Пользователь перевел $129 млн на фишинговый адрес, но их просто вернули

cryptonews.ruPublished on 2023-07-20Last updated on 2024-11-20

Неназванный пользователь перевел $129 млн в USDT мошенникам, скопировав неправильный адрес из истории транзакций. В отличие от большинства подобных случаев, в течение 6 часов владельцы адреса-получателя сами вернули деньги.

🤯不知道哪位遭遇了首尾号相似钱包地址的钓鱼攻击，误转了 1.29 亿 USDT 给钓鱼地址：

THcTxQi3N8wQ13fwntF7a3M88BEi6q1bu8https://t.co/vhPxMpewc8

不过，钓鱼团伙不到 1 小时归还了其中 90%，然后又过了 4 个多小时把剩余的 10% 也归还了…

情报来自 InMist 成员。 https://t.co/cFhK3sM8Go
— Cos(余弦)😶‍🌫️ (@evilcos) November 20, 2024

По наблюдениям аналитиков Scam Sniffer со ссылкой на данные SlowMist, в историю транзакций пострадавшего попал подозрительный адрес, последние символы которого совпадают с одним из знакомых пользователю кошельков. Тот не проверил и отправил деньги на кошелек-самозванец.

По такому принципу работает схема «address poisoning». Мошенники создают адрес, похожий на какой-то из привычных потенциальной жертве. С него присылают незначительную транзакцию, которая сохраняется в истории пользователя.

Злоумышленники рассчитывают, что жертва не будет проверять полный идентификатор и скопирует подставной адрес при создании транзакции.

В мае в результате подобной атаки неназванный трейдер потерял $68 млн. В июле от «отравления адреса» пострадала хакерская группа Pink Drainer, тогда добыча составила 10 ETH.

Удивленные быстрым возвратом средств, некоторые пользователи X предположили, что мошенники испугались такой крупной добычи. Тот, кто пересылает $129 млн, вполне мог бы нанять команду аналитиков и хакеров для поиска виновных, потому может представлять угрозу.

Другие допустили, что на другом конце транзакции оказался не злоумышленник, а порядочный человек, который по собственной воле вернул средства владельцу.

Напомним, 10 ноября трейдер, потерявший около $26 млн из-за ошибки копирования, попросил сообщество помочь с возвратом средств и пообещал 10% в награду.

GitHub, Transfixed by AI

On the night of February 9th, GitHub suffered a major outage caused by a simple configuration change—reducing a cache refresh interval from 12 to 2 hours—that triggered a cascade of failures. This was not an isolated event, but part of a broader pattern. In early 2026, GitHub experienced at least 8 major incidents, failing to meet its promised 99.9% availability. These outages stemmed from structural issues: explosive growth in load, tight service coupling, and insufficient protection against abnormal traffic. This unprecedented load is driven by AI Agents. In 2025, GitHub handled ~1 billion commits. By 2026, weekly commits reached 275 million, projecting to ~14 billion for the year—a 14x increase. AI tools like Claude Code now contribute 4.5% of all public repository commits, with weekly submissions surging 25x in just three months. AI-generated pull requests jumped from 4 million to 17 million per month in half a year. Unlike human developers, AI Agents work continuously, generating commits at a scale that overwhelms infrastructure designed for human rhythms. The surge also shattered GitHub's business model. Copilot's flat-rate pricing, based on assisting human developers, became unsustainable as Agentic AI sessions consumed resources worth hundreds of dollars for a few dollars in fees. In response, GitHub imposed usage limits and, by June 1st, shifted to a pay-per-use "AI Credits" system. Facing this new reality, GitHub realized a 10x scaling plan was insufficient. It announced a need to *redesign* its architecture for 30x current scale—decoupling services, adding fault isolation, and improving change management to prevent cascading failures. Other platforms like Stripe and AWS are facing similar challenges with AI Agents. Fundamentally, GitHub is transitioning from a human collaboration platform to an "exhaust pipe" for automated AI workflows. Its detailed post-mortem reports aim to maintain trust during this turbulent rebuild. The February outage was not just a technical glitch, but a signal of the software industry's entry into a new, AI-driven era.

marsbit12m ago

marsbit12m ago

Visualizing Musk's Wealth: $3.6 Million Per Hour, Average Person Needs 11 Million Years of Work

Elon Musk is on the verge of becoming the world's first trillionaire, with a current estimated net worth of $970 billion. This wealth, largely tied to equity in SpaceX and Tesla, translates to him accumulating roughly $992 per second or $3.6 million per hour on average over his 31-year career. His fortune surpasses the annual GDP of over 125 countries and represents about 3% of U.S. GDP, double the relative wealth of historical figures like John D. Rockefeller. For perspective, an average U.S. household earning the median income would need to work over 11 million years to amass a comparable sum. Musk's wealth could buy approximately 2.4 million average American homes, all 32 NFL teams plus every NBA team, or over 10,000 private jets with operating costs. The analysis highlights the staggering scale and concentration of modern wealth, driven by successes in electric vehicles, space exploration, and artificial intelligence.

marsbit15m ago

Visualizing Musk's Wealth: $3.6 Million Per Hour, Average Person Needs 11 Million Years of Work

marsbit15m ago

Both Suffer Massive Losses Exceeding $90 Billion, Which Is in Greater Peril: Strategy or Bitmine?

Facing massive paper losses exceeding $90 billion each amidst a sharp market downturn, "Digital Asset Treasury" (DAT) giants Strategy and Bitmine find themselves in a precarious position, but with different underlying risks. Strategy, heavily invested in Bitcoin (BTC), faces significant financial strain. Its strategy relies heavily on debt, including convertible notes and preferred stock (STRC) requiring substantial dividend payments. With its cash reserves dwindling and BTC offering no staking yield for cash flow, Strategy's high leverage makes it vulnerable. A continued price decline could force asset sales to meet obligations, potentially creating a negative feedback loop. Its market value has already fallen sharply. In contrast, Bitmine, an Ethereum (ETH) holder, appears on firmer financial ground. It primarily funds its purchases through equity offerings (like ATM programs), avoiding debt pressure. It also generates income by staking a large portion of its ETH holdings. While not immune to market drops and shareholder dilution concerns, Bitmine maintains more flexibility, recently announcing a new preferred share offering to raise further capital. The core divergence lies in their financing: Bitmine uses equity (investor money), while Strategy uses debt (borrowed money). Consequently, Bitmine currently faces less immediate liquidity pressure than Strategy, which must navigate the dual challenge of servicing debt/dividends and a declining core asset (BTC) price.

marsbit19m ago

Both Suffer Massive Losses Exceeding $90 Billion, Which Is in Greater Peril: Strategy or Bitmine?

marsbit19m ago

Both Suffer Losses Exceeding $9 Billion, Which Is More at Risk: Strategy or Bitmine?

As Bitcoin and Ethereum prices drop significantly, two major corporate treasury holders, Strategy and Bitmine, are facing massive unrealized losses—approximately $100 billion and $90 billion, respectively. The article compares their financial health and underlying risks. Bitmine, holding over 5.4 million ETH, relies primarily on equity financing (like ATM offerings) and has substantial cash reserves, alongside staking income from its ETH holdings. It plans a $300 million perpetual preferred stock offering. In contrast, Strategy, holding Bitcoin, is burdened by significant convertible debt and preferred stock dividends, with cash reserves covering only about six months of its annual dividend obligations. Strategy's leverage is higher, and it faces potential liquidity pressure if BTC prices fall further, possibly forcing asset sales. While both companies are under pressure, Bitmine’s financial structure appears more resilient, whereas Strategy’s high leverage makes it more vulnerable to continued market declines.

Odaily星球日报23m ago

Both Suffer Losses Exceeding $9 Billion, Which Is More at Risk: Strategy or Bitmine?

Odaily星球日报23m ago

Where the AI Bubble Really Is: Which Layer of Players Are Naked

AI Bubble: Where It Really Is and Who's Swimming Naked This analysis dissects the AI industry not as a single entity but as a five-layer pyramid, arguing that bubbles are concentrated in specific tiers, not uniformly distributed. **Key Distinction from the 2000 Dot-com Bubble:** Unlike 2000, where companies had stock prices before revenue, today's leading AI players have massive, contract-backed revenue driving their valuations. Core infrastructure demand is real, with every GPU running at full capacity for paying customers. **The Five-Layer Pyramid & Bubble Assessment:** * **L0 (Fab/Manufacturing) & Top L4 (Leading AI Apps): NO BUBBLE.** Companies like TSMC, NVIDIA, major cloud providers (Microsoft, Google, Meta, Amazon), and top AI labs have real revenues and orders. Supply is tightly constrained by TSMC's disciplined capacity control and physical limits like power/land for data centers, preventing a supply glut. * **L1 (Memory): BATTLEGROUND.** Sky-high HBM margins could signal a new structural cycle or a classic "boom before bust." The oligopoly of three major players may enforce supply discipline, making this a high-stakes bet. * **L2 (Interconnect/Optical Modules): BUBBLE TERRITORY.** Companies like Lumentum and AAOI have seen stock surges (4-10x) far outpacing revenue growth. This hardware segment has lower physical barriers to expansion than fabs, allowing speculation. It mirrors the 2000 bubble's epicenter—optics. * **L3 (Infrastructure/"GPU Landlords"): VULNERABLE.** GPU leasing companies profit from the current compute shortage but own no long-term moat. Their business model relies on a temporary bottleneck that will ease as big tech expands and new tech (e.g., potential space-based data centers) emerges. * **L4 Long Tail (VC-backed Startups): STRONG BUBBLE SIGNALS.** VC funding concentration in AI is twice that of the 1999 peak. Many startups with little revenue use the valuation logic of successful giants to justify their own, creating high risk of a "valuation crunch" when funding dries up. **Critical Risks to Monitor:** 1. **GPU Depreciation & Accounting:** Companies extending the assumed useful life of GPUs artificially boost profits. The true economic life depends on future generational leaps from NVIDIA. 2. **"GPU Credit" & Off-Balance-Sheet Leverage:** Emerging structures where shell companies borrow to buy GPUs and lease them out (with chipmakers sometimes investing) move debt off major balance sheets. This echoes the "vendor financing" of 2000 and the securitization risks of 2008, though currently small-scale. 3. **TSMC Abandoning Caution:** If the primary supply bottleneck (TSMC's conservative capacity planning) breaks, runaway supply could trigger a bust. 4. **Algorithmic Efficiency Breakthrough:** A major leap in software efficiency could drastically reduce the need for raw compute hardware, undermining the investment thesis. **Conclusion:** The AI boom is expensive and has frothy areas, but its core is underpinned by real demand and physical supply constraints. The bubble risk is layered: most present in optical components, GPU leasing, and the long-tail startup ecosystem, while the foundational chip manufacturing and leading application layers remain relatively solid—for now.

marsbit31m ago

Where the AI Bubble Really Is: Which Layer of Players Are Naked

marsbit31m ago

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