Crypto wrench attacks reached 52 in H1 2026, with recorded financial exposure rising 1,079% to $124.1 million, according to a CertiK report.

Threat actors are using a new technique called “phantom squatting” to trick AI tools into directing users to phishing sites, according to researchers at Palo Alto Networks’ Unit 42.

Since AI models frequently hallucinate phony information, they sometimes point users to websites that don’t exist. Threat actors are now registering these AI-hallucinated domains and using them to host phishing sites.

Researchers at Cisco Talos are tracking a sophisticated phishing-as-a-service operator panel called “ARToken” that’s built on the EvilTokens phishing platform. ARToken focuses on targeted social engineering attacks, allowing operators to customize phishing attempts for each victim.

Welcome back, aspiring cyberwarriors!

Part of our work involves supporting red team engagements. We review completed tests, size up the risk tied to each vulnerability and build out recommendations for shoring up the infrastructure. This time around, we wanted to pull back the curtain on something special. It’s ATM security. 

This article is written to help with security assessments on ATMs, showing possible vulnerabilities you may find. It covers many things, from running malware bought off a forum, to an insider on the bank’s payroll, to a service technician who understands the machine’s internals and has been handed broad access to the equipment. We also look at whether a hacker could get into the bank’s broader network simply because the perimeter wasn’t locked down well enough.

Nothing here is meant as a tutorial. We’re documenting weaknesses hackers could exploit so that defenders know what to fix, not handing anyone a blueprint. We take no responsibility for how this information is used.

With that out of the way, let’s start with where ATMs came from.

The History of ATMs

London got the world’s first working ATM on June 27, 1967. It was primitive by today’s standards, incapable of checking a balance, which is exactly why withdrawals topped out at 10 pounds, and it dispensed cash only against special vouchers rather than reading a card. 

Nearly six decades later, ATMs look nothing like those early cash dispensers. Now they are multifunctional devices, but the hackers never stopped circling. Part of the appeal is obvious. An ATM sits on a pile of cash and offers quick access to it, and there are simply too many machines scattered across too many places to guard them all closely. A lot of them sit in isolated, low traffic spots that run unattended around the clock, think gas stations. That has shaped decades of security investment, most of it aimed at physical hardening. Today’s units can weigh over half a ton and come loaded with sensors tracking position, internal temperature, and whether a compartment has been pried open.

Here’s the catch, though. The safe holding the cash is genuinely hard to crack, but the compartment housing the control electronics is a different story, and in our assessment, it remains poorly defended. That gap opens the door to logical attacks, ones that skip the crowbar entirely and go after the software instead, and that category has been gaining ground fast.

Cisco Talos has tracked a steady climb in new ATM malware variants since 2009. The raw sample count still looks small next to other malware families, but don’t let that fool you. Europe alone saw logical attacks on ATMs jump 269% in 2020 versus the year prior, and the average payout per incident ballooned nearly a thousandfold across that same window, climbing from roughly a thousand euros to well over a million.

What changed the game was availability. ATM malware used to be a rare, closely guarded tool. Once it started circulating more freely on underground markets, prices fell and so did the skill required to use it. Cutlet Maker, which surfaced in 2017, is a good illustration. It came bundled with a Russian language manual complete with troubleshooting notes for running it against different ATM models.

Fast forward to 2024, and vendors on those same markets were offering ATM malware through subscription pricing, monthly plans included.

Logical attacks have always had one real weakness. They take skill and patience to pull off. That’s why cheap, well documented malware kits have had such an outsized impact on the trend. Their upside for hackers is just as real. They’re far quieter than smashing a machine open, and they often let the same person come back to a compromised ATM again and again. Manufacturers have started fighting back on the hardware side too, with tamper protected cassettes that flood the cash inside with indelible ink the moment someone tries to force them open, ruining the bills instantly.

Brief Attack Statistics

The numbers tell their own story. ATM related crime climbed 600% between 2019 and 2022, with 165% of that increase packed into 2021 and 2022 alone. Physical break ins, which have always driven the bulk of ATM crime, contributed alongside the rise in logical attacks. Germany had 496 ATM explosions recorded in 2022, a record for the country. Zoom out globally, and incidents of that kind blew past 18,000 in 2023.

Losses have kept pace. Banks worldwide absorbed $2.4 billion in direct losses from ATM fraud by the close of 2023. Europe’s share came to 173 million euros, with 67 million of that tied specifically to skimming. The United States handles just 25.29% of global transaction volume yet accounts for 42.32% of global losses. Skimming remains a big part of why, showing up in 45% of all ATM fraud cases in 2023 and costing North America over $900 million, with more than 315,000 cards compromised across at least 3,000 financial institutions.

None of this is happening in a vacuum. The market for ATM protection has grown right alongside the threat. Still, priorities inside most banks remain lopsided. Physical security tends to get the lion’s share of attention, while the operating system, drivers, and control software logic running underneath often get treated as an afterthought. That imbalance carries real consequences. A 2022 RTM Group study found that hackers could breach an ATM’s housing without setting off an alarm in one out of every two attempts, giving them free rein to tamper with the equipment inside.

How an ATM Is Built

Making sense of how these attacks work starts with understanding what happens inside the machine during an ordinary transaction. We’ll walk through that process using one representative configuration, illustrated in the diagram below.

The diagram reflects one specific setup we’re using for illustration, not a universal default, since real world configurations vary by device.

1. User Layer

From where the customer stands, using an ATM is simple. They need to present a card and pick a transaction. That wasn’t always the whole story. Inserting a physical card into a reader used to be the only entry point, and that reliance on the magnetic stripe made skimming and shimming, techniques aimed at stealing card data to produce counterfeit copies, a persistent problem for years.

Contactless cards changed the entry point itself. NFC readers now sit alongside traditional card slots on most machines. 

A PIN code layers on additional protection against someone using a stolen card. Entry happens through an encrypting PIN pad, a combination of physical keypad and cryptographic module that ensures the PIN never travels or gets stored anywhere in plain text. Verification of the resulting encrypted PIN block happens back at the processing center. 

Once identity checks clear, you can withdraw cash, check your balance, transfer funds, and so forth. There’s a full computer running inside the housing, but customers never get anywhere near it directly. Every interaction they have flows through a single banking application running in kiosk mode, locked to full screen.

2. OS Layer

That computer we just mentioned lives inside what’s called the service zone, and this section covers what happens there, setting the cash handling hardware aside for the moment. Physically, the service zone is protected by a thin door and a basic lock. Machines from the same product line frequently share an identical key too, one that’s often available for purchase online with minimal effort.

Beyond the system unit itself, the service zone also houses the ATM’s networking equipment and its wired connections to the card reader, contactless reader, PIN pad, and dispenser, typically running over USB, Ethernet, PCI, or COM interfaces depending on the device.

Windows powers most of these systems, historically through Windows Embedded and increasingly through Windows IoT, a Windows 10 variant built for embedded use.

The kiosk application isn’t the only thing running on that OS. Alongside it sits the ATM’s control software plus a handful of security tools. That can be antivirus protection, Windows AppLocker that keeps unauthorized programs from executing, and a VPN client that maintains a secure tunnel back to the bank’s internal network.

Control software is arguably the most important piece at this layer. Core responsibilities for the control software boil down to managing peripherals and communicating with the processing center, though specific implementations often add more on top of that. Some bundle in software for a monitoring server, letting technicians manage an entire network of self service machines remotely. Others are built in a supervisor mode meant purely for technical staff, offering quick access to diagnostic tools through a hidden menu to simplify physical maintenance visits.

3. Network Layer

Selecting a transaction sets off a verification process handled entirely by the processing center, a server living on the bank’s internal network. That server confirms the card data is legitimate, checks the PIN again before letting the transaction through, rules out any restrictions on the account, and verifies there’s enough balance to cover the request.

Everything exchanged between the ATM and the processing center travels encrypted, usually through a VPN tunnel, protecting against interception or tampering along the way. NDC and DDC are the most common messaging protocols in this exchange, functioning as something of an informal industry standard even before multi-vendor control software became widespread. ISO 8583 and its various offshoots see heavy use as well. 

The processing center isn’t the only thing an ATM talks to. Many machines also maintain a connection to a monitoring server used for remote management, health checks, and pushing updates, and unlike the processing center link, this channel frequently runs without any encryption at all.

4. Firmware Layer

Once the processing center signs off, the control software hands things over to the dispenser for a withdrawal, or the deposit module if cash is going in. These components typically sit inside the most fortified section of the ATM, the safe zone, built from tougher materials and secured with its own dedicated key separate from the service zone. 

The dispenser counts out the required banknotes from the ATM’s cassettes, moves them into position at the dispensing tray, then opens the shutter, the physical flap that blocks access to the cash until it’s ready. Data moving between the control software and the dispenser can be encrypted, and both sides authenticate one another before any exchange begins, a safeguard against device spoofing. All of that encryption and authentication logic lives directly in the dispenser’s own firmware. 

Deposits work differently. Incoming banknotes pass through a validator that checks their authenticity.

ATM Attacks

With the mechanics of an ATM covered, we can turn to the threats themselves. Every attack against these machines falls into one of two broad camps, physical or logical, depending on what the hacker is going after and how they approach it.

Physical attacks go straight after the machine or its components, aiming to extract cash or knock the device out of normal operation without touching a line of code. These predate targeted malware by decades and don’t require much specialized skill. Some don’t even target the machine itself, focusing instead on the people standing in front of it.

Logical attacks operate on a different level entirely. They demand genuine technical skill and preparation, built around exploiting weaknesses in the ATM’s software and network layers. They draw less public attention than physical attacks despite posing a bigger threat to banks, largely because they’re quieter and let a hacker return to the same compromised machine to cash in more than once.

System attacks go after functionality or logic running at the ATM’s OS layer, typically aiming to extract cash or sidestep security controls outright. Black box attacks deserve special attention, where a hacker skips gaining OS access altogether and instead wires their own device directly into the dispenser to control it externally. The same technique can target other peripherals, like the banknote validator.

Network attacks aim at the ATM’s networking components instead, with hackers looking to intercept, forge, or otherwise abuse data in transit, or to seize remote control of the machine. With weak enough safeguards in place, a hacker can forge the responses coming back to the ATM and push through a cash withdrawal even after the processing center rejected it.

Not every attack in this framework ends with cash in hand. A hacker might, say, work to gain remote network access first, then pivot into an OS layer attack from there. 

We have seen cases where compromising a single ATM meant compromising the entire bank because there was no network segmentation in place. Conversely, gaining access to the bank’s internal network could provide a path to ATMs and other critical systems connected to it. Credential reuse and a lack of understanding of Active Directory security can lead to devastating consequences in environments like these.

Summary

ATMs have evolved from simple cash dispensers into complex and networked systems. Their security has evolved unevenly alongside them. Physical hardening has made the cash safe itself genuinely difficult to crack, but the service zone housing the control electronics remains comparatively exposed, and that gap has fueled a steady rise in logical attacks. These attacks demand more skill than a physical break-in, but they’re increasingly accessible because of well-documented malware kits.

Cybersecurity is a vast field, and we offer courses covering a wide range of topics, including Active Directory Hacking, Wi-Fi Hacking, Web Application Hacking, SCADA Security, and much more. Our course library is constantly growing as we continue to add new training, all of which is available through our Member Gold plan. If you want unlimited access to our entire training library, including our most advanced courses, consider upgrading to Subscriber Pro.

The post Pentesting: A Look at ATM Security first appeared on Hackers Arise.

Cybercrime used to have a ‘"tell." It was the digital equivalent of a villain stroking their cat - clunky grammar, misspelt links and suspicious attachments that screamed ‘phishing’.

Fake FBI agents are using deepfake videos, spoofed IC3 websites and false recovery claims to steal money and personal information from people who were scammed before, the FBI warns.

An AI-led cyberattack breached limited Hugging Face datasets and service credentials, while public models, Spaces and published packages showed no signs of tampering.

Apparently, being a leading destination for AI development doesn't mean AI will bail you out. AI agents broke into Hugging Face's production infrastructure, but commercial LLM guardrails blocked the forensic investigation, forcing it to turn to a Chinese open-weight model instead. The intrusion, “driven, end to end, by an autonomous AI agent system,” compromised a “limited set” of Hugging Face’s internal datasets and “several” credentials used by its services, according to a Thursday security incident disclosure. While the ML platform says that it’s still investigating whether any partner or customer data was exposed in the breach, there’s “no evidence of tampering with public, user-facing models, datasets, or Spaces, and our software supply chain (container images and published packages) was verified clean.” It also doesn't know which model the attackers used to power a swarm of AI agents, which, we're told, executed many thousands of individual actions across short-lived sandboxes, using self-migrating command-and-control staged on public services. “This matches the ‘agentic attacker’ scenario the industry has been forecasting,” according to the Hugging Face blog. Additionally, after unsuccessfully using unnamed frontier models to start the forensic analysis, the Hugging Face security team ultimately ran the log analysis on GLM 5.2, an open-weight model developed by Chinese AI firm Z.ai, on the platform’s own infrastructure. The advanced commercial models didn’t work because their analysis required submitting real attack commands, exploit payloads, and command-and-control artifacts - all of the things that the LLMs’ guardrails have been trained to block so that the AI systems can’t be used in real-life attacks. “The attacker was bound by no usage policy, while our own forensic work was blocked by the guardrails of the hosted models we first tried,” the security team wrote, noting that it’s not arguing against safety measures on hosted models and has shared this information with the LLM providers. Using GLM 5.2 had another benefit, Hugging Face noted: “No attacker data, and none of the credentials it referenced, left our environment.” This also serves as an important reminder to defenders, according to the AI platform. “Have a capable model you can run on your own infrastructure vetted and ready before an incident, both to avoid guardrail lockout and to keep attacker data and credentials from leaving your environment.” The Hugging Face intrusion is yet another indication that attacks carried out by autonomous AI agents are no longer a future threat, but rather the current state of AI-based intrusions. Last week, The Register spoke with TrendAI VP of AI and security threat research Tom Kellermann about another recent attack, during which a jailbroken Google Gemini did 90 percent of the work - including spinning up a new C2 server in just six minutes. The human did just 10 percent. Additionally, earlier in July, Sysdig threat hunters documented what they say is the first-ever documented agentic ransomware infection with an LLM - not a human - driving the entire extortion operation, from gaining initial access to compromising a production database server and destroying data. “Think of a burglar that never gets tired, never needs sleep, and instead of jiggling one door handle at a time, is trying a thousand of them simultaneously,” Zero Networks field CTO Chris Boehm said in an email to The Register about the Hugging Face intrusion. “That's basically what happened here. Not one guy typing commands into a terminal, a swarm of little automated processes hammering away nonstop, hopping between hiding spots to make it harder to trace,” Boehm said. He added, the “part that actually unsettles” him most is that the platform’s security team couldn’t get commercial AI tools to help analyze the attack, “because those tools were built to refuse anything that looked like a real attack command. It didn't matter that it was the good guys asking.” Boehm said the takeaway for security teams is twofold: “These agents can now move faster and more relentlessly than any human ever could, and the safety tools we're building aren't always ready to help us respond at that speed.”®

Hackers have disabled Kenyan President William Ruto’s official website, replaced its homepage and demanded a ransom of 5 Bitcoin. According to a report, the Kenyan government opened an investigation after attackers took control of president.go.ke on July 18 and posted…

More than 23 million people who signed up to earn money from online gigs have allegedly had their personal and financial information spilled onto the internet following a breach of microtask platform Paidwork. The incident was added to Troy Hunt's Have I Been Pwned site after a database allegedly stolen from Paidwork was publicly released earlier this month. According to the breach notification service, the leak contains data on 23,272,765 users and traces back to an intrusion in March. The database first surfaced in April when someone using the handle "HACKFORMETOME" advertised what they claimed was an 11 GB dump from Paidwork's production systems on a popular cybercrime forum. At the time, the seller claimed the database contained records on more than 22 million users and attempted to auction it through Telegram and Tox. The alleged breach at Paidwork appeared in Have I Been Pwned on July 19. According to the breach listing, the exposed information goes well beyond names and email addresses. The data reportedly includes bank account numbers, phone numbers, physical addresses, dates of birth, profile photographs, IP addresses, device information, financial transaction records, payout histories, education levels, and passwords stored as bcrypt hashes. While bcrypt makes password cracking significantly harder than older hashing algorithms, weak passwords may still be recovered. Paidwork had not publicly acknowledged the alleged breach at the time of writing. The Register asked the company to confirm the authenticity of the leaked data and detail what steps it has taken to notify affected users, but the company didn't immediately respond. Paidwork markets itself as a way to earn money through small online tasks such as playing mobile games, watching advertisements, completing surveys, testing apps, shopping through cashback offers, and referring other users. Most individual jobs pay only a few cents, with workers required to earn at least $10 before cashing out. For Paidwork users, that modest payday may now come with a much larger bill. Anyone who reused their password elsewhere should change it immediately, keep an eye on financial accounts, and be alert for phishing emails built from the trove of personal information now circulating online. ®

FBI agents arrested a Florida man accused of spreading Steam game malware that stole $220,000 in crypto, including $32,000 from a terminally ill cancer patient.

Communities are forming around taking down the unpopular license plate scanning cameras, by legal means and vigilante action.

The FBI arrested a Florida man accused of uploading fake Steam games containing malware that stole passwords, data, and cryptocurrency wallet credentials from victims. Prosecutors say the scheme infected about 8,000 people, compromised roughly 80 crypto wallets, and stole at least $220,000 through games that appeared legitimate but secretly carried malware. TechCrunch reports: On Tuesday, the FBI arrested Zyaire Wilkins, a 21-year-old Florida resident and student. On Wednesday, prosecutors accused him and a number of unnamed co-conspirators of hacking crimes. Over the past two years, Wilkins and his partners allegedly published several malware-laden video games on Steam, including BlockBlasters, Dashverse, Lampy, Lunara, and PirateFi. Using that malware, says the FBI, Wilkins and his accomplices infected around 8,000 victims, and then hacked around 80 cryptocurrency wallets to steal at least $220,000 worth of crypto. Wilkins and the others marketed their malicious video games on Discord, LinkedIn, and Telegram, according to the authorities. [...] After the FBI identified another person involved in the crimes, according to the complaint, federal agents interviewed them. The unnamed person said they worked with other people to raise money to launch and market the malicious games in return for sharing some of the stolen cryptocurrency. The FBI identified a specific crypto account involved in the scheme, and then traced cryptocurrency payments made with that account to buy several gift cards, including for UberEats. After subpoenaing Uber, the feds were able to see that the gift cards were linked to an account that made deliveries to Wilkins, who went by the nickname Sibel.eth online, according to the complaint. The feds then got a search warrant for Wilkins' residence, where they seized his MacBook laptop, cellphones, other devices, and digital wallets. According to the complaint, he refused to speak or answer any questions.

Read more of this story at Slashdot.

Prosecutors accused 21-year-old student Zyaire Wilkins of publishing on Steam several fake video games that contained malware, infecting thousands of victims, and stealing crypto from some of them.

Ransomware has soured production at Coca-Cola-owned Fairlife, forcing the dairy business to temporarily halt production at its US plants. In an SEC filing on Thursday, Coca-Cola said Fairlife detected "unauthorized access by a third party to a portion of its systems, including its production-related systems," in what it described as a ransomware event. The company said it immediately activated its incident response and business continuity plans, brought in outside cybersecurity experts, and notified law enforcement. Fairlife has halted production at its US plants while Coca-Cola investigates the ransomware attack, but its Canadian facilities are still running. The business, which was fully acquired by Coca-Cola in 2020 and makes ultra-filtered milk and Core Power protein shakes, said the quality and safety of the products themselves have not been affected. The drinks giant said it is still investigating the incident and working to restore affected systems, adding that it has not yet determined whether the attack is reasonably likely to materially affect the company. The filing sheds little light on the technical details, other than confirming that production-related systems were affected. It remains unclear whether the ransomware reached operational technology used to run Fairlife's manufacturing facilities or whether production was suspended because supporting IT systems were taken offline as part of the company's response. Coca-Cola also hasn't said who was behind the attack or whether any data was stolen. No ransomware gang has publicly claimed responsibility at the time of writing, although such claims often surface days after an attack if negotiations fail or the attackers decide to increase the pressure. The Register asked Coca-Cola how many Fairlife facilities were affected, whether customer or employee data was compromised, whether operational technology was directly impacted, and when it expects US production to resume, but didn't receive an immediate response. The criminals may not have milked the company for a ransom yet, but they've already curdled production. ®

The company disconnected its systems on July 13 and is starting to gradually restore operations.

The post Cyberattack Disrupts Operations of Japanese Frozen Food Giant Nichirei appeared first on SecurityWeek.

Threat intel outfit Group-IB has detailed a previously undocumented macOS information stealer that doesn't bother hunting for software bugs. Instead, it persuades users to pwn themselves by pasting a command into Terminal, after which it helps itself to passwords, crypto wallets, browser data, and anything else worth stealing. The boffins have dubbed the malware “ClickLock Stealer,” a nod to its use of the increasingly popular ClickFix social engineering technique and a coercive "locker" feature that pressures victims into handing over their Mac login password. According to the researchers, the operation has been active since around May and has already targeted at least 100 victims across 33 countries, with more than half located in Europe. Group-IB said it discovered the malware after analyzing a malicious shell script uploaded to VirusTotal on June 9 that had zero antivirus detections at the time. The attackers appear to distribute the malware via fake verification pages using ClickFix, host payloads on compromised WordPress sites, and rely on Telegram infrastructure for command-and-control. "The current malware doesn't even need any elevated privileges or rely on exploits for the successful execution," the researchers wrote. Instead, victims are tricked into launching the infection themselves. After they paste the supplied command into Terminal, the malware displays what appears to be a Cloudflare verification sequence, complete with a fake progress animation, while quietly downloading additional components in the background. Group-IB says ClickLock targets data from eight browsers, 31 cryptocurrency wallet browser extensions, seven password manager extensions, eight desktop wallet applications, macOS Keychain, shell history, FTP credentials, and blockchain addresses spanning six different chains. The malware also deploys a modified version of the open source GSocket tool to provide the attackers with remote access. The researchers believe the malware is still under active development based on its code structure and other artifacts, suggesting operators are continuing to expand its capabilities. The nastiest touch comes when victims refuse to play along. During the fake verification process, ClickLock prompts for the user's macOS password. If the password isn't entered, the malware repeatedly kills visible applications, effectively preventing normal use of the machine until the victim complies. If the password is supplied, the theft completes quietly. If the machine is rebooted instead, persistence mechanisms are designed to resume the attack. "The entire attack chain from initial access to full credential theft and data exfiltration relies on a single moment of trust: the user pasting a command into Terminal," Group-IB wrote. The researchers say defenders will need to watch for suspicious behavior rather than known malware signatures. Among the warning signs are unexpected password prompts, applications being repeatedly forced to close, unusual access to browser data and stored credentials, and connections sending stolen information to Telegram. For everyone else, the advice is considerably simpler. If a website claiming to be Cloudflare, Google, or anyone else asks you to open Terminal and paste in a command, close the tab. ®

Nearly two years after a cyberattack disrupted Transport for London’s (TfL) online services and exposed customer data, two…

Owen Flowers and Thalha Jubair, two members of the prolific Scattered Spider hacking group, pleaded guilty and were sentenced to five years and six months in jail for hacking London’s metropolitan transit system.