Tether Freezes $4.2B USDT in Crime Crackdown

TheNewsCryptoPublicado a 2026-02-28Actualizado a 2026-02-28

Resumen

Tether, the issuer of the USDT stablecoin, has frozen approximately $4.2 billion in assets linked to criminal activities, with $3.5 billion frozen since 2023. The company, which has over $180 billion in circulation, works with global law enforcement to freeze tokens in response to official requests. Recent actions include blocking $61 million tied to "pig-butchering" scams, as well as funds connected to human trafficking, terrorism, and warfare. This crackdown reflects growing regulatory concerns over illicit crypto flows, which saw $82 billion in laundered funds last year. While Tether's control enables swift action against crime, it also highlights tensions around decentralization. Stablecoins like USDT are facing increased regulatory scrutiny as governments push for stronger anti-money laundering standards.

Tether has frozen approximately $4.2 billion worth of its USDT stablecoin over links to illicit activity, the company confirmed. The El Salvador-based issuer said it carried out most of these freezes in the past three years as global enforcement efforts intensified.

The stablecoin giant, which now has more than $180 billion USDT in circulation, retains the ability to remotely freeze tokens held in crypto wallets when law enforcement agencies request action.

Coordinated Law Enforcement Efforts

This week, Tether confirmed that it assisted the U.S. Justice Department in freezing nearly $61 million in USDT tied to “pig-butchering” scams. These schemes involve fraudsters building personal relationships with victims before persuading them to invest in fake crypto opportunities.

The latest freeze brings Tether’s cumulative enforcement total to $4.2 billion. According to company statements, roughly $3.5 billion of that amount has been frozen since 2023.

Tether has also blocked wallets connected to human trafficking networks and individuals linked to terrorism and warfare in Israel and Ukraine. Russian crypto exchange Garantex reported last year that Tether froze funds held on its platform.

The company sees itself as an active participant in the fight against crime. Tether argues that they work in collaboration with authorities around the world in monitoring and addressing suspicious transactions.

Rising Concerns Over Illicit Crypto Flows

Regulators around the world are becoming increasingly wary about the involvement of cryptocurrency in financial crime. The Financial Action Task Force (FATF) urged countries last year to step up their enforcement in crypto markets, which tend to be less regulated than traditional financial systems.

Blockchain researchers reported that money launderers received at least $82 billion in cryptocurrency last year. That figure represents a sharp increase from $10 billion in 2020. A portion of this is because of organized fraud groups, especially among Chinese-speaking individuals.

A key component of cryptocurrency markets is stablecoins. Traders frequently use USDT for exchange liquidity, cross-border transactions, and decentralized finances. As volumes rise, so do the efforts of those who monitor them.

Stablecoins Under Regulatory Spotlight

Tether’s ability to freeze tokens underscores an inherent tension in crypto markets. While blockchain technology peer-to-peer transactions, issuers like Tether maintain control mechanisms over their tokens.

Tether’s enforcement capabilities allow governments to take swift action against criminal organizations. However, critics say that Tether’s control undermines the concept of decentralization.

The rapid growth of stablecoins also increases regulatory focus. Tether’s circulation has expanded from about $70 billion three years ago to more than $180 billion today.

As global regulators push for stronger anti-money laundering standards, stablecoin issuers may face even tighter compliance requirements. The recent actions by Tether indicate that the intentions of the major players in the market are to show cooperation, not resistance.

The crackdown also points to a larger shift in the way the government is dealing with the market. Crypto is no longer viewed as a fringe market, but the government is increasing the pressure on the intermediaries in the digital asset intermediaries to comply with traditional financial crimes regulations.

Highlighted Crypto News:

Senate Democrats Urge Federal Review of Binance Compliance Controls

TagsCrypto RegulationscryptocrimestablecoinsTetherUSDT

Preguntas relacionadas

QHow much USDT has Tether frozen in total due to links to illicit activity?

ATether has frozen approximately $4.2 billion worth of USDT in total.

QWhat was the specific reason for Tether's recent freeze of $61 million in USDT at the request of the U.S. Justice Department?

AThe $61 million in USDT was frozen because it was tied to 'pig-butchering' scams, where fraudsters build personal relationships with victims before persuading them to invest in fake crypto opportunities.

QWhat is one of the main criticisms of Tether's ability to freeze tokens?

ACritics argue that Tether's control and ability to freeze tokens undermines the core concept of decentralization in cryptocurrency.

QAccording to the article, what has been a key factor in the rising volume of illicit cryptocurrency flows?

AA key factor is organized fraud groups, especially among Chinese-speaking individuals, contributing to a sharp increase from $10 billion in 2020 to at least $82 billion last year.

QWhat does the article suggest about the changing government view of the cryptocurrency market?

AThe article suggests that crypto is no longer viewed as a fringe market, and the government is increasing pressure on digital asset intermediaries to comply with traditional financial crime regulations.

Lecturas 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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

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.

marsbitHace 3 hora(s)

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

marsbitHace 3 hora(s)

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.

marsbitHace 3 hora(s)

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

marsbitHace 3 hora(s)

Trading

Spot
Futuros
活动图片