DOJ, Europol Freeze $3.5M In Crypto After Dismantling Global Proxy Fraud Network

bitcoinistPublicado em 2026-03-14Última atualização em 2026-03-14

Resumo

US and European authorities dismantled SocksEscort, a global proxy service that used malware (AVrecon) to hijack over 369,000 devices in 163 countries, allowing criminals to hide their locations. The service, operating for years, generated at least $5.7 million from users who paid in cryptocurrency for anonymity. A coordinated law enforcement effort across multiple countries resulted in the seizure of 34 domains, takedown of servers, and freezing of $3.5 million in crypto. The network was linked to various crimes, including a $1 million cryptocurrency theft from a New York resident, bank fraud, and account takeovers.

A New York resident lost close to $1 million in cryptocurrency. That single case became one of the clearest examples of the damage done by SocksEscort — a for-hire proxy service that gave criminals across the globe a way to hide while they stole.

A Network Built On Hijacked Devices

US and European authorities announced Thursday they had shut down SocksEscort after years of operation. The service worked by infecting routers and other internet-connected devices with malware, turning them into cover points that masked the real locations of cybercriminals.

According to the Department of Justice, the network had quietly burrowed into at least 369,000 devices spread across 163 countries. Criminals could then route their attacks through those compromised machines, making them far harder to trace.

The malware at the heart of the operation — known as AVrecon — had been publicly identified by cybersecurity firm Black Lotus Labs as far back as July 2023. The network kept running anyway.

Source: DOJ

The takedown was not a single agency effort. Law enforcement from Austria, France, Germany, Hungary, the Netherlands, Romania, and the US worked the case together.

On the American side, the FBI’s Sacramento Field Office, the IRS Criminal Investigation Oakland Field Office, and the Department of Defense’s Defense Criminal Investigative Service all had a hand in it.

Europol and Eurojust provided cross-border coordination support. Black Lotus Labs and the nonprofit Shadowserver Foundation supplied technical intelligence that helped investigators connect the dots.

Bitcoin is now trading at $70,541. Chart: TradingView

Criminals Paid In Crypto To Stay Anonymous

SocksEscort did not just attract individual bad actors. It ran like a business. Customers paid to access the service, and they did so anonymously — using cryptocurrency to avoid leaving a financial trail.

Based on reports from Europol, the platform pulled in at least 5 million euros, roughly $5.7 million, from its paying users over the course of its run.

Authorities were ultimately able to seize 34 domains, take down about two dozen servers operating across seven countries, and freeze approximately $3.5 million in crypto tied to the operation.

Europol Executive Director Catherine De Bolle said proxy services of this kind give criminals the cover to carry out attacks, move illegal content, and dodge detection. She credited the international cooperation for exposing the infrastructure behind it.

Fraud Stretched From Bank Accounts To Crypto Wallets

The crimes enabled by SocksEscort went beyond any single method. Officials linked the network to bank fraud and cryptocurrency account takeovers dating back to 2020.

The New York victim’s case stood out for its scale, but reports indicate the damage was spread across multiple countries and target types.

Featured image from Pexels, chart from TradingView

Perguntas relacionadas

QWhat was the name of the proxy service dismantled by US and European authorities?

ASocksEscort

QHow many devices were infected by the malware used in the SocksEscort operation according to the Department of Justice?

AAt least 369,000 devices

QWhat was the name of the malware at the heart of the SocksEscort operation?

AAVrecon

QHow much cryptocurrency was frozen by authorities in connection with the SocksEscort network?

AApproximately $3.5 million

QWhich cybersecurity firm had publicly identified the AVrecon malware as far back as July 2023?

ABlack Lotus Labs

Leituras Relacionadas

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbitHá 33m

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbitHá 33m

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbitHá 38m

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbitHá 38m

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbitHá 38m

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbitHá 38m

Trading

Spot
活动图片