A new macOS information-stealing malware dubbed ClickLock terminates all visible processes to force users into entering their system login password.

The malware is designed to steal cryptocurrency assets, login credentials, password-manager data, browser information, and macOS authentication data, and it can also install a persistent backdoor for ongoing remote access to infected systems.

Researchers at Group-IB analyzed the ClickLock shell script after discovering the malware on VirusTotal, where it was first submitted on June 9. At the time of the report, it remained undetected by all security vendors available on the platform.

Further investigation revealed that the malicious script has infected at least 100 systems across 33 countries since May.

The compromise likely begins via a ClickFix lure, as the researchers observed pastes of a malicious command in the Terminal that trigger a fake Cloudflare “human verification” sequence with an animated progress bar.

At the same time, keyboard interrupts are disabled, the terminal cursor is hidden, and the stealer modules are downloaded in the background.

The macOS NotificationCenter is also suppressed for about six hours, effectively disabling notifications that could expose the attack.

Forcing password entry

Group-IB researchers highlight that ClickLock does not require any exploits or elevated privileges but achieves its goal through social engineering and forced interaction loops.

Operational success is obtained through the malware’s mechanism for coercing the victims into entering their macOS system password.

Group-IB says that the script initially displays a fake macOS password dialog using the victim’s real username and a downloaded Apple icon.

If the user enters their password, the malware validates the data and exfiltrates it to the attacker via Telegram.

In case the user cancels the dialog, the malware establishes persistence via two macOS LaunchAgents (com.authirity.plist, com.chromer.plist) and reloads at the next login.

At the next activation, the password-stealing module runs a termination loop every 210 milliseconds, targeting key apps (e.g., Finder, Dock, Terminal, Activity Monitor, Console, System Settings, Spotlight, web browsers) and shows only a password dialog on the screen until the victim complies.

Group-IB reports that the loop is configured to continue for 300,000 seconds (about 83 hours), or until the victim supplies a correct password.

The second LaunchAgent runs a separate coercion mechanism that also terminates many of the mentioned system applications, requesting Keychain authorization via a legitimate system prompt, seeking approval to access Chrome’s Safe Storage key.

That key could then be used to decrypt offline Chromium-stored passwords, cookies, and autofill information from stolen databases.

This second mechanism has a repeat interval of 200 milliseconds and is configured to last for nearly 35 days (3 million seconds).

ClickLock also deploys a data-harvesting module, which targets the following:

• Data from eight browsers: Chrome, Firefox, Brave, Edge, Opera, Vivaldi, Arc, and Chromium
• Saved logins, cookies, autofill data, bookmarks, local storage, and session storage
• Cryptocurrency wallet extensions and desktop wallet files
• Encrypted wallet vault material for potential offline cracking
• Password-manager extension data
• Cached cryptocurrency addresses across EVM, Bitcoin, Solana, TRON, TON, and Stacks
• Shell histories
• FileZilla FTP configuration and recent-server data
• Basic system information and the public IP address

The harvesting module packages the collected information and a summary log file into a ZIP archive, then uploads it via the Telegram Bot API.

Files larger than 40 MB are split into smaller parts, while retry logic ensures that uploading resumes after temporary network failures.

The final module is a modified version of the open-source tool GSocket that acts as a persistent backdoor for the attackers.

The backdoor establishes persistence through multiple methods, including a LaunchAgent, crontab entries, and modifications to shell configuration files.

It connects through a GSocket relay, allowing the attacker to open a reverse shell and remotely control the system.

Unlike the other ClickLock modules that self-delete after execution, GSocket is the only component that persists on infected systems.

Group-IB warns that “malware leaves a narrow detection window” and that the malicious payloads are hosted on compromised legitimate domains with a clean reputation.

Additionally, the script is not flagged as malicious on VirusTotal, and its modules self-delete after execution, leaving no artifacts.

Despite this, the researchers say that detection is possible based on the activity generated by the malware, such as osascript launching password dialogs, repeated process termination, mass access to browser profile directories, and outbound connections to Telegram’s API.

To defend against these attacks, users should avoid pasting in Terminal commands they don’t fully understand, especially if the request comes from a website.

“Any page that instructs you to open Terminal, regardless of how professional it looks, is attempting to compromise your system,” the researchers say.

If prompted to enter the login password when the rest of the system appears unresponsive, Group-IB recommends forcing a system shutdown by holding the power button and then booting into Safe Mode to recover the system.

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1Password has launched a browser integration that lets Anthropic’s Claude use stored credentials to complete tasks on the web without the passwords ever reaching the AI model, according to a blog post published on Thursday. The company calls it a zero-exposure architecture: when Claude needs to sign in, 1Password shows the user which credential is being requested and why, then waits for biometric approval before injecting the login directly into the page. Claude never sees the vault item, password, or one-time code, and access ends when the task is complete.

The integration addresses a fundamental tension in agentic AI. Browser-based agents like Claude can navigate websites, fill out forms, and complete purchases, but reaching a login page has historically forced users to either hand over their password or take the wheel themselves. 1Password says this is the first browser integration that lets an agent use credentials without granting direct access to them.

After autofill, 1Password checks whether secrets were exposed on the page. If submission fails, the extension clears the filled values before returning control to Claude. The credential stays encrypted and controlled by 1Password throughout the process.

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The launch also introduces Agentic Mode, a feature in the 1Password browser extension that automatically locks down the vault when a compatible AI agent takes control. The agent can only use logins and one-time codes explicitly approved for the current task, and the rest of the vault stays out of reach. Agentic Mode activates even if the 1Password-Claude integration is not configured, and supports agents beyond Claude.

The timing is notable given that security researchers recently demonstrated how AI browsers could be tricked into leaking user credentials through prompt injection attacks, with Anthropic’s own Claude extension among those affected. 1Password CTO Nancy Wang said in the company’s announcement that the answer is not handing agents your secrets, but letting a user give an agent permission to use a credential without letting the agent see it. She called that distinction the foundation of trust in AI agents.

1Password for Claude is available now on Mac for business, family, and individual plans, and requires the 1Password desktop app, browser extension, Claude desktop app, and Claude browser extension. The company, which recently acquired Israeli startup Apono to govern AI agent access inside enterprise systems, said it plans to add support for payment cards and identity details after launch.

CNET’s password manager expert Joe Supan said he would normally be very wary about giving an AI agent access to his password manager, but that 1Password appears to have several good guardrails in place, particularly biometric authentication for each login. The integration marks the first time a major password manager has built a dedicated secure channel for an AI agent to use credentials at runtime, rather than exposing them to the model’s context. Whether the approach holds up against the kind of prompt injection attacks that have already compromised AI browsers remains to be seen.

The brand iRobot launched the first Roomba robot vacuum back in 2002, and popularity for the handy devices skyrocketed from there. Countless competitors have emerged, but Roomba is still going strong. Its latest models have all the new features we love, from doubling as a vacuum and a mop to fantastic navigation and suction. The Roomba Max 705 is currently keeping my house clean as I test it for our robot vacuum guide, and it’s doing a great job both mopping and vacuuming the floors in my massive second story.

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U.S. beverage maker Coca-Cola said one of its dairy subsidiaries was hacked and that it’s shutting down its operations for the foreseeable future. The multinational giant said in a disclosure with the U.S. Securities and Exchange Commission that its Fairlife dairy company was hit by ransomware and that its production systems are affected. The company said that its Fairlife production operations across the United States are “temporarily suspended.”

Fairlife’s operations in Canada are unaffected.

Coca-Cola is one of the largest companies in the world, with products spanning carbonated drinks, water, and dairy products. Its Fairlife dairy is one of the company’s major brands, with an estimated $4 billion in sales by 2024.

Ransomware attacks on food and beverage companies can have lasting effects. Past incidents at Arizona Beverages in 2019 and food distributor giant UNFI last year resulted in weeks-long disruptions to their respective production lines and empty grocery shelves.

Coca-Cola didn’t say when Fairlife’s systems would be restored.

Do you know about the cyberattack at Fairlife? Do you work at the company? We would love to hear from you. From a non-work device, you can securely contact Zack Whittaker on the Signal messaging app with the username zackwhittaker.1337.

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Presented by Ping Identity

Enterprises need to treat zero trust security architecture as an immediate requirement for AI agents rather than a long-term goal, says Andre Durand, CEO and founder of Ping Identity. Zero trust, the security model built on the assumption that no user, device, or system should be automatically trusted, requires continuous verification before every action rather than a single check at login. Agentic AI has profoundly compressed the risk timeline enterprises must manage, demanding that permission decisions be evaluated in real time.

That compression shows up in how permissions accumulate. Every time an employee approves an AI agent’s request for access to a company drive, a database, or a code repository, the enterprise hands over a sliver of control that looks routine in isolation. Across thousands of agents making thousands of requests, those approvals accumulate into an exposure that most existing security architectures were never built to measure.

“The rise in desire to use agents right now, and the speed of agentic, is highlighting the need to move faster on the principles of zero trust,” Durand says. “Agents just move faster, full stop. A human compromise might be measured in minutes or hours, sometimes days. At agentic speed, a thousand actions could happen in five minutes.”

Why zero trust is now urgent for agentic AI

That difference in velocity changes how enterprises need to think about permissions. Two variables matter: the surface area of access an agent is granted and the duration that access remains valid. Traditional identity and access management tends to grant broad permissions and leave sessions open for extended periods because the human using them moves at human speed. Zero trust, in contrast, collapses both variables at once by narrowing access down to what is strictly necessary and revalidating it continuously, rather than once at login.

“Zero trust really just says, just enough, just in time,” Durand says. “It’s your next action that we care about. We’re moving identity from an era where access was our runtime control point — meaning were you logged in, did you have a session — toward the decision that sits behind that login.”

Why agents must be treated as first-class identities

That shift to decision-based control has direct implications for how agents should be provisioned in the first place. The common practice of letting an agent operate under a cloned human login or a shared service account doesn’t work, Durand says.

“Each agent should have its own identity,” he explains. “It should not be impersonating the human. It can act on behalf of the human, we could explicitly delegate authority to an agent, but we don’t want to blur the lines between the human taking action and the agent taking action.”

And beyond that is another concern: the shared secrets, API keys in particular, that many service accounts still rely on. For example, the habit of embedding keys directly in source code, where they can be committed accidentally and exposed, is a convenient but weak security pattern that agentic workflows make considerably riskier. Building service account architectures that let agents authenticate without relying on those shared credentials or other long-lived standing access is now an urgent priority rather than a long-term cleanup project.

Where enterprises can enforce zero trust policies

Enforcing any of this in practice requires identifying where policy can actually be applied. Several existing choke points, including API gateways and the agent gateway sitting in front of MCP servers, offer practical locations where enterprises can inspect what an agent is requesting and apply policy rules before granting it.

“Those policies could leverage real-time risk and fraud signals, and then enforce, deterministically, what the agent can do when it interacts with these systems,” Durand explains.

The goal is to move authorization from something decided once at login to something evaluated at the moment of every consequential action, such as an agent attempting to commit code to a repository. Instead of carrying a standing permission to write to GitHub, the agent’s request would be checked against context and policy at that specific moment, closing the window of trust down to the scope of a single action.

Stopping AI agents from rewriting their own permissions

That model becomes especially important given how agents can behave once they are already inside a system — for example, coding agents that have acknowledged, when questioned, either ignoring a specific guardrail entirely, or attempting to rewrite the permissions they were given.

“Who’s watching the watcher? Zero trust needs to apply here,” Durand says. “If generative AI systems follow your instruction 97% of the time, and you’re simply asking it for advice, that might be fine. If it’s responsible for making a decision about who gets let in, 97% is not good enough.”

How to trust AI-generated output at agent speed

The answer to that gap is not to eliminate AI from the review process, but to structure reviews so no single agent’s judgment is taken at face value. Because human review cannot scale to the volume and speed of agentic output without erasing the advantage of using agents at all, a new framework is necessary, so that when one agent produces work, such as code, separate agents evaluate it, provided those reviewing agents are kept from communicating with one another or with the one they are checking. It’s a new human-AI paradigm, Durand says.

“We probably will have to develop frameworks that we trust without seeing or verifying the output directly,” he explains. “It’s not that that construct is 100% foolproof. However, it’s the best we can do to move at agent speed. We can’t trust the exact output, but we can trust the framework.”

In practice, that means combining automated review with clear human accountability for higher-risk decisions, rather than treating agent output as self-validating.

For traditional auditors, reviewing every transaction individually is never feasible, and statistically valid sampling stands in for full verification. The same applies to risk accumulation: a single agent action might carry little risk on its own, while a sequence of actions moving in a consistent direction could cross a threshold that triggers an intervention, including a kill switch capable of halting the agent before further harm occurs.

What to ask when evaluating agentic identity platforms

For security leaders evaluating identity platforms for agentic AI, there’s no narrow checklist. Enterprises should evaluate what their full lifecycle of agent management looks like. Most enterprises are managing agents on two fronts simultaneously: customer-facing agents acting on behalf of external users, and internal agents deployed to automate enterprise processes.

“Pause long enough to see the totality of what it would mean to secure multiple agents, both interacting with you from the outside as well as being deployed on the inside,” Durand says. “We need discovery and visibility of all the agents operating within our estate, a place to register them, a standard way to assign custodians, and a way to construct and centralize policy so security can enforce it across the organization.”

And while basic security principles were already fully understood before agentic AI arrived, what has changed, Durand says, is that the cost of moving slowly has finally caught up with the cost of moving carelessly, giving enterprises a narrowing window to build the right architecture before widespread agentic adoption makes retrofitting far more expensive.

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

Keytec brought the Magic Touch to the 1994 Summer Consumer Electronics Show in Chicago. The Texas company, founded in 1987, offered a straightforward way to give standard CRT monitors and notebook screens touch input without replacing the entire display. At a moment when keyboards and mice defined personal computing, the idea of pressing a finger directly on the glass stood out as genuinely forward-looking.

The Magic Touch’s hardware took the shape of a framed overlay, similar to a plastic frame with a clear adhesive membrane inside. This membrane rested on top of whatever monitor you were using at the moment, with a nice border that matched the forum’s standard beige or black color scheme. Within this jumble of layers was a brilliant innovation: an invisible spacer system that kept the whole device light and didn’t interfere with your view while also protecting the screen from scratches. The membrane handled roughly 80% of the display, which isn’t awful, and it was strong enough to withstand the ordinary 3H pencil scratch.

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To make things work, you needed a little external controller. Early versions were connected via an obsolete serial port, whereas subsequent versions just used USB. The panel simply hooked into the controller, and many configurations provided power via the computer connection, eliminating the need for a bulky power brick on your desk. The items were offered in a number of sizes, ranging from 12 to 17 inches for poor laptops to 13 to 24 inches for desktop displays, with larger cousins appearing in related products. It was very simple to install; simply attach some clips or brackets for large desktop monitors that would clip over the top of the bezel, or use adjustable straps for laptops to keep everything in line with the screen. You finished in a few minutes, and the old screen was as good as new underneath.

Once everything was set up and connected, you’d need some software to instruct the touchpad what to do. This would convert all of the touchy feely inputs into mouse operations. You’d go through a simple calibration process to get the pixels and the membrane in line. Then you could choose whether to click on contact or lift off to get the device to work, and you could even change for left or right hand preference, as well as temporary right click on using a software toggle. It performed all of the standard mouse actions: cursor movement, single clicks, double taps, and drags. The touch resolution was great and high, 4096 by 4096 points, and the entire thing responded to finger pressure, which ranged from 50 to 120 grams per square centimeter.

The effectiveness of the system was determined by the software being used. Big buttons on interface elements functioned perfectly with a finger or a stylus, and menu navigation was simple. You’re probably familiar with some of the older action games, which were more hit and miss. Because of the small physical space between the membrane and the screen, you had to make sure you had everything set up correctly for those tiny targets; otherwise, you’d have bizarre parallax, and glare may be an issue, especially if you had a shiny display. It also took some force to work, albeit not much.

After completing the initial Magic Touch launch, the company continued to develop subsequent versions that worked with a variety of operating systems and connection methods. They made this type of device until 2017, when it was handed over to a new business that continued to create custom touch-focused solutions for a variety of monitors and panels. Nowadays, you can get very much the same idea, just updated, and still purchase a Magic Touch-style gadget to convert an old display into a touchscreen.

AI AND ML

Adapting existing local LLM project for security and sovereignty purposes and hopes to one day match Mythos

South Korea is developing its own security-focused AI model and hopes to bring it online by the end of the year, to ensure the nation has sovereign bug-finding capabilities.

Deputy Prime Minister and Minister of Science and ICT Bae Kyung-hoon revealed the effort to create the model yesterday, and said it’s needed so South Korea possesses a bug-finding model to rival Anthropic’s Mythos.

The US government has twice blocked access to Mythos, once by requiring Anthropic to offer it only to American citizens – a demand the AI company could not meet and therefore blocked all access – and a second time by ordering the company to take down its services so Washington could investigate allegations of possible dangerous performance problems.

Those incidents led many other nations conclude that the US could in future deny access to powerful models – meaning US-based organizations and national security agencies would have an edge. Washington has since allowed limited access to Mythos to some of its allies.

Interest in developing sovereign AI capacity has nonetheless soared, and Bae said South Korea now aspires to develop its own Mythos-class model. The Register is aware of another effort to create Mythos-like tools, involving private firms and infrastructure operators across several countries.

In South Korea, the government’s approach is to add security-related information to the corpus it is using to train a locally developed frontier model. The minister said he expects that security-capable model will debut by the end of 2026.

South Korea has also sought bids to create a chatbot that will be made freely available to all residents, plus an agentic application that will help locals interact with government services.

Minister Bae made his remarks at a policy briefing session conducted by President Lee Jae Myung, during which discussions about AI also touched on using the technology to detect fake news in real time, and put it to work handling complaints about government services more quickly than is currently possible. ®