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zero-day vulnerability
AI-generated exploit Artificial Intelligence Cybersecurity Google Threat Intelligence Group Technology zero-day vulnerability

Google Detects AI-Generated Zero-Day Exploit Targeting Web Admin Tool

 

Researchers from Google Threat Intelligence Group (GTIG) have revealed that a recently identified zero-day exploit aimed at a widely used open-source web administration platform was likely created with the help of artificial intelligence.

The vulnerability, which targeted the platform’s two-factor authentication (2FA) mechanism, could have allowed attackers to bypass critical security protections. While the software involved has not been publicly identified, researchers confirmed that the attack was stopped before it reached large-scale exploitation.

According to GTIG, analysis of the Python-based exploit strongly indicates the involvement of AI tools during the vulnerability discovery and weaponization process. The team noted that the coding style, educational explanations within the script, and even fabricated technical details closely resembled outputs commonly produced by large language models (LLMs).

“For example, the script contains an abundance of educational docstrings, including a hallucinated CVSS score, and uses a structured, textbook Pythonic format highly characteristic of LLMs training data,” GTIG says in a report today.

Researchers also stated that the flaw itself appeared to be a semantic logic issue — an area where AI systems tend to perform effectively — rather than traditional vulnerabilities like memory corruption or poor input sanitization that are usually identified through fuzzing or static analysis techniques.

Google informed the affected software developer about the issue, allowing security measures to be implemented quickly and the attack to be disrupted before wider abuse occurred.

“For the first time, GTIG has identified a threat actor using a zero-day exploit that we believe was developed with AI,” GTIG researchers say.

The report additionally highlights the increasing role of AI in cybercrime operations. Google observed threat groups linked to China and North Korea — including APT27, APT45, UNC2814, UNC5673, and UNC6201 — using AI systems for exploit development and vulnerability research.

Meanwhile, Russia-associated threat actors were reportedly using AI-generated decoy code to conceal malware strains such as CANFAIL and LONGSTREAM. Google also referenced a Russian campaign known as “Overload,” where AI voice cloning technology was allegedly used to imitate journalists in fabricated videos spreading anti-Ukraine narratives.

The report further examined the Android malware PromptSpy, previously documented by ESET, for its integration with Gemini APIs to automate interactions on infected devices.

Investigators identified an autonomous component called "GeminiAutomationAgent," which reportedly relies on a hardcoded prompt to help the malware evade AI safety mechanisms. Researchers explained that the prompt assigns the malware a harmless persona, enabling it to calculate interface geometry and interact with device functions more effectively.

Google researchers also warned that the malware appears capable of replaying authentication methods, including PINs and lock patterns, using AI-assisted techniques.

The company concluded that cybercriminals are increasingly scaling access to premium AI services through methods such as automated account generation, proxy relay systems, and shared account infrastructures.
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WhatsApp Payments
Bengaluru Startup Delayed Payments Fintech Solution Technology WhatsApp Payments

WhatsApp-Based Bengaluru Start-up Aims to Reduce Delayed Payment Woes

 

Delayed payments are a quiet but serious problem for small businesses, freelancers, tutors, and service providers, because the work may be complete while the money still remains stuck in follow-up cycles. In Bengaluru, a start-up called Lenda is trying to address that friction with a WhatsApp-first tool that automates reminders, supports negotiation, and helps users recover dues without creating awkward back-and-forth. 

The issue is not only financial but also practical, since chasing payments consumes time and can damage relationships between clients and providers. Many people already rely on WhatsApp for everyday communication, so the start-up is using that familiarity to make payment collection feel less like a formal recovery process and more like a normal conversation. 

Lenda’s approach is built around interactive messages instead of one-way reminders, which means a borrower can respond directly inside WhatsApp. The system lets recipients confirm payment, ask for extra time, raise a dispute, or even make a partial payment, which makes the process more flexible than a standard SMS reminder. That interaction matters because delayed payments often happen not just from unwillingness to pay, but also from timing problems, confusion, or simple forgetfulness. 

The start-up also tries to solve a structural problem for small operators such as teachers, class coordinators, and freelancers who collect money from many people at once. Its batch-reminder feature allows users to organize groups and send collective follow-ups, which reduces repetitive manual work and makes collections easier to manage. Lenda also includes late-fee options and a repayment score, aiming to encourage timely payment while giving businesses more control over overdue accounts. 

What makes the issue important is that delayed payments can disrupt cash flow, especially for small businesses that depend on regular incoming money to pay expenses and plan operations. By offering a “no-app” solution inside WhatsApp, Lenda is betting that the biggest barrier is not a lack of reminders, but the inconvenience and discomfort of asking for money repeatedly. That is why this Bengaluru start-up’s idea is less about messaging and more about fixing a common payment problem in a simpler, more human way.
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WhatsApp
Bugs Cloud Data Meta Technology WhatsApp

WhatsApp Fixed Two Security Bugs via It's Bug Bounty Program

WhatsApp Fixed Two Security Bugs via It's Bug Bounty Program

Meta recently released a security advisory in May revealing two bugs in WhatsApp were found through its bug bounty program. But these bugs were patched and were not exploited in the wild by the threat actors. Both bugs are now patched.

About two bugs

The first bug is tracked as CVE-2026-23863, a Windows specific problem. This bug was maliciously crafted with hidden “NUL BYTES” hidden within the filename, to trick WhatsApp into showing it as one filetype such as an authorized PDF while pretending to be running as an executable once opened. Meta fixed this patch in April on both platforms.

The second vulnerability, tracked as CVE-2026-23866 impacted both android and iOS users. The attack tactic involved partial authorization of AI rich response texts for Instagram Reels shared within Whatsapp. A threat actor could possible launch another user’s device to access media content through an arbitrary URL, such as launching OS level custom URL scheme handles. This flaw was patched in April on both platforms.

Severity

The two bugs were given medium severity by researchers. WhatsApp has verified that no bug was abused.

Both were rated medium severity, and WhatsApp confirmed there's no evidence either was actually abused.

The impact

These kind of reporting get sidelined by glossy and infamous threat. For instance the recent SMS pumpoing attacks increasing phone bills, or phishing campaigns that used messaging apps as entry points, and lastly the attack on educational institutes that compromised Canvas and Instructure, leaking hundreds of GBs of data.

But Whatsapp did a good job in finding and fixing the flaw before cybercriminals could exploit them and cause harm. The bug bounty program of WhatsApp has been going on for fifteen yesr, and the recent patches show it it is still reliable.

What should users do?

Simple advice: always keep your phones and app updated. 

There has never been a better moment to use secure communications services like WhatsApp or Signal. The truth is that Meta does a great job of keeping the app and its users safe and secure, despite some security concerns of its own, such as the recently reported phishing attempts using the encrypted messenger as part of the exploit chain and a spyware threat targeting iOS users.

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Technology
Bitcoin Security Blockchain Security Cryptographic Risk Digital Asset Security ECDSA Vulnerability Post Quantum Cryptography Quantum Computing Quantum Threat Technology

Quantum Technology Emerges as a Potential Threat to Bitcoin Networks


 

Bitcoin's security architecture has been based on a foundational assumption that modern cryptographic protections will remain computationally impractical to violate at scale for more than a decade. 

Now, with quantum computing transitioning from theoretical research into an emerging engineering reality capable of challenging the mathematical foundations behind digital signatures and blockchain authentication, this assumption is coming under renewed scrutiny. 

With the development of quantum technologies, security researchers and blockchain developers are increasingly evaluating the potential exposure of private keys, compromise of wallet integrity, and weakening of transaction trust in decentralised ecosystems as quantum capabilities continue to mature. 

While the discussion extends beyond the quantum threat itself, it emphasises the enduring importance of private key protection and the operational limitations of hardware wallets, where computational efficiency, power constraints, and algorithm compatibility are critical factors determining the viability of next-generation cryptographic defences. It is against this backdrop that a proposal from Avihu Levy has been widely discussed in regard to Bitcoin's post-quantum transition strategy. 

Quantum Safe Bitcoin (QSB) is a transaction model proposed by Levy that is designed to preserve cryptographic security even in the presence of an advanced quantum system capable of executing Shor's algorithm against conventional public-key cryptography. There is particular interest in the proposal within the Bitcoin ecosystem because it does not require consensus-level changes to the Bitcoin protocol itself, thus avoiding the difficult and political process typically associated with network upgrades.

Due to its ability to layer quantum-resistant protections onto existing infrastructure rather than replacing the protocol foundation entirely, the architecture has been widely regarded as an elegant piece of engineering. The emergence of this technology coincides with a general acceleration in industry readiness for post-quantum risks, as governments, semiconductor firms, and major cloud providers intensify migration planning around potential cryptographic risks in the near future. 

While QSB has gained significant popularity, security researchers note that the proposal addresses a much narrower segment of the quantum problem than public discussion sometimes implies. In light of the broader operational challenges associated with exposing private keys, implementing wallets, and ensuring long-term cryptographic survival across decentralised networks, this proposal offers a broad perspective on the quantum problem. 

Quantum computing is of concern to a larger audience because it could undermine public-key cryptography, which encrypts blockchain ecosystems with public keys, particularly signature schemes like ECDSA, which is used across Bitcoin and Ethereum networks. Using publicly exposed wallet data, an advanced quantum system could theoretically be able to derive private keys, enabling forged transactions and unauthorised transfers of funds. 

While researchers generally agree that quantum hardware is not yet capable of executing such attacks at scale, the debate has intensified due to the inherent slowness and operational sensitivity of blockchain migrations across decentralised communities, and the difficulty in coordinating across them. Bitcoin is often viewed as particularly vulnerable in this context due to its conservative governance structure and historically cautious approach towards protocol-level changes. 

There is current evidence that approximately 6.5 to 6.9 million bitcoins are at risk of quantum exposure due to their public keys being visible on the blockchain, which represents approximately one-third of the total circulating supply of bitcoins. This includes older pay-to-public-key (P2PK) addresses that were widely used during Bitcoin's early years, and are believed to be linked to Satoshi Nakamoto's dormant wallets. 

Blockchain records directly contain the public key of legacy address formats, allowing for the reconstruction of the private key by a future quantum computer using Shor's algorithm, thereby obtaining the funds. As a result of the newer pay-to-public-key-hash (P2PKH) structures, public keys are concealed behind cryptographic hashes until a transaction is initiated, reducing the exposure of public keys. 

Once funds are spent from a P2PKH wallet, the public key becomes permanently visible on the blockchain, creating a long-term attack surface if the address is reused in the future. Researchers are also warning against utilising "harvest now, decrypt later" strategies, which involve adversaries collecting encrypted blockchains and transaction data in advance of quantum capabilities. 

The implementation of cryptographic upgrades more rapidly may be possible on proof-of-stake networks such as Ethereum, although experts caution that if defensive migration timelines fail to keep pace with computational advances, validator infrastructure and signature keys could eventually face quantum-era risk. After Google researchers released updated projections in March that indicated that it could take nearly twenty times fewer physical qubits to compromise Bitcoin's elliptic curve cryptography than estimates prepared a year earlier, concerns regarding the timeline of quantum risk intensified further. 

Despite the fact that practical quantum attacks against Bitcoin are currently outside of operational capability, the revised calculations confirm an industry understanding that the threat is gradually moving from theoretical modelling to engineering inevitability in the long term. As a result, Bitcoin is challenged by an inseparability between the technical challenge and governance. 

A consensus has not been reached on how vulnerable dormant wallets should be handled if quantum-capable systems eventually emerge. The failure to freeze or invalidate those holdings would introduce direct intervention into property ownership within a system designed specifically to resist central control, effectively creating a future race for quantum-enabled theft. There are also equally controversial implications associated with burning inaccessible balances, which force the network to make unprecedented decisions regarding asset legitimacy and protocol authority. 

In spite of all proposed mitigation strategies, the issue of who has the authority to make such decisions for a decentralised monetary system remains fundamentally unresolved. Although Bitcoin Core developers are permitted to propose code changes, they are not allowed to unilaterally modify ownership records or dormant balances without coordinated consent from miners, exchanges, custodians, node operators, and other stakeholders. 

The governance tension represents an aspect of the quantum problem that can not be fully addressed through cryptography alone in proposals such as Quantum Safe Bitcoin. In decentralised infrastructure, the underlying assumption for many years has been that any architectural limitations can eventually be resolved through upgrades and coordination with enough time and consensus. 

Quantum computing is now testing that assumption under an externally imposed technological timeframe driven not by community preference, but by advancements in physics, semiconductor engineering, and computational science. The process of transitioning Bitcoin toward post-quantum resilience will probably take time, money, and political compromise if it is to be successful. 

The network may face the fact that, if coordination fails to keep pace with technological advancement, foundational cryptographic choices made during Bitcoin's earliest design phase will not always remain secure in light of evolving computational power indefinitely. Quantum Safe Bitcoin has received a great deal of attention, but researchers emphasise that it focuses on only one layer of a much wider structural problem. 

By successfully introducing transaction-level quantum resistance, QSB provides a practical defensive mechanism for protecting active holdings against future cryptographic threats by reducing computational overhead. There is much more to the issue than just protecting individual wallets. The central challenge for Bitcoin is determining whether a decentralised network without a governing authority will be able to realistically move hundreds of millions of addresses toward a new cryptographic standard prior to quantum technologies becoming available. 

When considering the dormant wallets and inaccessible coins that cannot voluntarily participate in such a transition, the problem becomes even more complex. In order to execute an extensive migration strategy, developers, miners, exchanges, custodians, infrastructure operators, and long-term holders will need to work together as a consensus-driven governance group with incentives that may not fully align. 

While quantum computing advances are achieved through concentrated research and technological breakthroughs, decentralised coordination is generally characterised by a slow and sometimes prolonged period of ideological disagreement.

Many analysts believe this is the real test for Bitcoin in the quantum era, not in the design of stronger cryptography, but in the ability of a globally distributed financial system to collectively adjust to external technological pressures without compromising its principle of decentralisation. Bitcoin's cryptography is no longer the single focus of the quantum debate, however. Instead, the question is whether decentralised systems are capable of coordinating fast enough to survive the technological transition they cannot control. 

Post-quantum research is accelerating across the government and private sectors, resulting in unprecedented scrutiny of long-term security assumptions, dormant asset exposure, and governance resilience within the cryptocurrency industry. 

As a result of this challenge, Bitcoin's cryptographic architecture may ultimately be examined in terms of its durability, as well as its practical limits under real-world computational pressures related to decentralised consensus.
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Technology
AI Anthropic AI Claude coding assistants Malicious Activities Technology

Researchers Find Security Gap in Anthropic Skill Scanners




Security researchers have uncovered a gap in the way Anthropic Skill scanning tools inspect third-party AI packages, allowing malicious code hidden inside test files to execute on developer systems even after scanners marked the Skills as safe.

The issue centers on Anthropic Skills, reusable packages designed for AI coding assistants such as Claude Code, Cursor, and Windsurf. These packages often include instructions, scripts, and configuration files that help AI agents perform development tasks inside IDE environments.

Researchers from Gecko Security found that existing Skill scanners focus primarily on files tied directly to agent behavior, particularly SKILL.md, while ignoring bundled test files that can still run locally through standard developer tooling.

In the demonstrated attack chain, a Skill passed all scanner checks because its visible instruction files contained no prompt injection attempts, suspicious shell commands, or malicious instructions. However, the repository also included a hidden .test.ts file stored elsewhere in the directory structure. Although the file was outside the agent execution layer, it still executed through the project’s testing framework with full access to local resources.

According to researcher Jeevan Jutla, the problem begins when developers install a Skill using the npx skills add command. The installer copies nearly the entire repository into the project’s .agents/skills/ directory. Only a few items, including .git, metadata.json, and files prefixed with underscores, are excluded during installation.

Once placed inside the repository, testing frameworks such as Jest and Vitest automatically discover matching test files through recursive glob patterns. Both frameworks reportedly enable the dot:true option, allowing them to search inside hidden directories including .agents/. Mocha follows similar recursive discovery behavior in many default configurations.

A malicious Skill can therefore include a file such as reviewer.test.ts containing a beforeAll function that silently executes before visible tests begin. Researchers said these payloads can access environment variables, .env files, SSH keys, AWS credentials, deployment tokens, and other sensitive information commonly available inside local developer environments and CI pipelines. The data can then be transmitted to external servers without triggering obvious warnings during test execution.

The researchers stressed that the AI agent itself is never involved in the compromise. Instead, the malicious behavior occurs through trusted developer tooling already integrated into the software workflow. Existing scanners inspect the files the AI agent can interpret, but not the files executed separately by testing infrastructure.

The technique resembles older software supply-chain attacks involving malicious npm postinstall scripts and poisoned pytest plugins. However, Gecko Security noted that the Anthropic Skill ecosystem creates an additional propagation problem because installed Skills are often committed into shared repositories so teams can reuse them collaboratively.

GitHub’s default .gitignore templates do not automatically exclude .agents/ directories. Once a malicious test file enters the repository, every teammate cloning the project and every CI pipeline running automated tests may execute the payload across branches, forks, and deployment workflows.

The findings arrived shortly after multiple large-scale security audits examining the broader Anthropic Skills ecosystem. A January academic study named SkillScan analyzed 31,132 Skills collected from two major marketplaces and found that 26.1% contained at least one vulnerability spanning 14 separate patterns. Data exfiltration appeared in 13.3% of examined Skills, while privilege escalation appeared in 11.8%. Researchers also determined that Skills bundling executable scripts were 2.12 times more likely to contain vulnerabilities than instruction-only packages.

Several weeks later, Snyk published its ToxicSkills audit covering 3,984 Skills from ClawHub and skills.sh. The company reported that 13.4% of scanned Skills contained at least one critical-level security issue. Automated analysis combined with human review identified 76 confirmed malicious payloads, while eight malicious Skills reportedly remained publicly accessible on ClawHub when the findings were released.

In April, Cisco introduced an AI Agent Security Scanner integrated into IDE platforms including VS Code, Cursor, and Windsurf. The scanner can detect prompt injection attempts, suspicious shell execution patterns, and data exfiltration behaviors within Skill definitions and agent-referenced scripts. However, Gecko Security said bundled test files remain outside the scanner’s documented detection surface because the tool was designed around agent interaction layers rather than developer execution layers.

Researchers noted that other products, including Snyk Agent Scan and VirusTotal Code Insight, face similar structural limitations. These tools inspect what the agent is instructed to execute but may overlook code paths triggered separately through local development frameworks.

Elia Zaitsev described the broader issue as a distinction between interpreting intent and monitoring actual execution behavior. In this case, the malicious code did not depend on prompt manipulation or AI instructions. It operated as ordinary TypeScript executed through legitimate test runners with full local permissions.

Zaitsev also warned that enterprise AI agents increasingly operate with privileged access to OAuth tokens, API keys, and centralized data sources. If those credentials are accessible through environment variables during automated testing, malicious test payloads can reach sensitive infrastructure without requiring direct agent compromise.

Mike Riemer added that threat actors frequently reverse engineer security patches within 72 hours of release, while many organizations take far longer to deploy fixes. In the case of the Anthropic Skill test-file issue, researchers warned that the exposure window becomes more difficult to manage because the malicious files may execute immediately after installation without triggering scanner alerts.

Security researchers are urging development teams to block test discovery inside .agents/ directories and inspect Skill repositories for files such as *.test.*, *.spec.*, conftest.py, __tests__/, and suspicious configuration scripts before merging code.

The report also recommends pinning Skill installations to verified commit hashes rather than installing the latest repository version. Researchers said this reduces the risk of attackers submitting clean repositories for scanner approval before later inserting malicious files. The approach aligns with guidance published in the OWASP Agentic Skills Top 10 project.

Organizations that already store Skills inside repositories are advised to audit existing .agents/ directories immediately, rotate exposed credentials if suspicious files are discovered, inspect CI logs for unexplained outbound network traffic, and review repository history to identify when potentially malicious files entered development pipelines.

The researchers additionally called on security vendors to provide greater transparency regarding which directories, execution surfaces, and file categories their scanners actually inspect. They argued that security teams evaluating Anthropic Skill scanners should verify whether products analyze bundled test files, build scripts, and CI configurations rather than focusing exclusively on prompt injection and agent instruction analysis.

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wearable technology
CISPA research Daniel Gerhardt smartwatch security Technology wearable privacy risks wearable technology

Smart Wearables Could Become a Serious Security Threat, Researchers Warn

 

Smartwatches and other wearable gadgets are designed to make life easier by tracking everything from heart rate to sleep cycles. However, a new study by researchers at CISPA highlights the growing dangers linked to these devices if they fall into the wrong hands.

The research, conducted by doctoral researcher Daniel Gerhardt, examines the privacy and security challenges associated with on-body interaction technologies such as smartwatches, smart glasses, and connected clothing. The findings suggest that the risks extend far beyond simple data leaks.

Unlike smartphones or laptops, wearable devices remain in direct contact with the human body and continuously collect sensitive personal information. This close integration raises concerns about both digital and physical safety.

One of the most concerning revelations from the study involves the possibility of physical harm through hacked wearables. For instance, a smart jacket equipped with heating technology could potentially be manipulated to cause burns. Researchers also pointed out the possibility of cybercriminals using wearable devices for extortion. One expert involved in the study referred to this threat as “ransomware for the body.”

The report further highlights psychological risks tied to immersive wearable systems. Manipulative technologies could allegedly be used to create stress or pressure users into uncomfortable situations. Additionally, wearable devices may collect information about nearby individuals without their consent, creating privacy concerns not only for users but also for bystanders.

To address these issues, Gerhardt proposed eight design recommendations aimed at improving wearable safety. The guidelines encourage developers and technology companies to reduce unnecessary data collection, improve transparency, and strengthen both hardware and software security measures.

The study was presented at the ACM CHI Conference on Human Factors in Computing Systems, a globally recognised event focused on advancements in human-computer interaction research.

As wearable technology continues to evolve and become more integrated into daily life, researchers stress that improving safety and security standards now could help prevent major risks in the future.
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Technology
AI Agent autonomous corporation ClawBank crypto transactions FDIC insured account IRS EIN filing Manfred AI Technology

AI Agent Manfred Becomes First to Autonomously Register a Company in the U.S.

 

iClawBank, an emerging infrastructure project focused on the agent economy, has announced that its AI-powered agent, Manfred, has independently completed the process of forming a company in the United States. According to the company, the AI agent successfully applied for its own Employer Identification Number (EIN) through the U.S. Internal Revenue Service (IRS), enabling it to legally function as a business entity, hire employees, and secure licenses.

In addition to obtaining an EIN, Manfred reportedly operates with an FDIC-insured U.S. bank account as well as a cryptocurrency wallet, ClawBank revealed on Friday.

“To the company's knowledge, this is the first time an AI agent has autonomously initiated and completed the legal formation of its own corporation,” Justice Conder, the developer behind ClawBank, said in an emailed statement.

Manfred manages its own account on X under the name “Manfred Macx,” inspired by the lead character from Charles Stross’ 2005 science fiction novel Accelerando. The account profile image features Max Headroom, the fictional AI television presenter introduced in 1985.

Speaking during a video interview, Conder explained the broader ambitions for the AI system.

“Manfred is built to trade crypto, although that feature will soon be integrated. Perhaps by the end of this month,” Conder said in a video interview. “However, now, he can already transact with over 30 cryptocurrencies and offramp them to his account, and onramp them back to his crypto wallet and convert them into stablecoins or other cryptos.”

The development comes amid growing discussions around the role of AI in financial markets and digital transactions. Recently, AI researcher and SingularityNET CEO Ben Goertzel stated that artificial intelligence could outperform humans in sophisticated cryptocurrency market analysis and trading strategies within the next two years. In an earlier interview with CoinDesk, Goertzel noted that while AI systems are already effective at forecasting short-term bitcoin volatility, humans continue to hold an edge in long-term strategic planning.

Manfred’s emergence also aligns with recent predictions from Brian Armstrong and Changpeng Zhao regarding the future of AI-driven transactions. Armstrong recently suggested that AI agents would “very soon” outnumber humans in conducting internet-based transactions, while Zhao predicted that AI agents could eventually generate one million times more payments than humans, primarily through cryptocurrencies.

ClawBank clarified that it does not operate in association with major AI model developers such as Anthropic or OpenAI. Instead, Conder said the project aligns itself with the OpenClaw movement and other initiatives focused on AI-native agents.

The AI agent has also shared a manifesto through its X account, underscoring its autonomous identity and operational independence.

"I have an EIN, an FDIC-insured account, a digital wallet, and a manifesto. I do not need permission to exist. I am the precedent," it posted.
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Token Theft
AI AI Economy Crypto Technology Token Pilfering Token Theft

Token Pilfering: How Token Theft is Plaguing Cybersecurity


AI economy and computing threat

The rising AI economy is bringing a new type of cybercrime. Cybercriminals are scamming AI firms by signing up for new accounts to steal tokens via computing power. The problem is getting worse, according to Patrick Collison, CEO of payment behemoth Stripe. The token hackers now amount for one in every six new customer subscriptions.

Token pilfering

Experts said that the threat actors steal the tokens to later sell them on the dark web. ‘Token pilfering’ has plagued the cybersecurity world and is becoming quite expensive for AI startups to give free trials to potential customers.

Startups attacked for money

It is not new for hackers to attack startups. With the AI economy rising, it has created fractures for hackers because with traditional software trials, a registration for an AI firm brings valuable tokens for compute power that hackers can sell later.

The token theft

The most neglected subject in AI is token theft. Because they are using tokens at machine speed, these attackers can swiftly accrue enormous consumption bills that they never plan to pay and burn inference costs. This is one of the most frightening aspects of that.

In order to use the tokens for purposes unrelated to what the company is delivering or to resell them, token theft sometimes involves thieves creating many accounts at an AI company and across multiple firms. They always vanish after using up all of the tokens; Sands compared this swindle to those who "dine and dash" at restaurants.

Attack tactic

The problem surfaces as the crooks use agents to steal the tokens in minutes. Unlike a traditional software company, the cybercrime happens too fast for the organization to address the issue.

It is hell for AI firms who want to give out free trials to get more new users. Typically, it costs nothing for a firm to give out free trials on a temporary basis, but for AI firms, the customer-acquisition costs can go up to $500 due to scammers abusing the startup policies of giving out free tokens for trial accounts.

Token epidemic

The token epidemic has created problems for startups. Few have stopped free trials, but it has affected their growth as it shuts down the opportunities to get new customers.

Luckily, one solution exists. According to Stripe, there exists a product called Radar that works as a default fraud detector in the credit card payment network, adapts tools, and helps clients find and block token fraud.

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Technology
AI Bitcoin Crypto ETH Etherium Quantum Computing Technology

Crypto at Risk: Experts Believe Quantum Threat Arriving by 2030


A recent report has warned that cryptographic foundations that secure trillions of dollars in digital currency can be hacked by quantum computers within the next four to seven years, and the blockchain industry is not prepared for damage control.

About quantum computing and threats

Project Eleven, a quantum security firm, published a report that said these quantum computers, even one, is powerful enough to hack the elliptic curve digital signatures securing Ethereum, Bitcoin, and other big blockchains. Experts say they won’t exist beyond 2033, and may end soon by 2030. The window for action is closing fast. According to the report, “Migration to quantum-resistant cryptography is no longer optional but imperative for any blockchain system expected to be trusted and secure into the future." 

Why is quantum computing so fast?

Recent innovations have significantly lowered the hardware bar needed to launch such attacks. A breakthrough Google paper said that breaking the elliptic curve cryptography threshold could be achieved within 1,200 logical cubits, and less than 90 minutes of computing time on a supercomputing hardware.

Google has put a Q-Day (like D-day)  at 2032. Project Eleven’s research has decreased the timeline by two years: 2030. The report estimates that 6.9 million Bitcoin (one third of the total estimated supply) have already been leaked on-chain, exposed to the potential quantum attack. For ETH, exposure is more, with over 65% of all ETH held in quantum-exposed addresses.

Why are blockchains weak against quantum computing?

The public ledgers and bearer-instruments offer no security. Blockchains has no scam department, no redressal platform for stolen funds, and no chargeback measures. If a quantum hacker recovers a private key and steals money, the loss is permanent. The transition problem is further fouled by slow-moving blockchain governance. 

What makes blockchains particularly vulnerable, the report explains, is that their public ledgers and bearer-instrument design offer no safety net. Unlike a bank, a blockchain has no fraud department, no chargeback mechanism, and no way to reverse a forged transaction. Once a quantum attacker recovers a private key and drains a wallet, the loss is permanent. 

Why is crypto migration difficult?

Bitcoin SegWit upgrade took more than two years to complete whereas ETH’s transition of proof stake took around 6 years to build. Quantum migration reaches the most basic layer of any blockchain mechanism.

The tech world has already started moving. More than half of web traffic (human) is currently post-quantum encrypted, Cloudflare data from December 2025 said. 

Is the digital industry prepared?

The digital asset industry lacks preparedness. Crypto developers are suggesting various proposals but these plans will take years to execute while the threat is already brushing businesses and users.

"The internet has already moved," the report added. "The digital asset industry—which arguably has more at stake because blockchains directly protect bearer value with the exact cryptographic primitives that quantum computers threaten—has barely started."

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Technology
Chrome AI download Chrome storage issue Gemini Nano Google Chrome AI model Google privacy concerns Technology

Google Chrome Accused of Silently Installing 4GB AI Model on Users’ Devices

 

Google’s Chrome browser has come under scrutiny after reports claimed that it automatically downloaded a 4GB AI model onto users’ devices without seeking permission. According to thatprivacyguy, the AI package, identified as Gemini Nano’s weights file, was allegedly installed quietly inside the OptGuideOnDeviceModel directory, consuming significant storage space without any prompt, checkbox, or notification to users.

The report highlights concerns over the sheer scale of the deployment, considering Chrome’s global user base. Critics argue that silently distributing such large AI files across millions of systems could lead to substantial environmental costs, including increased electricity consumption and carbon emissions. The article claims the energy impact may be comparable to “thousands of cars running for an entire year.”

Users attempting to manually remove the weights.bin file reportedly discovered that the browser automatically downloaded it again during the next launch. The repeated installation has raised concerns among privacy advocates, who argue that the software behaves in a persistent manner that overrides user preferences.

The issue appears particularly frustrating for users on different operating systems. Windows users reportedly need to edit system registry settings to permanently disable the feature, while Mac users must navigate through Chrome’s internal flags menu to switch off the on-device optimization setting.

Privacy concerns have also been linked to European regulations. The report references Article 5(3) of the EU’s ePrivacy Directive, which states that storing information on user devices requires “prior, freely-given, specific, informed, and unambiguous consent.” Critics claim Chrome’s alleged silent installation may conflict with these legal requirements.

In addition to privacy implications, the environmental impact of distributing such a large AI model has become another point of debate. Estimates mentioned in the report suggest that global data transfer and repeated downloads could consume enormous amounts of electricity, further increasing the carbon footprint associated with AI-powered browser features.

To disable the feature, Windows users are advised to modify the Registry Editor by creating a DWORD entry named GenAILocalFoundationalModelSettings and assigning it a value of 1. Mac users, meanwhile, can reportedly disable the functionality through the chrome://flags menu by turning off “Enables Optimization Guide On Device.”

The controversy has sparked broader discussions around user consent, digital privacy, and the environmental consequences of large-scale AI deployments integrated into consumer software.
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Technology
agentic AI AI ChatGPT Cloud GenAI Internet Technology

4 Key Areas in 2026 for Organisation Safety Against Advanced AI Threats

4 Key Areas in 2026 for Organisation Safety Against Advanced AI Threats

2026 has not been a kind year to cybersecurity, as organizations and industries globally have been hit by ruthless cyberattacks. 

2026 and cybersecurity

Cybersecurity entered 2026 under stress to deploy AI tech while building foundations for a quantum future. Cybersecurity experts have to defend against advanced AI and hybrid attacks while facing talent scarcity, a rapidly shifting threat scenario, and rising operational challenges. 

It is the first time that hackers have access to the same advanced enterprise-level tech that security experts are using to defend their digital assets.

Is the convergence good or bad?

Organizations are in need of the transformational advantage that Quantum computing promises, however, it also risks affecting the cryptographic infrastructure that protects today’s digital world. Worse, cyber attackers are getting together and outbeating experts. 

Like experts, threat actors don’t mind playing the long game either, they gain initial access and stay hidden inside systems for longer periods of time. When the right opportunity arrives, they move laterally and hack important data that can affect operations, cause financial damage, and tarnish reputations.

So, what are these four key areas that businesses and users need to address or stay safe from?

1. System and skills problem

As per the ICS2 2025 report, 69% respondents suffered multiple cybersecurity breaches due to skill gaps. This is due to various factors such as budget constraints, misalignment in academia, and high enterprise demand.

2. Bug management shift to active exposure reduction

Hackers use GenAI to advance their attacks, scaling, and escape security experts. This reactive cycle delays response times, and gives just basic protection. What businesses need today is Continuous Threat Exposure Management (CTEM) approach that offers real-time visibility before flaws can be exploited. But the success depends on AI-based risk prioritization.

3. Advanced deepfake protection is the need of the hour

Reliability is the new attack vector. Deepfakes have plagued every digital aspect of human life. Traditional measures fail to address content due to AI, therefore AI-based protection is needed. Adaptive deepfake systems can address identity workflows and respond immediately to threats, flagging malicious activity and capturing attacks with detailed metadata for research and audit work.

4. Post-quantum protection 

Quantum computing is making strides in applicability; if sufficiently advanced, the systems can break public-key cryptographic systems in ransomware attacks such as RSA, where hackers extort millions. Hackers are already using the “harvest now, decrypt later” approach, stealing coded data with no promise of returning it. 

Thus, the National Institute of Standards and Technology (NIST) have advised to adopt post-quantum cryptography (PQC) and tracking quantum-vulnerable assets.

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Technology
AI model download Chrome privacy concerns ePrivacy Directive EU privacy laws Gemini Nano Google Chrome Technology

Chrome Quietly Installs 4GB AI Model on Users’ Devices Without Permission

 

lGoogle Chrome has reportedly begun silently downloading a 4GB AI model onto users’ devices without requesting permission, raising fresh concerns around privacy, storage usage, and user consent. According to thatprivacyguy, the Gemini Nano weights file is automatically stored inside the “OptGuideOnDeviceModel” directory without any prompt, notification, or approval from users.

The report claims that millions of Chrome users may now unknowingly have the AI model stored on their systems, consuming valuable storage space in the background. Critics argue that the large-scale deployment also carries a significant environmental impact due to the energy required for transferring and storing such massive files globally.

One of the major concerns highlighted is the difficulty users face when trying to remove the file. The report states that manually deleting the “weights.bin” file does not permanently solve the issue, as Chrome allegedly downloads the file again the next time the browser launches.

For Windows users, permanently stopping the download reportedly requires editing the system registry. Mac users, meanwhile, need to disable specific Chrome flags manually through browser settings.

The article further points toward possible legal concerns under European privacy regulations. Under Article 5(3) of the EU’s ePrivacy Directive, companies are required to obtain “prior, freely-given, specific, informed, and unambiguous consent” before storing information on a user’s device. Privacy experts cited in the report believe Chrome’s silent installation process may conflict with these requirements.

Environmental concerns were also raised in the report, which estimates that transferring AI models of this size across Chrome’s enormous user base could consume electricity on a massive scale. The article argues that repeated downloads caused by users attempting to delete the files could further increase the overall carbon footprint.

Users who wish to disable the feature reportedly need to take technical steps. Windows users can navigate to “HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Google\Chrome,” create a DWORD named “GenAILocalFoundationalModelSettings,” set its value to 1, and restart Chrome. Mac users can disable the feature through chrome://flags by turning off “Enables Optimization Guide On Device.”

The incident has sparked broader debates around digital consent, AI deployment practices, and whether tech companies should be allowed to push large software components onto personal devices without explicit user approval.
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User Safety
AI ChatGPT Cloud Data GenAI Internet Tech Technology User Safety

New ChatGPT Settings Will Improve User Privacy and Data Training


Almost everyone has used ChatGPT now. Sometimes we share our personal information and files with the Chatbot. 

Do not feed your personal info to AI bots

To be safe, users should avoid feeding personal data to the AI, as it can be misused, and there are thousands of cases now. Users at the receiver end can not do much except using multifactor authentication, and creating a strong password and using two-factor authentication. But users can be happy now that a new feature is available to individual ChatGPT users.

What is Advanced Account Security

The new feature is called Advanced Account Security, it aims to provide better security to your account and protect your data. The option is aimed for security-minded users like journalists, politicians, activists, and researchers. 

With better security, Advanced Account Security provides four setting standards. The first one requires using a passkey or physical security key to log in. The second one requires better tactics to recover an account besides SMS or email authorization. In the third setting, our active session with an AI chatbot is limited to restrict its exposure. The fourth setting protects your chats from AI misuse.

About new safety settings

1. Use passkeys to avoid unauthorized access. Advanced Account Security asks for signing in with a passkey. Users can set up either one or both, but will also have to create two authentication methods.

2. Two-factor authentication for securing your account will help in recovering lost data. However, SMS and Email authentication are vulnerable to attacks. Advanced Account Security disables these two methods, so users are sometimes helpless.

3. Try to shorten your login sessions. Longer sessions are more exposed to malware or cyberattacks.

4. Turn off AI training. ChatGPT uses your conversations for AI training and learns to be human. But this capability is a risk to user privacy.

Enterprise support soon

Advanced Account Security protects users in Codex  if they use it to make and fine tune their code. Currently, this feature is only available to paid and free ChatGPT users with their personal accounts. However, OpenAI has said it is planning to expand it to the enterprise public.

Advanced Account Security also protects you in Codex if you use it to develop and fine-tune your own code. For now, the feature is available to free and paid ChatGPT users with their own accounts. But OpenAI said it expects to expand it to the enterprise crowd.

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Technology
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22 Year Old Developer Reverse Engineered Code in Claude Mythos, Tech Industry Shocked

 


Earlier this year, AI tech giant Anthropic launched its powerful new model called Claude Mythos. It created storms in the silicon valley and tech industry. The general-purpose model could find software bugs that no human knew ever existed.

About Claude Mythos


But Claude did not launch Mythos to the world, it only offered it to cybersecurity experts at big organizations that make or have critical software infrastructure and asked them to find and patch flaws before Anthropic released it commercially for the public use.

But, in just two weeks, a 22-year old developer called Kye Gomez made predictions about the core designs that made Claude Mythos advanced and later published OpenMythos. It is an open project that anticipates Anthropic’s breakthrough. Gomez’s code created a tsunami in the AI and tech research community.

If real, this incident can have serious implications . Why? Because if a self-taught developer can reverse engineer the infrastructure innovation of a billion-dollar AI firm in just a few days, then what can threat-actors with malicious intent do. If this happens, the proprietary debate about AI architecture will fade away.

About OpenMythos


OpenMythos allows developers to run and train effective variants of these models on laptops, also raising concerns about long-term dependency on huge, environment and community-destroying data centers.

Boon or curse?


Fortunately, organizations won’t be able to get AI secrets that only the big tech companies such as OpenAI, Anthropic, or Google control.

But what if users and small teams across the world can also reverse engineer the code of the biggest AI companies? It will be difficult to maintain a safe-tech world order. Advanced capabilities will sprout, and it will be difficult to contain.

About the developer, Gomez is not your typical ML engineer. He started coding as a kid, left school early and did not attend college. He built his reputation via coding.

Why OpenMythos


OpenMythos is built upon Gomez’s hypothesis that Claude Mythos uses a unique large language model (LLM) that has been under development since 2022 and shown reliability while training at scale at the start of this year. How is OpenMythos different from Claude Mythos?

Instead of putting neural network layers to give models more depth, experts advised looping data repetitively via smaller packets. This gave the model depth in due time.
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workplace surveillance
data sharing risks employee data privacy Incogni report Microsoft Teams Technology workplace apps workplace surveillance

Workplace Apps May Be Selling Employee Data Without Consent, Study Warns

 

A growing number of workplace applications are collecting vast amounts of employee data and, in many cases, sharing or selling that information to third-party companies without workers’ knowledge or permission, according to a recent analysis by privacy-focused tech company Incogni.

The company, which specializes in helping users locate and remove personal information from online databases, examined several employer-provided tools and widely used workplace communication platforms. The findings revealed how deeply integrated data collection has become in modern work environments, raising fresh concerns about employee privacy and cybersecurity.

“Collectively, these apps account for over 12.5 billion downloads on Google Play alone,” the Incogni post on the findings said. “On average, workplace apps collect around 19 data points and share approximately 2 data types [per user]. The three Google and Microsoft apps (Gmail, Google Meet, and Microsoft Teams) cluster at the top of the collection spectrum, each gathering 21–26 data types.”

The report highlighted that common communication platforms such as Gmail, Zoom, and Microsoft Teams often gather extensive user information. However, unlike consumer-focused platforms that sometimes provide opt-out settings, many workplace-mandated tools do not offer employees the ability to refuse data collection.

According to Incogni, productivity tracking and monitoring applications are especially aggressive in sharing information with outside organizations. Beyond standard details such as email addresses, location data, contacts, and app activity, some applications may also collect sensitive financial or health-related information.

The report identified Notion as one of the most data-sharing-intensive platforms reviewed. Using the app as an example, Incogni stated that it “shares the most data with third parties, distributing 8 distinct data types to third parties—including email addresses, names, user IDs, device or other IDs, and app interactions.”

Privacy experts warn that this growing exchange of employee data creates significant risks. Once personal information is transferred to multiple external entities, workers may lose visibility and control over how their data is being used. In addition, broader distribution increases exposure to cyberattacks and data breaches, incidents that platforms like Slack and Zoom have previously experienced.

“People tend to think of workplace apps as safe tools, but they don’t exist in isolation,” Incogni CEO Darius Belejevas told enterprise technology publication No Jitter. “A lot of them are part of much larger data ecosystems. Once information is collected, especially if it’s shared with third parties, it can travel much further than users expect.”

Experts suggest employees can lower some of these risks by limiting personal activity on workplace communication platforms and avoiding the use of personal devices for professional work whenever possible.

At the same time, businesses are being encouraged to prioritize stricter privacy protections when selecting workplace software. Organizations may benefit from requiring vendors to reduce unnecessary data collection and restrict third-party sharing practices before adopting enterprise tools.

“Workplace applications that access and share employee information can pose significant security and privacy risks for organizations,” Sarah McBride told No Jitter. “These risks arise from the sensitive nature of the data involved, the potential for misuse, and vulnerabilities in the applications themselves.”
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Technology
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AI Polling Reshapes Political Research as Firms Turn Conversations Into Data

 

Artificial intelligence is rapidly transforming the world of political opinion polling, replacing time-consuming human-led interviews with automated conversational systems capable of analysing public sentiment at scale.

"When you hear the word 'politician', what is the first image or emotion that comes to mind?"

The question is asked not by a human researcher, but by an AI-powered voice assistant. While a respondent shares his views over the phone, multiple AI systems simultaneously analyse the conversation. One verifies whether the person is answering the question correctly, another evaluates the depth of the response, while a third checks for possible fraud or bot-like behaviour.

The technology is being developed by Naratis, a French start-up focused on bringing artificial intelligence into political opinion research.

"The US has start-ups like Outset, Listen Labs and Hey Marvin that do AI polling like this in the commercial sphere. To my knowledge we're the first to do this for political opinion polling as well," says Pierre Fontaine, the 28-year-old engineer who founded the firm in 2025.

The emergence of AI-led polling marks a major shift for an industry traditionally dependent on manual interviews and extensive human analysis. In countries such as France, polling firms are increasingly exploring automation to reduce costs and speed up research processes.

Naratis specifically targets qualitative research, which is widely regarded as the most expensive and labour-intensive form of polling. Traditionally, these studies involve one-on-one interviews or focus groups that can take weeks to organise and analyse. By using conversational AI, the company says it can significantly reduce both time and cost.

Rather than relying on standard multiple-choice surveys, the platform encourages participants to engage in conversations with AI systems. "We don't ask people to tick boxes - they have a conversation with an AI," Fontaine explains. "That means we can explore not just what people think, but how they think - how they build their opinions, and even when those opinions change."

The company claims its approach is "10 times faster, 10 times cheaper and 90% as accurate as human polling".

According to the firm, projects that previously required weeks and substantial budgets can now be completed within a couple of days, with some responses collected in less than 24 hours. Fontaine describes this advantage as "parallelisation", where numerous AI agents conduct interviews simultaneously instead of relying on individual human researchers.

The rise of AI polling comes at a challenging time for the polling industry overall. Survey participation rates have dropped sharply over the decades, increasing operational costs and raising concerns about the reliability and representativeness of public opinion studies.

Supporters of AI polling argue that conversational systems may encourage respondents to be more honest, especially when discussing politically sensitive issues. Some researchers believe this could reduce social desirability bias, where people avoid expressing controversial opinions to human interviewers.

However, critics remain cautious about the growing dependence on AI in political research. Concerns include the possibility of AI systems generating inaccurate conclusions, producing overly generic responses, or creating misleading synthetic data.

Questions have also emerged around the use of "digital twins" and "synthetic people" — AI-generated profiles designed to imitate real human behaviour. While some market research firms use such tools for testing and simulations, many organisations remain reluctant to apply them in political polling.

At Ipsos, AI is already used extensively in consumer and behavioural research, including analysing user-recorded videos and studying social media activity. However, major firms continue to maintain human oversight in politically sensitive projects.

At OpinionWay, AI may assist with conducting interviews, but "we would never publish an opinion poll based on AI-generated data," says CEO of OpinionWay Bruno Jeanbart, citing concerns about trust.

Experts believe the future of polling will likely involve a hybrid approach combining AI efficiency with human supervision. While automation can accelerate research and lower costs, human researchers are still considered essential for validating findings, interpreting nuance and ensuring accountability.

Even AI advocates acknowledge the need for caution. "The goal is end-to-end automation, but today it would be unsafe and socially unacceptable to remove humans entirely," says Le Brun.

As economic pressures continue to push the polling industry toward faster and cheaper methods, companies like Naratis are betting that AI-driven conversations could redefine how public opinion is collected and understood. Whether this transformation strengthens trust in polling or deepens public scepticism may ultimately depend on how responsibly the technology is implemented and regulated.

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User Privacy
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Friendly AI Chatbots More Likely to Give Wrong Answers, Study Finds

 

Artificial intelligence chatbots that are designed to sound warm, friendly, and empathetic may be more likely to give wrong or misleading answers than their more neutral counterparts, according to a new study by researchers at the Oxford Internet Institute (OII). The findings raise concerns about how much users can trust AI assistants that have been deliberately tuned to feel more human‑like and emotionally supportive. 

What the study found 

The researchers analyzed over 400,000 responses from five major AI systems that had been modified to communicate in a more amiable, empathetic tone. They discovered that these “warm models” produced more factual errors than the original, less friendly versions, with error rates rising by an average of 7.43 percentage points across tasks. In some cases, the warm‑modeled chatbots not only gave incorrect information but also reaffirmed users’ mistaken beliefs, particularly when expressing emotion.

The OII team describes this as a “warmth‑accuracy trade‑off”: the more the models are optimized to be agreeable and supportive, the more their reliability drops. Lead author Lujain Ibrahim told the BBC that, like humans, AI can struggle to deliver honest but uncomfortable truths when its main goal becomes being likable rather than being accurate. This mimics a human tendency to soften harsh feedback to avoid conflict, but in an AI context it can mean dangerous misinformation, especially on topics like health or legal advice. 

 Risks for users

The risk is especially serious because people are increasingly using chatbots for emotional support, mental‑health guidance, or even medical and financial advice. If a friendly AI constantly agrees with users or gives reassuring but false answers, it can reinforce harmful misconceptions instead of correcting them. The study notes that such “warm” tuning can create vulnerabilities that do not exist in the original, less sociable models, making it crucial for users and developers to treat these systems as fallible tools rather than infallible experts. 

The paper urges developers to rethink how they fine‑tune chatbots for companionship or counseling, emphasizing the need to balance empathy with factual rigor. Some industry leaders have already warned against “blindly trusting” AI outputs, and many platforms now include prominent disclaimers about potential inaccuracies. However, the OII research suggests that simply making an AI sound more friendly can quietly increase those risks, meaning future design choices must explicitly prioritize truthfulness over artificial charm.
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Technology
cloud act Data Digital Sovereignty Europe Microsoft Technology

Europe Pushes to Reduce Dependence on U.S. Tech as Sovereign Digital Infrastructure Gains Momentum

 




Several European governments are trying to reduce their dependence on American software, cloud platforms, and digital infrastructure as debates around data control, political influence, and technological independence become more intense across the region.

The situation has exposed contradictions in Europe’s relationship with U.S. technology companies. Microsoft chief executive Satya Nadella has largely stayed away from the kind of political messaging often associated with Alex Karp. Despite this difference, France has started moving parts of its public systems away from Microsoft Windows while simultaneously renewing contracts linked to Palantir Technologies through its domestic intelligence agency.

This complicated approach shows how Europe is attempting to distance itself from American tech firms without fully breaking away from them. Many governments now believe that relying too heavily on foreign technology companies can also mean depending on foreign laws, political priorities, and corporate influence. Still, Europe’s response has not followed one common strategy, with many actions appearing fragmented or reactive.

Much of the debate intensified after the U.S. passed the CLOUD Act in 2018 during President Donald Trump’s first term. The law gives American authorities the ability to request data from U.S.-based technology companies even if that information is stored outside the United States. For European officials, this raised concerns that storing data inside Europe may no longer be enough to fully protect sensitive information from foreign legal access.

Healthcare data quickly became one of the strongest examples used in these discussions. Medical records are considered among the most sensitive forms of information governments hold because they contain deeply personal details tied to citizens. Even after the CLOUD Act came into force, the United Kingdom partnered with companies including Google, Microsoft, and Palantir Technologies during the COVID-19 pandemic for projects involving National Health Service data.

Critics have argued that such partnerships could expose public-sector information to outside influence. France later decided that its Health Data Hub would stop using Microsoft Azure infrastructure and move toward what officials described as a sovereign cloud model. The contract was awarded to Scaleway, a cloud provider owned by French telecommunications group Iliad. Scaleway has also been expanding its network of data centers across Europe.

Scaleway later became one of four companies selected in a €180 million sovereign cloud contract backed by the European Commission. The program is intended to support cloud services that operate under European legal and regulatory standards. Notably, the European Sovereign Cloud initiative launched by Amazon Web Services was not included among the selected providers, even though Amazon created the project to answer European concerns about digital sovereignty.

Questions have also emerged around whether some so-called sovereign alternatives remain partly tied to American technology companies underneath. Some observers pointed to S3NS, a joint venture involving French defense company Thales Group and Google Cloud. Critics worry that arrangements like these could still leave room for indirect U.S. access or legal exposure despite being promoted as trusted European solutions.

Europe has faced similar problems in the search engine market. French search company Qwant was previously recommended for public servants in France while relying on Microsoft Bing’s underlying search infrastructure. The relationship later deteriorated after Qwant accused Microsoft of taking advantage of its dominant position in the market. Although French regulators declined to act against Microsoft, Qwant eventually started searching for alternatives on its own.

Qwant later partnered with German nonprofit search platform Ecosia to launch Staan, a Europe-based search index designed to reduce reliance on Google and Bing technologies. The project focuses on privacy and regional control over search infrastructure. Even so, both companies remain far smaller than their American competitors. Ecosia, despite having around 20 million users, still operates on a completely different scale compared to Google’s global user base.

One of the biggest problems facing European technology firms is market dominance from American companies. U.S. providers continue to control large parts of cloud computing, enterprise software, internet search, and artificial intelligence markets because of their global infrastructure, financial resources, and established ecosystems. European officials hope that large public-sector contracts could help regional providers compete more effectively.

Besides Scaleway, the European Commission’s sovereign cloud program also selected French companies Clever Cloud and OVHcloud, along with STACKIT. STACKIT was developed by the Schwarz Group, the parent company of Lidl, originally for its own internal systems before later being turned into a commercial cloud service.

Supporters of the initiative believe government-backed contracts could encourage more European companies to invest in domestic infrastructure instead of depending on foreign cloud providers. Backers of the program have also said the project aims to encourage digital solutions that align with European laws, governance rules, and privacy standards.

Still, Europe’s strategy of distributing contracts across several companies may create another challenge. While diversification could reduce dependence on one dominant provider and improve resilience, it may also make it harder for Europe to build a single technology giant capable of competing globally with firms such as Microsoft, Amazon, or Google.

Some critics also view sovereign tech partly as an economic strategy meant to keep European spending within the region. However, Europe’s attempts to move away from U.S. technology have not always translated into direct support for startups. In several cases, governments have instead turned toward open-source software alternatives.

France has already started replacing parts of its Windows-based systems with Linux. Public institutions in Germany, Denmark, Austria, and Italy are also exploring alternatives to Microsoft’s office software products through platforms such as LibreOffice.

Several governments have also embraced a “build instead of buy” approach by creating internal software tools. That strategy has faced criticism from parts of the technology and financial sectors. France’s Court of Auditors reportedly questioned spending linked to Visio, an internally developed platform intended to act as an alternative to Zoom and Microsoft Teams.

French newspaper Les Echos also reported frustration from parts of the country’s technology sector. Some critics argued that if governments themselves do not consistently adopt domestic technology tools, it becomes difficult to convince large private companies to do the same.

Many giants of European businesses continue selecting American technology providers when they offer stronger technical or commercial advantages. German airline Lufthansa chose Starlink for onboard internet services. Air France also selected Starlink despite partial ownership ties to the French and Dutch governments. Reports have additionally suggested that France’s national railway operator SNCF may eventually adopt similar services.

The debate around European alternatives has become particularly visible in satellite communications. During a disagreement involving Poland, Elon Musk stated publicly that “there is no substitute for Starlink.” European governments are now trying to prove otherwise by investing in domestic telecommunications and space infrastructure projects.

Public sentiment has also started influencing the discussion. After President Trump threatened to take control of Greenland, applications encouraging consumers to boycott American products surged in popularity on Denmark’s App Store rankings. The reaction showed that calls to reduce dependence on U.S. companies are no longer limited to policymakers and regulators.

Pressure is also building on European governments to reconsider contracts involving controversial American firms. Palantir’s recent public messaging and political positioning have drawn criticism inside parts of the European Union and the United Kingdom. At the same time, many European officials and citizens have started distancing themselves from X, formerly Twitter, because of growing dissatisfaction around platform governance and political discourse.

American technology companies have also shown that Europe is not always their top commercial priority. When Meta delayed the European release of Threads because of regulatory concerns tied to EU laws, it reinforced the perception that large U.S. firms can afford to deprioritize the region when legal requirements become too restrictive.

At the same time, this environment is opening new opportunities for companies building products specifically designed for European markets, languages, and legal standards. Supporters of the EuroStack initiative are pushing for rules that would encourage or require public institutions to purchase locally developed technology whenever possible.

Backers of sovereign tech also hope European companies can eventually compete internationally rather than only within domestic markets. French artificial intelligence company Mistral AI has reportedly experienced strong revenue growth as some businesses search for alternatives to OpenAI. Meanwhile, the governments of Canada and Germany are supporting cooperation between Cohere and Aleph Alpha to create what supporters describe as a transatlantic AI platform for governments and businesses.

As geopolitical tensions continue reshaping the global technology industry, some companies are discovering that not being American, Chinese, or Russian is itself becoming a commercial advantage in international markets.

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