Trust Wallet Users Lost $7 Million in Cryptocurrency Due to Hack

RBK-crypto2025-12-26 tarihinde yayınlandı2025-12-26 tarihinde güncellendi

Özet

On December 26, the team behind Trust Wallet, a popular cryptocurrency wallet owned by Binance, reported a security breach affecting the browser extension version 2.68. According to Binance founder Changpeng Zhao, the incident was the result of a hack, resulting in losses of approximately $7 million. Users of the compromised version were advised to disable or uninstall it and upgrade to the secure version 2.69. The breach did not impact the mobile application. Trust Wallet's native token (TWT) initially dropped around 7% in price but recovered after the official announcement. Zhao stated that Trust Wallet would cover all user losses and urged affected individuals to contact support. Although no official cause was confirmed, reports suggest that version 2.68 contained hidden malicious code that intercepted users' secret recovery phrases during wallet imports, sending them to an external server. This allowed attackers to gain full access to affected wallets. Some community members, including Zhao, suspect the breach may have been an inside job involving a team member.

On the night of December 26, the team of the popular cryptocurrency wallet Trust Wallet reported a security breach that affected the browser application version 2.68. As explained by Binance founder and head of YZi Labs, which owns Trust Wallet, Changpeng Zhao, the incident occurred as a result of a hacker attack, and the damage amounted to $7 million.

Users of version 2.68 should disable or delete this build and only then install the new version 2.69, and under no circumstances should they open or use the unsafe version. The hack only concerns the browser version 2.68 and did not affect the mobile application.

The native token of Trust Wallet (TWT) reacted with a price drop of about 7%, falling for three hours before the team's announcement around 01:20 Moscow time on December 26. After the official announcement, the TWT price recovered to previous levels and the asset is trading slightly below $0.83.

Zhao stated that Trust Wallet will cover all user losses, and affected users were asked to write to the wallet's support service, a link to which can be found on the official website.

No official statements have been made regarding the causes of the hack. But its essence, according to a report by user Akinator on social network X, may be that a hidden malicious code was embedded in version 2.68 of the Trust Wallet browser extension. Akinator was one of the few who reported the hack several hours before the official confirmation from Trust Wallet.

The assumption is that when a user enters their secret phrase to import a wallet, this code stealthily intercepts the data and sends it to an external server. In this way, the attackers could gain full access to the users' wallets and funds.

The crypto community believes that the malicious code was embedded by someone from the cryptocurrency wallet team. In response to a suggestion by X user under the nickname Crazino.eth that the hack was "certainly carried out by an insider working in the team," Zhao replied "probably."

Broke the cycle. How the price of Bitcoin changed over 10 years at Christmas

AI outperformed humans in a crypto trading tournament. What were the results

Miner "capitulation" called a bullish factor for Bitcoin. Why

İlgili Sorular

QWhat was the total amount of cryptocurrency lost by Trust Wallet due to the hack?

AUsers lost $7 million in cryptocurrency due to the hack.

QWhich specific version of the Trust Wallet application was compromised in the security breach?

AThe security breach affected the browser application version 2.68.

QWhat was the impact on Trust Wallet's native token (TWT) price following the incident?

AThe native token TWT initially dropped by approximately 7% but recovered to its previous levels after the official announcement, trading slightly below $0.83.

QAccording to the article, how did the alleged malware in version 2.68 potentially steal user funds?

AThe hidden malicious code allegedly intercepted a user's secret recovery phrase during wallet import and sent it to an external server, giving attackers full access to the wallets and funds.

QWho did the crypto community and Binance's Changpeng Zhao suggest might be responsible for the hack?

AThe crypto community and Changpeng Zhao suggested that the hack was likely carried out by an insider within the Trust Wallet team.

İlgili Okumalar

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.

marsbit1 saat önce

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

marsbit1 saat önce

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.

marsbit1 saat önce

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

marsbit1 saat önce

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.

marsbit1 saat önce

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

marsbit1 saat önce

İşlemler

Spot
活动图片