US Moves to Seize $3.44M USDT Tied to Crypto Investment Scam

TheNewsCryptoPublicado em 2026-03-11Última atualização em 2026-03-11

Resumo

U.S. federal prosecutors have filed a civil forfeiture action to seize approximately $3.44 million in USDT linked to an online cryptocurrency investment scam. The scheme targeted victims across multiple states by establishing trust through mistaken text or encrypted app messages before promoting a fake Ethereum investment opportunity backed by physical gold. Victims were instructed to send ETH to scam-controlled wallets, where funds were converted to USDT and moved through intermediary addresses. The investigation began in late 2024 after reports from four victims. Authorities seized the USDT in early 2025 and are seeking permanent forfeiture. This case is part of broader efforts to recover crypto assets, including recent actions against romance scams.

The federal prosecutors of the United States have filed a civil forfeiture action to recoup around 3.44 million USDt associated with a claimed online crypto investment scam that targeted victims across various states.

As per the March 10 announcement from the US Attorney’s Office in Boston, the funds were associated with a scheme that convinced victims to send cryptocurrency to wallets managed by scammers.

Officials mentioned that they captured the USDt in February and March 2025 and are now asking a court to permit the permanent forfeiture of the assets. The prosecutors mentioned that in a fraud scheme like this, scammers get funds from victims using manipulative tactics.

It also added that they set up a level of trust with a victim and then lure the victim into investing in a fraudulent investment scheme. The investigation started in late 2024 after around four people reported losses, comprising two residents of Massachusetts and others in Utah and South Carolina.

Performing Scam after Gaining Trust

In this situation, the scammers first had a word with victims via messages that appeared to be sent by mistake, mostly via text messages or encrypted apps like WhatsApp and Telegram.

After making trust, the individuals allegedly pushed what they referred to as an exclusive Ethereum investment opportunity supported by physical gold. Victims are asked to buy Ether (ETH) and send it to wallets given by the perpetrators.

As per the release, court documents state that once the ETH reached those wallets, the funds were directed via intermediary addresses, changed intoUSDt, and shifted to unhosted wallets handled by the scammers.

The officials from the US have lately captured more crypto associated with fraud schemes. In one case, the US Attorney’s Office for Massachusetts filed a civil forfeiture action looking to recover around $327,829 in USDt, which the investigator mentioned was associated with a romance scam targeting a Massachusetts resident in 2024.

Highlighted Crypto News Today:

Cardano (ADA) Struggles for Stability: Is a Breakdown or Bounce Ahead?

TagsScamUSAUSDT

Perguntas relacionadas

QWhat is the total value of USDT that U.S. federal prosecutors are seeking to seize in this case?

A$3.44 million USDT.

QHow did the scammers initially contact their victims according to the announcement?

AVia messages that appeared to be sent by mistake, mostly through text messages or encrypted apps like WhatsApp and Telegram.

QWhat type of fraudulent investment did the scammers promote to their victims?

AAn exclusive Ethereum investment opportunity that was backed by physical gold.

QWhat happened to the victims' Ether (ETH) after they sent it to the provided wallets?

AThe funds were directed through intermediary addresses, converted into USDT, and then transferred to unhosted wallets controlled by the scammers.

QWhen did the investigation into this scam begin and what prompted it?

AThe investigation began in late 2024 after approximately four people, including two residents of Massachusetts and others in Utah and South Carolina, reported losses.

Leituras Relacionadas

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

Ethereum Q1 2026 Report: Fees Down, Users & Transactions Hit New Highs Token Terminal's Q1 2026 report on Ethereum presents a pivotal development: the network achieved record highs in monthly active users (13.2M, +85.9% YoY), total transactions (200.4M, +81.5% YoY), and throughput (25.78 TPS), while transaction fees on the mainnet plummeted by 47.9% quarter-over-quarter. This shift is attributed to the network's strategic move into a "low fees for scale" phase, exemplified by the Fusaka upgrade which increased data capacity and lowered block space costs, releasing pent-up demand (a manifestation of Jevons's Paradox). The report highlights a core narrative shift for Ethereum: from a DeFi-centric blockchain to a global financial settlement layer. It maintains a dominant position in tokenized assets, holding majority market shares among top chains in stablecoins (61.8%), tokenized funds (73.0%), and tokenized commodities (84.0%). Growth in tokenized funds (+73.1% YoY) and commodities (+325.9% YoY) was particularly strong, driven by institutions like BlackRock and JPMorgan entering the space. Contrasting these usage gains, several USD-denominated value metrics declined in Q1: fully diluted market cap fell 30.3% QoQ, total value locked (TVL) dropped 11.0%, and ecosystem transaction volume decreased 24.0%. The report interprets this as Ethereum prioritizing long-term network expansion and cementing its role as the default settlement layer for finance over short-term fee capture. The commentary from Etherealize argues that, much like the early internet, Ethereum's open, permissionless model is poised to win over closed alternatives as institutional tokenization accelerates.

marsbitHá 1h

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

marsbitHá 1h

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbitHá 1h

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbitHá 1h

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbitHá 3h

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbitHá 3h

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

Beyond the familiar performance charts like MMLU-Pro and MMMU, which major AI models strive to ace, stands a key "examiner": Chinese-Canadian researcher Wenhu Chen. An assistant professor at the University of Waterloo and founder of TIGERLab, Chen addresses the crucial need for more rigorous AI evaluation. As models like GPT-4 began scoring near-perfect results on older benchmarks like MMLU, it became difficult to distinguish their true capabilities. In response, Chen introduced MMLU-Pro in 2024, featuring harder, more reasoning-focused questions with more answer choices, successfully reintroducing meaningful performance gaps. His work extends to multi-modal evaluation with MMMU and its enhanced version, MMMU-Pro. These benchmarks test a model's ability to understand and reason with complex information from images, charts, and text across diverse academic subjects, exposing the significant challenges even top models face in genuine comprehension. Chen's background in complex QA, table reasoning, and his experience at Google DeepMind on projects like Gemini inform his approach. He understands that effective benchmarks must anticipate how models might "cheat" by memorizing data or avoiding visual analysis. His lab also actively researches video understanding and generation models (e.g., UniVideo, Vamba), ensuring his evaluation work is grounded in practical model-building challenges. Now at Meta's Super Intelligence Lab, Chen continues his focus on multi-modal data and evaluation, representing the deep yet often unseen contributions of Chinese talent in shaping the fundamental tools of the AI industry.

marsbitHá 3h

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

marsbitHá 3h

Trading

Spot
Futuros
活动图片