The FBI’s New York Field Office has warned that fraudulent tokens impersonating the agency are being airdropped to Tron wallets, with recipients threatened with ‘total block’ of assets unless they submit personal information via phishing sites.
At least 728 wallets were affected, some holding over US$1 million in USDT, when the warning was issued on 19 March.
The scam warns users that their wallets are ‘under investigation’ and instructs them to complete an online anti-money-laundering form. The FBI urged crypto holders to ignore these messages and avoid entering any personal data on linked websites.
Attackers exploit Tron for its fast and low-cost transactions, using bots to distribute tokens widely and generate spoofed addresses.
Impersonation scams have surged dramatically in 2025, with Chainalysis reporting a 1,400% year-over-year increase. Total crypto fraud losses are estimated at US$17 billion, with AI-assisted scams proving far more profitable than traditional schemes.
The FBI previously ran a blockchain sting using Ethereum tokens, resulting in indictments and the seizure of millions in assets.
The bureau encourages anyone who receives the fake FBI tokens to report the incident to the Internet Crime Complaint Centre to help combat ongoing crypto fraud.
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Meta recently confirmed that an AI agent inadvertently exposed sensitive company and user data to some employees. The leak happened when an engineer followed the AI agent’s forum suggestion, exposing data for about two hours.
Meta stated that no user data was mishandled and emphasised that human errors could cause similar issues.
The incident reflects broader challenges in deploying agentic AI tools within major tech companies. Amazon faced similar issues, with internal AI tools causing outages and operational errors, showing risks of quickly integrating AI into critical workflows.
Experts describe these deployments as experimental, with companies testing AI at scale without fully assessing potential risks.
Security specialists note that AI agents lack the contextual awareness that human engineers accumulate over years of experience. Lacking long-term operational knowledge, AI can make decisions that compromise security, a factor in the Meta breach.
Analysts warn that such errors are likely to recur as AI adoption accelerates.
The episode comes amid growing attention on agentic AI’s potential to disrupt workflows, affect productivity, and introduce new vulnerabilities. Industry observers caution that AI tools must be carefully monitored and accompanied by robust safeguards to prevent future incidents.
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Mastercard has introduced a generative AI foundation model trained on billions of anonymised transactions. The model is designed as a backend system to power insights across payments and commerce services.
The company plans to extend AI use beyond fraud detection into cybersecurity, loyalty programmes and small-business tools. The model is being developed with support from Nvidia and Databricks technologies.
Earlier AI tools focused on fraud detection, significantly improving accuracy and reducing false positives. The new model marks a shift towards a broader infrastructure approach across multiple products.
This move aligns with Mastercard’s growing reliance on value-added services, which generated over $13 billion in revenue. These services include security, analytics and digital payment solutions beyond the core network.
Competitors such as Visa and PayPal are also expanding AI-driven commerce platforms. The race is intensifying as firms build integrated systems for payments, automation and intelligent services.
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A newly identified cyberattack known as ‘DarkSword’ is raising concerns about the security of iPhone devices, following reports that millions of users could be exposed to rapid data extraction techniques.
Cybersecurity researchers indicate that the attack targets specific iOS versions, exploiting vulnerabilities in the Safari browser and a graphics processing feature known as WebGPU.
Once access is gained, attackers can retrieve sensitive information, including messages, emails and location data, within minutes, while removing traces of the intrusion almost immediately.
Estimates suggest that a significant share of global iPhone users may be affected, with hundreds of millions of devices running vulnerable software versions.
The scale of exposure remains uncertain, particularly as experts continue to assess whether additional versions of iOS may also be impacted.
Researchers have associated the campaign with a threat actor previously identified by Google, with observed activity across multiple regions.
Such a development highlights growing concerns about the evolution of mobile cyber threats, where increasingly sophisticated techniques are being deployed beyond traditional state-level operations.
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BTQ Technologies has deployed Bitcoin Improvement Proposal BIP-360 on its Bitcoin Quantum Testnet v0.3.0, marking the first live test of the proposal. The upgrade introduces a quantum-resistant transaction model, Pay-to-Merkle-Root, designed to strengthen Bitcoin’s long-term security.
BIP-360 focuses on mitigating a vulnerability linked to Taproot’s key-path spending mechanism, which can expose public keys on-chain. Such exposure may become a risk if future quantum computers are capable of exploiting cryptographic weaknesses using advanced algorithms.
The testnet adds new consensus rules, post-quantum signatures, and full transaction lifecycle testing. Faster one-minute block times and adjusted fee structures have been introduced to accommodate larger and more complex signatures.
Growing global attention on quantum threats adds urgency to the development. US, EU, and Canadian authorities are setting timelines for post-quantum cryptography to protect future system security.
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Broadcom is facing increased regulatory pressure in the EU following a formal antitrust complaint concerning changes to VMware licensing practices.
The complaint highlights growing tensions between large technology providers and European cloud infrastructure firms.
The filing, submitted by Cloud Infrastructure Services Providers in Europe, raises concerns that revised licensing models could significantly alter market dynamics.
European providers argue that the changes may limit flexibility, increase costs, and affect their ability to compete effectively in the cloud services sector.
At the centre of the dispute lies the broader issue of market concentration and control over critical digital infrastructure.
Industry stakeholders suggest that restrictive licensing conditions could reshape access to essential virtualisation technologies, which underpin a wide range of cloud and enterprise services across the EU.
Regulatory attention is expected to focus on whether such practices align with the EU competition rules, particularly regarding fair access and market neutrality.
The case emerges at a time when European policymakers are intensifying oversight of dominant technology firms and seeking to strengthen digital sovereignty across strategic sectors.
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Global efforts to regulate AI are accelerating, but innovation continues to outpace formal rules. Policymakers and industry leaders are increasingly turning to standards to help bridge compliance gaps.
At the AI Standards Hub Global Summit, experts highlighted how technical standards support responsible AI development. These tools are seen as essential for scaling AI safely while regulatory frameworks continue to evolve.
Differences across regions remain significant, with the EU relying on formal regulation and the US leaning on flexible standards. This fragmented landscape is raising concerns over compliance costs and barriers to cross-border deployment.
Experts stress that standards must evolve alongside AI while aligning with global frameworks and enforcement efforts. Without coordination, inconsistencies could limit innovation and weaken trust in AI systems.
Calls are growing for shared definitions, measurable benchmarks and stronger international cooperation. Stakeholders argue that aligning standards with regulation will be critical for future AI governance.
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Armenia’s recent technology announcements are helping to form a clearer national AI strategy with stronger coordination. A memorandum with the US on semiconductors and AI now appears to be moving beyond symbolic commitment into action.
Momentum has accelerated with plans to expand a large-scale AI factory backed by significant investment. The project is estimated at around $4 billion and includes tens of thousands of advanced GPUs to support large-scale development.
The initiative is already entering construction, marking a shift from concept to execution in a short timeframe. Officials have described a broader vision of building a network of AI factories across the country.
Energy planning is becoming central, with discussions around deploying a small modular nuclear reactor to meet demand. Stable and scalable power is considered essential for sustaining long-term AI infrastructure growth.
Efforts are also targeting the wider ecosystem through a Virtual AI Institute and planned GPU access for startups. These steps aim to strengthen research capacity and ensure local participation in the country’s AI expansion.
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Britain is opening access to its national AI Research Resource to support domestic AI development. Startups and spinouts can now use supercomputers previously reserved for frontier research.
The AIRR combines infrastructure from government, universities and leading technology firms. It provides the computing power needed to train models and run complex simulations.
Access will be worth around £20 million per year for participating companies. Officials say reducing compute barriers will help startups move faster from prototype to product.
The government’s Sovereign AI Unit, backed by up to £500 million, will also support long-term growth. The programme targets areas including advanced models, scientific discovery and trustworthy AI systems.
Concerns remain over regulatory alignment with the EU’s stricter AI rules. Tensions could shape whether the UK maintains a more flexible environment for innovation.
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Researchers at MIT have developed a new approach that combines generative AI with wireless signals to detect objects hidden behind obstacles. The system uses Wi-Fi-like millimetre wave signals to build partial reconstructions and then completes missing details with AI.
Traditional methods struggled with limited visibility due to how signals reflect off surfaces, often leaving large portions of objects undetected. The new technique, Wave-Former, uses generative AI to fill missing data, improving reconstruction accuracy by nearly 20%.
An extended system, called RISE, takes the concept further by mapping entire indoor environments. By analysing reflected signals from human movement, the system reconstructs room layouts using a single stationary radar, removing the need for mobile sensors.
Applications range from warehouse automation to smart home robotics, where understanding hidden objects and human positions is critical. Unlike camera-based systems, the technology also preserves privacy, marking a significant step forward in wireless sensing capabilities.
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