes that cybercriminals are often sharing jailbreaks that allow people to bypass the safety restrictions put in place by the makers of popular LLMs. But even unconstrained versions of these models may, thankfully, not be that useful for cybercriminals in their current form.

Shykevich, the Check Point researcher, says that even when he has seen cybercriminals try to use public models, they haven’t been effective. They can “create ransomware strains, info stealers, but no better than even an average developer,” he says. However, those on the cybercrime forums are still talking about making their own clones, Shykevich says, and they’re only going to get better at using the systems. So be careful what you click.

Criminals Have Created Their Own ChatGPT Clones

Here’s one simple way to reduce your security risk while logging in.

This feature has been disappearing and reappearing from Google Messages and isn't available in every region. If you don't see the menu option, those could be the reasons. If you want another text messaging app that does the same job, SMS Organizer from Microsoft works: Tap the three dots (top right), then **Settings**, **Message rules**, and **Delete older OTP messages**.

On the iPhone, this feature is available only if you've upgraded to iOS 17 or later. At the time of writing, the software is in its public beta stage—you can choose to enroll in the public beta or wait for the final version of the software to reach everyone. Once you have iOS 17 installed, the auto-delete OTP feature appears in both Messages and Mail.

The same toggle switch controls the behavior of the feature in both apps: From the main iOS Settings screen, head to **Passwords**, then tap **Password Options** and enable **Clean Up Automatically**. Once your passcodes have been copied over to the relevant app and used, Messages or Mail will take care of deleting the texts or emails that they came in.

Good Passcode Security

Auto-deleting OTP codes is one of several precautions you can take.

SMS Organizer via David Nield

Automatically deleting passcodes can certainly help. But there are other precautions you can take to make sure the risk of your accounts getting exposed is as small as possible. That starts with keeping your phone lock screen locked down—the harder it is for someone else to get on your phone, the harder it is for them to get at your passcodes.

You also need to think about the possibility of someone intercepting your text messages or emails. On which other devices and in which other apps can these passcode messages be accessed? Those avenues need to be well blocked off, whether that involves physical access to a particular computer or a third-party app that's connected to one of your accounts.

With the right know-how and equipment, SMS messages can be intercepted, although it's harder to do now than it has been. To guard against this, make sure your network provider has your up-to-date contact information, and take advantage of any security precautions available to you—such as security questions to verify your identity over the phone.

You might want to consider switching to an authenticator app for two-step verification, if the accounts you're using allow it. Apps such as Twilio Authy (Android, iOS) and Google Authenticator (Android, iOS) can produce passcodes directly on your phone, with no text messages or emails required. That limits the opportunity for someone else to intercept those communications.

How to Automatically Delete Passcode Texts on Android and iOS

Plus: A framework for encrypting social media, Russia-backed hacking through Microsoft Teams, and the Bitfinex Crypto Couple pleads guilty.

Between a cascade of indictments against former US president Donald Trump, a tumultuous 2024 election season (in which Trump is a main character), and the rapid rise of generative artificial intelligence, 2024 is shaping up to be a complete nightmare.

At the center of it will be a rise in personalized disinformation. Not only will there be more BS to sift through thanks to tools like ChatGPT and Google’s Bard, but the disinformation will likely be more effective, and even tailored to target specific groups with frightening consequences. Of course, some of this could be fixed with new regulations. But the US Congress still hasn’t figured out how to tackle privacy, and regulating AI will only be more difficult.

In addition to disinformation, people keep figuring out new ways to break through the guardrails that generative AI tools have in place to stop malicious activities. The latest is something called an “adversarial attack,” which researchers at Carnegie Mellon University found can be carried out simply by attaching a string of nonsense-looking instructions to the end of certain prompts entered into tools like ChatGPT. While it’s possible to block specific attack strings, nobody yet knows how to fix this flaw entirely.

AI might be the new frontier for security researchers. But regular ol’ platforms are still a wealth of terrible vulnerabilities. The latest is the Points platform, which provides the underlying tech for dozens of major travel rewards programs. Researchers recently discovered flaws in the Points API that exposed people’s private information. And a bug in a Points administrator website could have allowed an attacker to give themselves unlimited airline miles and hotel points. But don’t get any big ideas, hackers—all the flaws have since been fixed.

The Points bugs aren’t the only ones patched recently. If you use Apple iOS, Google Android, or Microsoft products, check our list of the recent security updates you’ll want to install right now.

But that’s not all. Each week, we round up the security and privacy stories we didn’t cover in depth ourselves. Click the headlines to read the full stories. And stay safe out there.

A single cloud firm has provided server space to at least 17 state-sponsored hacking groups from countries including China, Russia, and North Korea, according to researchers at security firm Halcyon. The firm, Cloudzy, also provided its cloud storage to state-backed hackers from Iran, India, Pakistan, and Vietnam, as well as two ransomware groups, researchers found. While Halcyon estimates that “roughly half” of Cloudzy’s business “was malicious,” according to Reuters, the company pins it at just 2 percent. But who's counting, really?

Renowned hacker crew Cult of the Dead Cow (cDc) has big plans for social media. No, they’re not launching another Twitter alternative (mercifully)—they’ve created a framework for encrypting social media, _The Washington Post_ reports. The networked application framework, dubbed Veilid, would give companies the ability to release encrypted versions of their apps, allowing users greater privacy protections against prying eyes. Veilid (pronounced vay-lid) will formally debut next week at the Def Con security conference in Las Vegas, and cDc promises “flagship apps available from the launch.”

Microsoft revealed this week that state-backed hackers linked to Russia carried out “highly targeted” phishing attacks through the company’s Teams platform. The hackers used previously compromised Microsoft 365 accounts “owned by small businesses” to create domains that were then used to dupe their targets through Microsoft Teams messages, “by engaging a user and eliciting approval of multifactor authentication (MFA) prompts,” Microsoft wrote. The hackers are believed to be part of a group widely known as APT29 or Cozy Bear, which Microsoft calls Midnight Blizzard. Western authorities say APT29 is part of Russia’s Foreign Intelligence Service (SVR). You might remember the group from such hits as 2020’s historic SolarWinds hack and 2016’s breach of the Democratic National Committee.

A couple arrested in 2022 for allegedly stealing and laundering $4.5 billion in bitcoin from the Bitfinex exchange pleaded guilty on Thursday to a variety of charges stemming from the 2016 hack. Ilya Lichtenstein admitted to hacking Bitfinex and pleaded guilty to a conspiracy to launder the ill-gotten fortune. His wife, Heather Rhiannon Morgan, also entered guilty pleas on charges of conspiracy to launder money and conspiracy to defraud the United States. Lichtenstein’s admission ends the mystery of who hacked the cryptocurrency exchange, which suffered from several security issues, according to an internal report obtained by the Organized Crime and Corruption Reporting Project and reviewed by WIRED. If convicted, Lichtenstein faces up to 20 years in prison, while Morgan could spend 10 years behind bars.

Source

Wired